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With the increasing digitization of health data, many developing countries are now seeking ways to understand and obtain actionable insights from them. The “Big Data for Health Conference and Workshops for the Asia-Pacific� aims to bring together policy-makers, innovators, academics and business to discuss strategies and approaches to making health data work for improving health systems. The University of the Philippines, Massachusetts Institute of Technology, Stanford University, and the Asia eHealth Information Network join forces to bring to Cebu a renowned team with expertise ranging from artificial intelligence, machine learning, analytics and technopreneurship to share the state-of-the-art in health data analytics.


Table of Contents

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Message from Chancellor Carmencita Padilla Message from Dr. Boonchai Kijsanayotin Message from Dr. Jaime Montoya Acknowledgments Welcome Ceremonies - Chancellor Liza Corro Keynote Speech - Commissioner Lilian de las Llagas

Plenary Sessions 10   The Role of Governance, Architecture, Program Management,    Standards and Interoperability (AeHIN’s Mind the GAPS)    in Health Data Analytics 12   Resource Sharing in Medical Education towards Improving    Health Outcomes, Medical Mobile Clinic and Telemedicine 13   Sharing Critical Care Data for Research and Education 14   The Rise of Health Hackathons and Datathons 15   Machine Learning in Healthcare 16   Physiological Signal Processing 17   Artificial Intelligence in Medicine 18   Big Data for Public Health 19   Protecting Big Data: Complying with the 2012 Data Privacy Act    of the Philippines 21   Protecting Big Data: United Kingdom Experience 22   Building an Ecosystem around Health Data Analytics 24   Nurturing Innovation & Entrepreneurship in Academia Parallel Workshops 26   Physiological Signal Processing 26   Integrating Technology in Medical Education 27   Big Data for Public Health And eHealth 27   Biomedical Text Mining and Natural Language Processing 28   Edge Clinical - Preparing a Platform for Big Data in Health Research 28   Innovation and Entrepreneurship 29   Machine Learning Techniques for Big Data and Extracting   Actionable Knowledge 29   Datathons to Support Cross-Disciplinary Education and Research 31  33  

Question and Answer Annexes


Message from Chancellor Carmencita Padilla As a pediatric geneticist, I have personally seen the rapid accumulation of health data from many sources. Whereas lack of data had been a common complaint a decade ago, its inverse, too much data, is now the norm. The health sector is now inundated with unprecedented volumes of mostly unstructured data and with this massive opportunity comes uncertainty. Now more than ever, collaborations and partnerships between health and information technology professionals must happen to help us understand what this sea of data means to us. Ethicists should provide advice and guidance for the safe processing of personal information and remain the protector of our patients. Almost all aspects of patient care and research will be affected by the big data phenomenon. I extend my congratulations to the Asia eHealth Information Network, the UP Manila faculty and most especially to Chancellor Liza Corro and Commissioner Lilian Delas Llagas for providing the much-needed support to the conference. UP Manila will continue to nurture these linkages and craft a roadmap for stakeholder engagement towards the ethical use of data for better healthcare.

Message from Dr. Boonchai Kijsanayotin I congratulate the organizers of the Big Data for Health Conference for taking up the challenge of gathering many stakeholders on board for this auspicious event. While my first encounter with MIT Critical Data is in Thailand, I have come to realize the idea should be more widely disseminated to as largest audience as possible. The Asia eHealth Information Network is composed of more than one thousand members from twenty-five countries. It is a natural platform for knowledge exchange and resource sharing. We know that we are just catching up with all of these eHealth concepts but if we work together, we will get there. I look forward to more events like this and encourage the eHealth community to collaborate and establish partnerships. As an African proverb says: “If you want to go fast, go alone. If you want to go far, go together.

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Message from Dr. Jaime Montoya The Department of Science and Technology recognizes the important role of conference and meetings in catalyzing new knowledge and partnerships. With this in mind, we have wholly supported the Big Data for Health Conference and we look forward to the collaborations and projects that will emanate from it. The Philippine Council for Health Research and Development hopes that with a transparent and multi-stakeholder approach, our country can fully maximize the large amounts of health data that are being collected nationwide through various means. With this big data, more innovative approaches to health care and public health may arise. It is with excitement that we wait for these new ideas.

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Acknowledgments When Dr. Boonchai Kijsanayotin was invited by Dr. Leo Celi to participate in a big data event in Thailand, the AeHIN chair reported back that there is tremendous value in having the MIT Critical Data team interact with the members of the Asia eHealth Information Network. Right away, I contacted Leo (who was my contemporary in medical school) and we quickly agreed on location, dates, and theme. The MIT Critical Data Team has had years of experience and were perfect to bootstrap our burgeoning big data community. Armed with the conference coordinates, I requested for a meeting with UP Cebu Chancellor (preferred venue for the event) and she gladly agreed to co-host. Of course, UP Manila Chancellor Carmencita Padilla likewise gave her blessings, and soon Dr. Jette Gabiola of Stanford was also on board. By the end of the conference, a community around big data was formed and ideas for the next one was hatched. We thank Dr. Jaime Montoya and Ms. Merlita Opena from the Philippine Council for Health Research Development for the publication of the proceedings and for supporting the conference. Prof. Aileen Joan Vicente was an indispensable partner along with the UP Cebu faculty who generously lent their expertise and time for the event. I will be remiss if I do not mention the support provided by Dr. Iris Isip-Tan from the Medical Informatics Unit and Dr. Raymond Sarmiento from the National Telehealth Center. Dr. Alvin Marcelo Executive Director Asia eHealth Information Network

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Welcome Ceremonies Chancellor Liza Corro The University of the Philippines is happy to welcome you all to this UP-MIT-Stanford-AeHIN Big Data for Health Conference and Workshops for Asia-Pacific. I would like to specially greet my dear friend and mentor, our keynote speaker Commissioner Lilian De las Llagas. Likewise greetings to our renowned speakers from MIT, Stanford, Singapore, Thailand, Taiwan, and Philippines and all members of the Asia eHealth and Information Network, and all those who are into the fields of artificial intelligence, machine learning, analytics and technopreneurship, who found time to share with us today the state-of-the-art in big data for health. Welcome to Cebu City, the oldest city in the Philippines. It has a population of about a million as of 2016, while the whole of Cebu Province is the second most populated province in the Philippines with a population of about 2.9 Million as of 2016. Cebu City alone has an area of 315 square kilometers while Metro Cebu has an area of 1,062 kilometers. In the whole of Cebu Province, we have more or less about 57 hospitals, which includes both the private and public hospitals, with the district hospitals in the municipalities covered among the public hospitals. We in UP Cebu does not have any hospital at all. It is only in UP Manila that we have a university hospital. So why are we hosting this event? Not only because Doc. Eloy requested us, and we grabbed the opportunity that experts like you are already here, but also because we in UP Cebu aspire to become a Data Science Center in the near future. With the increasing digitization of health data, among other pertinent data, we too in UP Cebu are finding ways on how we can be relevant in the digital world and keep up with the developments, not merely as a data warehouse, but to be able to process these data and establish their relationships and analyze them, for the better understanding and benefit of our community. As a National University, we are committed to National Development. In relation to this quest for national development, it is our mandate to identify key concerns, formulate responsive policies regarding these concerns. And it is our Social Responsibility to relate our activities to the needs of the Filipino people for social progress and transformation. No doubt, viral attack, be it in the medical or IT industry, is a major concern for any one of us here right now. Come to think of it, there is so much similarity between health management and IT management, even in the usage of words alone, we hear common terms like having gone viral, boosting IT immunity, in this Cyber ruled world.

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Just recently, we have heard of Ransom Ware on how it had caused chaos in hospitals, which were crippled by these cyber attacks. Others say this is just the beginning, as hackers use malware atomic bomb. I hope we can be a step ahead on all these, instead of us merely being reactive, with all our cyber experts being pro-active. So I hope thru this Big Data for Health Conference and Workshops, we can seek ways to understand and obtain actionable insights from Big Data gathered not just in health, but among all other sources. And I hope out of this, we can encourage our policy-makers, innovators, academics and business leaders to discuss strategies and approaches to making health data work for improving health systems, be it in cybersecurity, among others. And in the future, other big data’s analytics to follow for improving our various systems. The future will be awash with data but it will be our ability to manage, analyze and use and protect these data that will propel our country’s development and provide us the cutting edge not just in the region, but globally as well.

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Keynote Speech Commissioner Lilian de las Llagas Distinguished Organizers UP-MIT-Stanford-AeHIN Big Data for Health Conference Workshops for Asia Pacific; invited participants; guests, friends, a pleasant morning to all of you. We are gathered here today to discuss a very timely and responsive topic on health –Big Data for health. “Who will keep the Public healthy for the 21st century?” asked by the Committee on Educating Public Health Professionals for the 21st century, Washington, DC. I asked, “What should be mainstreamed; institutionalized and sustained?” This event is an opportunity, a platform to provide leverage to Sustainable Development Goals (SDGs) and enhance health of the publics across the nations. The 2030 Agenda for the 17 SDG’s reflects a significant change in thinking about what should be achieved and how to go about it. This applies to development in general, as well as health more specifically The health goal SDG3 builds on significant success of the health-related MDGs, but it is also much broader in scope, calling on everyone to “ensure healthy lives and promote well-being for all at all ages. SDG’s not only implicitly recognize the need to close-out the unfinished MDG agenda, but also respond to new priorities such as communicable diseases, health security, and health impact of migration and climate change. Achieving these goals is of utmost importance to our region. The local health system this 21st century is faced with scientific and technological advances, environmental and socio-demographical changes: There are major challenges related to these advances, in SCIENCE and HEALTH related technologies which include legal, social and ethical questions. Communication technology, for example, offers increased opportunity for dissemination of health information but also requires a response to the misleading information. Before, we need days to have access to health information; we need to search through piles of books, documents and read journals; and write health agencies to obtain health data and wait for their responses. Now, we have Public Health Informatics and Health informatics. These offer great potential for improving our surveillance and healthcare system. But this is accompanied by concerns regarding confidentiality and security of the information systems. Health System Information is very much needed. An e-health network is essential to share, exchange information; a need to link inter and intra-operate to combat diseases shared commonly by the region.

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We need an e-Health to understand common health issues cross-culturally; to bridge the gap of information and action – to connect, and to harmonize best practices across cross challenges, and with a large volume of data available. What will the health organization do to manage that matters to them? There should be a system to fix these big data, such that, analytics come in handy. What do we have? In UP Manila, the National University for Health: • The National Telehealth Center of the National Institutes of Health – which is in the forefront of Health Information Technology Innovations, while tackling new challenges in terms of governance, sustainability, legal and ethical considerations in the use of e-Health tools. • Using ICT of the prevention and control of parasitic diseases (CPH-UPM). • A model of Dengue Prevention and Control for BIG DATA usage (CPH, UP Manila and School of Statistics, UP Diliman) • Data on ecologic study of Schistosomiasis The Department of Health has come up with the Philippine e-Health Strategic Plan 2014-2020 to expand access and use of e-Health information in the country. It has acknowledged the use of e-Health as a tool to support and facilitate the achievement of the National Health Goals of better health outcome. What do we need? How? • To operationalize; we need to FIX our method of gathering qualitative data; to have a true picture of our health landscape; • To institutionalize; (to move). We need a framework to eclipse all the materials, methods, machines, and resources in place Distance is no longer an ocean between the health providers and the health recipients (health informatics must be thoroughly established) What is the framework that is desired in this workshop? Do we know data management; analytics using precision and accuracy as our regulatory nodes? A need to have a framework to and or upon an efficient, effective regional information exchange system. At the Commission on Higher Education, we are initiating an HEI program review on health informatics to educate the health professionals. Finally, I take this opportunity to express my sincere appreciation to Asia e-health information network through Dr. Marcelo and the organizers, and for making my presence, and CHED a part of your conference. I reiterate my warmest congratulations to you all. I wish you a successful conference-workshop and an enjoyable stay here in Cebu through the warmth and hospitable hosting of the Chancellor, Atty. Liza D. Corro, UP Cebu, The CHED Centre for Excellence in Information Technology. Thank you. 8


Plenary Sessions

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The Role of Governance, Architecture, Program Management, Standards and Interoperability (AeHIN’s Mind the GAPS) in Health Data Analytics Dr. Alvin Marcelo introduced the Asia eHealth Information Network (AeHIN) as a pool of professionals from South and Southeast Asia committed to promoting better use of ICTs to achieve better health. Today, the network is composed of about 1,000 professionals in eHealth, health information systems, and CRVS in 25 countries. AeHIN is also supported by 21 development partners and implementing partners combined. Alvin Marcelo, MD

The speaker showed the timeline of AeHIN annual general meetings Speaker which started way back in 2011 when HIS professionals in different parts of Asia participated in the interoperability conferences in Hoi An (April) and Manila (June). This was followed by the first AeHIN meeting organized in Bangkok in 2012. Today, five AeHIN general meetings have already been conducted. The most recent one was held in Nay Pyi Taw, Myanmar last March 2017 which was attended by around 300 participants. AeHIN’s strategies are to 1. Enhance leadership and sustainable governance, and monitoring and evaluation; 2. Build capacity for eHealth, Health Information Systems, and Civil Registration and Vital Statistics (CRVS); 3. Increase peer assistance and knowledge exchange and sharing through effective networking; and 4. Promote standards and interoperability within and across countries AeHIN conducts bi-monthly webinars called the ‘AeHIN Hour’ where experts from all over the world are invited to talk about eHealth-related topics. AeHIN also supports in-country webinars, such as the Bangladesh Hour, Cambodia AeHIN Hour, Thai AeHIN Hour, and AeHIN Hour PH. It promotes resource sharing among the members through the HingX platform which can be accessed through http://aehin.hingx.org. It also disseminates quarterly newsletter issues through its official publication, ‘The Hexagon’. Some of its capacity-building activities include COBIT 5 training (50 participants over nine countries), TOGAF training (29 participants over 7 countries), and AeHIN Academy via AeHIN Hour webinars (more than 1,000 participants for 65 topics). It is noteworthy to mention that AeHIN has been part of the ADB policy briefs on health IDs, CRVS, and Geographic Information Systems and Interoperability. It also supports the Laos CRVS program and the forthcoming convergence workshops in Viet Nam and Nepal. Currently, AeHIN is spearheading special interest groups such as the AeHIN Geographic Information Systems Laboratory, Standards and Interoperability Laboratory for Asia, Routine Health Information Systems Group, DHIS2-implementing countries, and a research group where 10 PhDs will work on AeHIN-related topics.

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Based on the WHO ITU National eHealth Strategy Toolkit, AeHIN developed its own ‘Mind the GAPS, Fill the GAPS Framework.’ The Network advises governments to establish a governance (G) structure and framework to oversee the eHealth development in the country. This includes evaluating the needs of the stakeholders, setting directions (benefits, risks, resources), and monitoring performance. The governing body then disseminates a shared architecture (A) that informs stakeholders what their roles are and how they can contribute to national eHealth development. Consequently, stakeholders should acquire good program management (P) capabilities to be able to comply with this architecture including the ability to manage data and content. Lastly, there should be standards to enable interoperability even if stakeholders have competing agenda. To help countries develop their own national eHealth strategies, AeHIN has also developed its ‘National eHealth Capacity-building Roadmap.’ This roadmap suggests certification training to be taken and materials to be used in each step of the roadmap: 1) National eHealth Action Plan; 2) eHealth Governance Framework; 3) National Standards and Interoperability Framework, 4) Health Information Exchange; 5) Interoperability Profiles, Terminology Services, 5) Management Plans, Policies, and Procedures; and 6) eHealth Service Agreements. At the regional and country level, AeHIN is also trying to build an interoperability framework or open eHealth information exchange, which is similar to the health information exchange platforms in Malaysia and Indonesia. Dr. Alvin Marcelo ended his presentation by sharing a motto from AeHIN, “when we help friends, friends will help us.”

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Resource Sharing in Medical Education towards Improving Health Outcomes, Medical Mobile Clinic and Telemedicine The presentation of Dr. Julietta Gabiola focused on three initiatives: 1) Digital MEDIC; 2) Medical Mobile Clinic; and 3) Doctorgram. The first part of the presentation showed how the global deficit of health workforce will double by 2035. With this regard, Stanford launched Digital MEDIC, a multi-faceted platform which provides digitally enhanced and learner-centric courses on medicine. Digital MEDIC aims to address gaps in medical education, particularly the lack of medical educators and medical professionals.

Julietta Gabiola, MD Speaker

One of its features is content creation where multiple content creators develop healthcare education content in the form of foundational courses (e.g., applied biochemistry, quantitative medicine, and point of care ultrasound) and high-impact training (e.g., emergency medicine and maternal & child health). The content creation process also promotes adaptive learning where students actively engage in activities and assessments. Personalized feedback is also given. Another feature is the content library, a shared media platform (i.e., teaching resources and guides; a digital library of lessons; and analytics and data collection) which aims to contribute to the global digital repository in improving healthcare delivery and healthcare education. Lastly, it also features content delivery via a learning management system or a mobile application to reach its end users mainly, informal health workers, allied health professionals, medical students, and educators. The second part of the presentation introduced the Medical Mobile Clinic, a community outreach program which deploys healthcare services (i.e., prevention, education, and research) on wheels. This program is part of the ABCs (Advocacy, Betterment, and Commitment) for Global Health initiative. It aims to address the challenges of healthcare access. Due to geographical barriers, awareness, treatment, and control for the increasing prevalence of chronic diseases (e.g., hypertension, diabetes, and asthma) has become slow. Thus, the disparity of care emerges. The Medical Mobile Clinic offers advantages where short-term medical missions and understaffed fixed clinics fall short. The identified advantages of Medical Mobile Clinic include collaboration with fixed clinics to promote continuity of care and gain feedback from the community. It bypasses access challenges and offers services within the comfort zone of the people in a less formal and less intimidating environment. Diabetes and hypertension are among the top clinical cases catered to by the mobile clinic. The team is composed of a licensed physician, support staff, registered nurse, custodian, and coordinators from both the Philippines and the USA. As of now, proposed services include referral services, follow-up care, and health assessments among others. Lastly, the presentation also introduced Doctorgram, a telemedicine platform involving mobile phone application and electronic medical devices. This initiative they are currently working on is also part of the ABCs for Global Health. It has low-cost, low-bandwidth requirement; and store-andforward, real-time function. Dr. Julietta Gabiola showcased the actual device during the plenary session. 12


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Sharing Critical Care Data for Research and Education Dr. Tom Pollard highlighted that there is a research opportunity in the huge volumes of data being captured daily. Specifically, he explained that these data could be used to discover new knowledge for the benefit of patients. However, these data are inaccessible to researchers. To address this problem, an openly available dataset has been developed by the Massachusetts Institutes of Technology (MIT) Lab for Computational Physiology under the name, Medical Information Mart for Intensive Care (MIMIC) project. The MIMIC dataset is comprised of deidentified health data associated with approximately 40,000 critical care patients. These data include demographics, vital signs, laboratory tests, medications, and more.

Tom Pollard Speaker

The data collected from the intensive care unit (ICU) have undergone de-identification, data shifting, and format conversion before they make it to the MIMIC database. After passing through user feedback and corrections, the filtered data now go back to the data archive of the ICU. Two key steps are needed to gain access to MIMIC: 1) complete a recognized course in protecting human research participants; and 2) sign a data use agreement. Today, MIMIC is widely used in research, teaching, and industry. As an example, Dr. Tom Pollard showed some journal articles involving the MIMIC database such as ‘Reproducing a Prospective Clinical Study as a Computational Retrospective Study in MIMIC-II’ published in AMIA Annual Symposium Proceedings Archive, and ‘Mortality Prediction in Intensive Care Units with the Super ICU Learner Algorithm (SICULA): A Population-based Study’ published in The Lancet Respiratory Medicine. There are about 20 courses in the USA using MIMIC. It also employs a datathon model to support cross-disciplinary collaboration. More details about the MIMIC project can be accessed through http://mimic.physionet.org and http://eicu-crd.mit.edu. In summary, Dr. Tom Pollard stressed that different audiences can take advantage of MIMIC: 1) for a new investigator, it can be used for research and replication of their own studies; 2) for a department head, it can be used for teaching; 3) for an information officer, it can be used to develop a multi-center dataset; and 4) for a grant reviewer, it can consider the value of data sharing with the research community.

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The Rise of Health Hackathons and Datathons

MIT Sana has been spearheading hackathons and datathons in Mexico, Thailand, and other countries in the field of mobile health technology. Dr. Mataroria Lyndon started his presentation by providing a definition of hackathon and datathon. Hackathons were described as “intense competitions of short duration in which teams generate innovative solutions.” On the other hand, datathon’s focus is more on data analysis and using big data. “It is a cross-disciplinary event to address specific challenges in healthcare with technology and data analytics.”

Mataroria Lyndon Speaker

Dr. Mataroria Lyndon presented some challenges in healthcare which include failure to harness technology and analytics; lack of cross-disciplinary collaboration; and limitations of clinical research and practice. ‘Big data’ lies at the center of healthcare, information science, and computer science. To address the identified challenges in healthcare, a crossdisciplinary collaboration is encouraged among these fields. Utilization of health data leads to the production of health research; generation of IT solutions leads to enhanced healthcare delivery. In health datathons and hackathons, the first step is to ‘pitch a problem’ by asking questions (e.g., ‘how to improve rates of blood donation’ and ‘how to support people to quit smoking’). The second step is to ‘form cross interdisciplinary teams’ by bringing together clinicians, engineers, entrepreneurs, and data scientists among other fields. This should encompass cultural and international collaboration where there is an opportunity also for more women to participate. The third step is ‘hacking’ which takes place about 48 hours working alongside the data scientist. The fourth step is ‘presenting ideas’ to the judges on the final day (with awards such as cash prizes). The last step is where ‘hackathon outcomes’ should materialize. A datathon/hackathon model supports a cross-disciplinary collaboration. It contributes to medical technology innovation, including clinical trials, business plan, development, securing investment capital/funding and new company formation. To conclude, Dr. Mataroria Lyndon imparted that the significant challenges in healthcare require innovative solutions. He shared that hackathons and datathons are valuable sources of healthcare research and innovation through cross-disciplinary collaboration. He also recommended that further research is needed to determine the impacts on healthcare delivery and outcomes.

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Machine Learning in Healthcare In this presentation, Dr. Alistair Johnson discussed that huge amount of data now available enables machine learning. “There’s lots of excitement about new insights the algorithms can discover,” he said. To showcase, he presented a graph depicting the dramatic increase in percentage of office-based physicians with electronic health record systems in the USA from 2011 to 2013. The speaker exemplified that in critical care, large volumes of data are streamed every second and that most of the time, only a single attending physician is caring for five or six patients a time. There’s only so much data they can handle. Hence, in the critical care setting, smart algorithms will play an increasingly important role.

Alistair Johnson Speaker

However, handling the data is not easy. One of the biggest studies using big data in healthcare from 2016 shows that healthcare’s current data archiving model is broken: with key measurements for patients not being captured appropriately. This leads to the question, ‘why aren’t we doing better?’ With this, Dr. Alistair Johnson emphasized that the biggest barrier to machine learning in healthcare is the lack of access to high-quality data. Dr. Alistair Johnson introduced the Medical Information Mart for Intensive Care (MIMIC)-III database as the flagship dataset for ICU informatics, providing thousands of users with access to high-quality high-granularity data. In the same way, he also introduced the eICU Collaborative Research Database, another large critical care database. He further stressed that a collaborative approach, with public sharing of documents, data, and code (GitHub) is key to building a community around the data and accelerating research in the field. The presentation ended with an example model developed in the ICU, showing how big data can be used in the critical care setting. The example model accurately predicted key patient outcomes such as death and length of stay using large quantities of data available such as heart rate, respiratory rate, and blood pressure.

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Physiological Signal Processing Chen Xie presented a brief background and history of arrhythmia algorithms. He recounted that during the 1970s, development of arrhythmia analysis algorithms was hampered by a lack of universally accessible data. Each group acquired and kept its own set of recordings. Thus, it was difficult to compare the performance of algorithms tested on different datasets. He then presented the MIT-BIH Arrhythmia Database, 48 half-hour excerpts of two-channel ECG recordings collected from 47 subjects. Each beat is labeled by two clinicians. As an open-shared data, this database has implemented universal standards in cottage industry, facilitated evaluation and competition, and eventually accelerated technology development.

Chen Xie Speaker

The impact of MIT-BIH Arrhythmia Database has set the foundation for Phsyionet, the research resource for complex physiologic signals. It makes use of open data, open source software, and protected workspaces. Operating with an open data, it has over 90 freely accessible databases, over 4TB of data, physiological signals, and clinical data. Chen Xie introduced two recent database contributions from the research community: the CHBMIT Scalp EEG Database with 252 channel EEG recordings of pediatric subjects with intractable seizures, and the Squid Giant Axon Membrane Potential Database, which contains single-unit neuronal recordings of North Atlantic squid giant axons in response to stimulus currents. The signals were shown to be available for preview on Lightwave, the online waveform viewer. With open source software, PhysioTools includes ECGSYN, which generates a synthesized ECG signal with user-settable mean heart rate, number of beats, sampling frequency, and waveform morphology; HRV Toolkit, an analysis of heart-beat statistics; and the WFDB Software Package, a waveform database format read/write, feature calculation, and analysis. Chen Xie also showed samples of topics that are part of the Physionet CinC Challenge. He mentioned that the challenge in 2015 is ‘Reducing False Arrhythmia Alarms in the ICU’ while in 2016 - ‘Classification of Normal/Abnormal Heart Sound Recordings.’ To conclude the presentation, Chen Xie discussed the benefits of sharing data and tools: 1) allow the community to leverage your content and thereby maximize research output; and 2) give validity to your study amidst the reproducibility crisis. More details can be found at http://physionet.org.

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Artificial Intelligence in Medicine Dr. Wei Hung-Weng commenced the presentation by showing journal samples on artificial intelligence such as the ‘Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs’ and ‘Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media.’ He explained that in the recent year, machine learning and artificial intelligence approach have become popular in solving medical problems. Today, 106 startups are transforming healthcare with artificial intelligence. Wei-Hung Weng In line with this, he presented key points in the emerging field of medical Speaker artificial intelligence: 1) ‘use of high-quality multi-modal data’ such as MIMIC (Medical Information Mart for Intensive Care)-III and medical images; 2) ‘adoption of electronic health record’ which helps in the easy assessment of real clinical data; 3) ‘standardization of medical language’ to integrate data from different data sources easily such as the ICD Code; and 4) democratization of knowledge and resources by making use of algorithm and techniques that will improve the community.

From a clinical perspective, there has to be cost or risk assessment and adjustment which will be helpful for insurance and resource allocation. In the same way, precision or personalized medicine must be accounted for oncology, rare diseases, mental disorders, etc. This is to support also applications on clinical decision support, drug discovery, outcome prediction, and chronic disease management. From an artificial intelligence perspective, it is important to show potential technical problems that might be faced with when dealing with medical data. Some of these are risk stratification, causal inference, bias, time series, unstructured data, interpretability, disease progression modeling, and reasoning as well as decision making. Dr. Wei Hung-Weng also discussed some potential issues in medical artificial intelligence such as the data security problem, transparency of methodology, and fear of people that artificial intelligence would replace their jobs. This infers that medical artificial intelligence model must be interpretable to clinicians. Some limitations of artificial intelligence were also described such that it lacks emotional component, creativity, social intelligence, thinking, reasoning, decision-making, and autonomous manipulation in an unstructured environment. With this regard, the speaker likened artificial intelligence to a super computational power which can perform repetitive tasks like calculation. But at the end of the day, he reminded that computers still don’t know what they’re doing. The prediction is that highly-skilled jobs can be replaced by computers. He then showed a journal on adapting with artificial intelligence in the medical field. In line with this, he presented some callto-action: 1) find good problems and collect reliable data rather than focus solely on big data and algorithms; 2) encourage collaboration (collaborating with a machine means lower error rate) rather than rely fully on artificial intelligence; 3) promote sharing where engineers can work on open-source platforms and algorithms and where clinicians can work with open-source data and knowledge; and 4) promote experience by learning how artificial intelligence works and how to communicate with a machine. Dr. Wei-Hung Weng’s take away message is to apply a data-driven, machine learning-based approach with standardized medical language. He mentioned that defining good problems and finding reliable data sources must go together. Ultimately, he stressed the importance of collaborating with the machine and human community through sharing, and the value of experiencing, learning, and communicating that come with it. 17


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Big Data for Public Health Dr. Stephanie Ko explained that good data can transform the healthcare landscape. She defined public health as the health of people, of populations. This refers to how we deliver healthcare in terms of quality, access, and cost, as supported by disease prevention and social determinants. In terms of healthcare quality, the question that must be asked is ‘how do we ensure patients get the care they do need and don’t get the care they don’t need?’ In other words, the speaker stressed the importance Stephanie Ko of delivering the right healthcare to the people at the right time. By Speaker using large amounts of data (e.g., vast number of variables from the electronic health record), good prediction models can be created. This can be used in improving healthcare service delivery in a population as a whole. In terms of healthcare access, one should ask, ‘how do we get healthcare to people who need it without suffering financial hardship?’ Universal Health Coverage (UHC) is part of the sustainable development goals. As an evidence, the speaker presented a survey data to show that the number of households suffering from medical impoverishment decreased with the introduction of UHC in Thailand. In terms of healthcare cost, the question should focus on the optimization of quality and access to the amount that we are spending. To illustrate, Dr. Stephanie Ko showed a graph of life expectancy versus per capita healthcare spending in international dollars. It can be inferred from the data that there is a difference on how countries spend for healthcare. This also shows that higher spending in healthcare does not equate to longer life expectancy. This reflects that big data can track changes in outcomes and effects of health policy. For disease prevention in healthcare, the problem that must be solved is how to convince people to adopt healthy behaviors and avoid developing diseases. To show the use of big data, mobile health monitors can track the impact of lifestyle interventions on health behavioral change. The example presented was the study on the average steps taken per day of Pokemon GO users versus non-players since the week of installation. Lastly, for the social determinants of healthcare, it is important to improve the ways people eat, live, study, work and play for better health outcomes. Dr. Stephanie Ko cited the study of Marmot back in 1991 concluding that social conditions are more powerful determinants of health than access to care. The status of social equity (e.g., quality of education granted to people of different classes) can determine if their day-to-day living contributes to the betterment or detriment of their overall health. In summary, Dr. Stephanie Ko explained that big data on public health has the potential be used to evaluate policy, programs, and interventions; and predict individual risk of mortality, disease, and utilization. Results of evaluation and prediction must then be used to identify gaps in quality, access, cost, and equity so that they may be able to identify best practices that can improve the health of people.

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PLENARY SESSIONS

Protecting Big Data: Complying with the 2012 Data Privacy Act of the Philippines Dr. Raymond Sarmiento presented a timeline of significant events after the Data Privacy Act was passed on August 21, 2012. In 2014, the National eHealth Privacy Experts Group was formed. The following year, the National Privacy Commission members were appointed. In the same year, the Implementing Rules and Regulations (IRR) was published and deemed effective 15 days from publication. The speaker also defined important terms in the Data Privacy Act of Raymond Sarmiento, MD 2012 such as 1) personal information – “any information whether Speaker recorded in a material form or not, from which the identity of an individual is apparent or can be reasonably and directly ascertained by the entity holding the information, or when put together with other information would directly and certainly identify an individual”; 2) personal information controller – “a person or organization who controls the collection, holding, processing or use of personal information, including a person or organization who instructs another person or organization to collect, hold, process, use, transfer or disclose personal information on his or her behalf”; and 3) personal information processor – “any natural or juridical person qualified to act as such under this Act to whom a personal information controller may outsource the processing of personal data pertaining to a data subject.” In addition to terms related to personal information, Dr. Raymond Sarmiento also explained that a personal information can be labelled as sensitive if it’s: 1) “about an individual’s race, ethnic origin, marital status, age, color, and religious, philosophical or political affiliations”; 2) “about an individual’s health, education, genetic or sexual life of a person, or to any proceeding for any offense committed or alleged to have been committed by such person, the disposal of such proceedings, or the sentence of any court in such proceedings”; 3) “issued by government agencies peculiar to an individual which includes, but not limited to, social security numbers, previous or current health records, licenses or its denials, suspension or revocation, and tax returns”; and 4) “specifically established by an executive order or an act of Congress to be kept classified.” The section on ‘Security Measures for Protection’ in the IRR also shows how organizational security measures can be observed through 1) assigning compliance officers; 2) formulating data protection policies; 3) establishing records of processing activities; 4) management of human resources; 5) processing of personal data; and preparing contracts with personal information officers. Under this section, the speaker also emphasized that “the head of each government agency or instrumentality shall be responsible for complying with the security requirements mentioned herein.” On the other hand, the section on ‘Data Breach Notification’ states that “the Commission and affected data subjects shall be notified by the personal information controller within seventy-two (72) hours upon knowledge of, or when there is reasonable belief by the personal information controller or personal information processor that, a personal data breach requiring notification has occurred.” Dr. Raymond Sarmiento explained that the offender will need to pay an amount between one million (minimum) to five million (maximum) for breach and that the head of the agency will be liable for up to six years in prison. In this case, business-as-usual is not acceptable. 19


PLENARY SESSIONS

This calls for a need to assess current workflows vis-Ă -vis Data Privacy Act and IRR (e.g., perform threat and vulnerability assessment). Dr. Raymond Sarmiento mentioned that this is an opportunity for the University of the Philippines Manila to lead the way (e.g., to model how a health institution should keep personal information private). The National Privacy Commission recommends the adoption of a data privacy accountability and compliance framework working around these components: governance; risk assessment; organization; day-to-day; data security; breaches; third parties; management of human resources; projects; and management of legal matters. As a conclusion, Dr. Raymond Sarmiento advised that we need to consider these things: 1) type of information being collected; 2) reason for collecting; and 3) ways or measures to handle information provided (physical, technical, and organization). Physical measures refer to policies and procedures; and design of office space. Technical measures pertain to security policy and data encryption. Lastly, organizational measures are DPOs and policies such as NDA, privacy policy statement; records of processing activities; HR management; and personal data processing.

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PLENARY SESSIONS

Protecting Big Data: United Kingdom Experience Prof. James Batchelor’s presentation introduced the EDGE Programme UK with 92, 000 clinical research projects, 36, 000 users, and 3.1 million patient records. One of its projects, the 100K Genome Project is running around 100,000 genome sequences with linked data. The speaker discussed that they pursued the integration of their informatics team in the clinical setting. They have invested in informatics within the NHS and the University of Southampton to build a foundation to create a unique partnership in health informatics, clinical research, and translational research.

Prof. James Batchelor Speaker

The Hospital and Research Informatics is composed of the (1) Clinical Informatics Core and (2) Bioinformatics Core. The Biomedical Informatics Platform is patient-centric and focuses on genomic care. It consists of the NHS EPR development team, clinical informatics research team, computational genomics, and bio-informatics. NHS is made up of 475 smaller entities. Under the Open Plan Care, they integrated the health record of the patients into a single repository and used one physical data warehouse. Health benefits components include: 1) big data - collecting, storing, and analyzing large volumes of data; 2) data without boundaries of hospitals walls, sensors, and remote data capture from the patient; 3) access to data collection tools and re-use of applications and data; 4) communication systems to support patient care and service interaction. In terms of technology, the trade and services industry is now developing a healthcare-specific application. The impact of technology has improved care, experience, cost saving, patient trust, and legalities in healthcare. It was able to escalate health transformation, especially with the emergence of electronic health record. However, Prof. James Batchelor explained that we might be a little bit behind. Most hospitals have not curated their data and do not use data or metadata dictionaries. There is data duplication as well as considerable lack of resource. With regards to interoperability, most clinical systems today use HL7. Industries use CDISC, while almost the rest of the world uses XML. With regards to security, regulations must be set to address public concerns and assure public trust. Patients are now using healthcare applications but the more important question to ask is if the data would be useful and if these would feed the health record. In terms of quality information, it is important to ensure the completeness and compliance of datasets. They also observe the UK Data Protection Act 1998, which protects the rights of individuals, and the use and processing of their personal information. Apart from these, they also observe the regulation of medicines and healthcare products, information governance toolkit, and good laboratory practice. The future technology landscape is expected to increase capabilities in the hospital. This patient-centric landscape is supported by big data; map of patient care; clinical centric software; innovative applications; health platform (hybrid platform as a service); security and regulation; cloud; and cost computation. To conclude, Prof. James Batchelor discussed that they have to start adopting new technology within the NHS that is clinical focused. They need to have systems that the public would trust when it comes to sharing data and genomics. He also mentioned that clinical systems will be the only way they can get the data they need. He imparted that they need to innovate to make all of this happen. 21


PLENARY SESSIONS

Building an Ecosystem around Health Data Analytics Dr. Leo Anthony Celi explained that the existing innovation pipeline system is effective only for market ready technologies. However, there’s no infrastructure to support complex, slower-growing concepts that could have huge long-term impact. He added that healthcare is the best example of a domain that requires slow-to-build and complex systems innovations. The speaker pointed out that the real barrier to innovation is not Leo Celi, MD Speaker technology. Technology alone cannot fix the problems of healthcare. The vision is that of a learning health system that is fueled by locallyderived data. He described the Medical Information Mart for Intensive Care (MIMIC) database as an example of an initiative to learn from data routinely collected in the process of care. MIMIC is a publicly available de-identified repository of patient data from the intensive care units of Beth Israel Deaconess Medical Center, a teaching hospital of Harvard Medical School in Boston, Massachusetts, where Dr. Celi attends. However, most are unaware of the value of health data. One of the biggest barriers in low- and middle-income countries is that health data doesn’t even exist in a machine-ready format: they are buried in paper records. Another is a lack of talent when it comes to building and sustaining a digital health infrastructure that requires expertise in data integration, harmonization, and analysis. Systems interoperability, a requirement for data integration, remains a pipe dream in these countries. Dr. Celi then presented Sana, an inter-disciplinary global consortium of clinicians, engineers, policy, public health, and informatics experts across the entire healthcare value chain. Sana is hosted at the Laboratory for Computational Physiology at MIT’s Institute for Medical Engineering & Science. It addresses the talent shortage in global health informatics, the building blocks of digital health. One of the organization’s publications, the textbook ‘Global Health Informatics: Principles of eHealth and mHealth to Improve Quality of Care’, focuses on the use of mobile phones and other information and communication technologies in capturing data in resourceconstrained settings. Sana also launched a course in edX. It must be noted though that Sana is not only focused on education and training; it is involved informatics projects around the world. Some of past and ongoing projects include acute care and triaging (Kenya), cardiovascular screening (India, South Africa), tele-dermatology (Mongolia), and post-operative follow-up (Haiti), vaccine registry (India), mobile health record system for refugees (Lebanon) and various projects to promote reproductive health (Uganda). In addressing the talent shortage in health data analytics, Dr. Celi cited another publication from their group on ‘Secondary Analysis of Electronic Health Records’ which was written with the aim of promoting a cross-disciplinary approach to health data analytics. The speaker reiterated that the biggest barrier to innovation is not technical but cultural in nature.

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PLENARY SESSIONS

For that reason, he explained that they travel around the world to engage, recruit and interface data scientists, engineers and clinicians. His group at MIT is breaking down the silos between these disciplines one event at a time. Examples of these events are mHealth Colombia; United Nations Population Fund #Hack4Youth; Bootcamp-Hackathon in Mexico; Thailand mHealth Bootcamp & Hackathon; and critical care datathons in Beijing, London, Melbourne, Singapore and Paris. More details can be found at http://sana.mit.edu and http://criticaldata.mit.edu. Dr. Leo Anthony Celi shared a quote from Albert Einstein, “we can’t solve problems by using the same kind of thinking we used when we created them.” He reiterated that we need a fresh perspective to look at things and try to change the culture by training a new generation of leaders who value collaboration. He emphasized that the greatest accomplishments and the most disruptive innovations are seldom the work of a single individual. To expand on this topic, the speaker presented Project Aristotle that involved Google’s People Operations department studying many teams at Google to see what made them work well with each other. Surprisingly, they found no evidence that the composition of a team made any difference: the ‘who’ did not matter. What matters is the ‘group norms’ (i.e., traditions, behavioral standards, and unwritten rules) that govern how a team functions. The key elements are conversational turn-taking and high ‘‘average social sensitivity’. A closely-related concept is psychological safety, which refers to the interpersonal trust and mutual respect in which people are comfortable being themselves. To conclude, Dr. Leo Anthony Celi, advised that the recipe for innovation is a ‘great groups mentality.’ With this, he said we need to influence not only how people work but how they work together.

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PLENARY SESSIONS

Nurturing Innovation & Entrepreneurship in Academia Dr. James Weis presented some global trends democratizing capacity for innovation. He started his explanation by showing examples that would illustrate Moore’s law such as the increasing storage capacity of computers and the increasing total word count of Wikipedia over the years. The Internet of Things also shows that today, billions of devices can be interconnected. The technology today is quickly advancing that you can even order human genome with your phone. Dr. James Weis mentioned that we are living in a uniquely exciting time. However, one must note that resources are not synonymous with innovation. Innovation is all about the culture they create to encourage people to be innovative and creative.

James Weiss Speaker

The speaker also pointed out that innovation varies enormously between institutions. He said that the majority of research organizations contribute very little to overall technology transfer— regardless of how that commercialization activity is measured. Hence, he emphasized that commercialization is not equal to technology transfer. Dr. James Weis also discussed that a meta-entrepreneurial approach is important to facilitate innovation, where organizations will be built. To do this, an engineering approach must be taken. Below are the guiding steps: 1) identify bottlenecks to innovation; 2) leverage existing resources; 3) align incentives between related parties; 4) nurture new initiatives; and 5) address bottlenecks that stifle innovation. Lastly, the speaker cited some organizations that were built to nurture innovation such as the MIT Biotech Group which houses Synapse, the MIT Biotech Group; REB, research experience in biopharma; and the MIT Alumni Angels of Boston Life Sciences Track. He also mentioned Nest Bio which develops innovation through research, labs, and ventures.

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Parallel Workshops

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PARALLEL WORKSHOPS

Workshop 1

Physiological Signal Processing False alarms in the ICU can lead to a disruption of care, and thus a decrease in the quality of healthcare. The challenge was to create an algorithm for identifying true and false ventricular tachycardia alarms. The workshop started by setting-up Python and needed packages. This was followed by exploring data using Jupyter-notebook, visualizing signal data through graphs, cleaning data for better results, identifying and visualizing features, and finally training and testing machine learning models. The session also held a workshop with VBears where they identified beats. A final analysis also took place where the group pondered upon the role of software developers for big data for health. Even with the lack of medical know-how, software developers have the technical know-how in developing tools to enable proper intervention. The code presented by Chen Xie in this workshop can serve as a model for signal processing work on Python, utilizing standard scientific libraries including Scikit-Learn and Pandas. Collaboration between MIT, Stanford, AeHIN and local universities without medical tracks is also encouraged. Universities with capabilities in computer science can augment manpower in writing tools to make sense of all the data available. The takeaway message is to push for a culture of collaboration, contribution, and leveraging public data and software, when conducting data analysis.

Chen Xie Facilitator

Van Owen Sesaldo Rapporteur

Workshop 2

Integrating Technology in Medical Education Technology is a given, but not a debate. The workshop promotes more ways of thinking in medical education. Hence, developing frameworks is important, where all teachers will have a content method on how to teach. The processes of redefinition, modification, augmentation, and substitution are also presented.

Julietta Gabiola, MD Facilitator

A free account at http://ed.ted.com/lessons can be created where links can be shared with students or embedded. Resources for this workshop can be accessed through http://slideshare.net/isiptan and http://digitalmedic.org. They have a creater module, library, and learning management system. Opportunities identified include faculty skills for development workshops and best practices in the creation of an online community of medical faculty interested in technology for medical education.

Iris Isip-Tan, MD Rapporteur

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PARALLEL WORKSHOPS

Workshop 3

Big Data for Public Health And eHealth The Big Data for Public Health Workshop was guided by these steps: 1) designing a public health question; 2) designing an analysis to answer the question; 3) analysis of an actual dataset; and 4) presenting results with public health implications. The data used was extracted from the eICU collaborative research database where 200,000 admissions have been de-identified and made available for analysis.

Stephanie Ko Facilitator

The workshop was divided into two: 1) regression model for predicting the length of hospital stay (question 1); and 2) size of bed affecting patients in the ICU (question 2). The takeaway message from the workshop is that year of expertise is not always required to perform big data analysis for public health. What’s important is to understand, clean, and analyze the data. Today, people involved in big data for public health are looking for more collaboration by participating in hackathons and datathons.

Raymond Sarmiento, MD Rapporteur

Workshop 4

Biomedical Text Mining and Natural Language Processing Workshop URL: http://ckbjimmy.github.io/2017_cebu The workshop was segmented into 1) setting up R and Studio; 2) introduction to information retrieval, information extraction, and text mining; and 3) machine learning based approaches for text mining.

Wei-Hung Weng Speaker

Alvin Marcelo, MD Rapporteur

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The participants were given the link to all resources and scripts. The data source was extracted from physionet.org which contains de-identified medical text. It takes five to seven days for a Physionet account to be approved. They also used R scripts which already have the algorithms to explore or discover http://physionet.org. One of the opportunities identified is the use of the workshop content for academic courses. Datasets may also be obtained easily from http:// physionet.org. In addition, a local corpus (e.g., PGH surgical database) can also be made available to the R scripts in the workshop. Once ethics approvals are obtained, organizations such as the PGH Department of Surgery can be open to partners (e.g., IT/CS/engineering, etc.) to help understand the data.


PARALLEL WORKSHOPS

Workshop 5

Edge Clinical - Preparing a Platform for Big Data in Health Research The EDGE platform allows clinical trial researchers to input all of their data in real time and to conduct their analysis also in real time. It allows everyone who’s interested in the particular clinical trial to track results. At the end of the workshop, delegates have signified their intent to sign up for the EDGE platform. A regional EDGE network for clinical trials in the Asia Pacific will be in the works to create an inter-country peer learning network.

Prof. James Batchelor Facilitator

Raymond Sarmiento, MD Rapporteur

Workshop 6

Innovation and Entrepreneurship James Weis shared how they developed entrepreneurship capabilities among students in MIT and how they were able to convert the output into actual spin-off and start-ups from students and faculties alike. In the workshop, participants also identified bottlenecks in innovation and entrepreneurship. They have a good technology transfer office at MIT but they have found ways to optimize their resources. To add, Luis Sison shared pitches about local products and technologies coming out from the pipeline. James Weis Facilitator

Prof. Luis Sison Rapporteur

One of the main takeaway points is the MIT experience, where the university supported the student network in leading innovation. Particularly, the MIT Biotech Group was able to start a lot of innovation by sending a newsletter on a bi-weekly basis to relevant stakeholders. The biotech environment in the USA is fast-paced and students are very attractive in the industry. In the supply and the demand side, there was a good meeting of minds; the industry is looking for students to invest in or to hire. Sample projects were also shared in the workshop. The lesson is to create a nurturing environment for students and faculty to work with each other. For the consortium of medical devices, it is important to note that the process they underwent to produce devices started with problems themselves, where clinicians presented problems to engineers and not the other way around. 28


PARALLEL WORKSHOPS

Workshop 7

Machine Learning Techniques for Big Data and Extracting Actionable Knowledge The data used in the workshop consist of patient’s measurements in ICU. Tools include Python and Scikit-learn, while the techniques used are gradient boosting, logistic regression, and tree classifier. In this case, the identified actionable knowledge is the death of the patient. The important part is to turn the data into information. Using a Jupyter notebook, the mean test score must interpret how likely the patient is going to pass away in the ICU.

Alistair Johnson Facilitator

In this workshop, the importance of balancing the ratio in machine learning is emphasized. Opportunities that may be tapped are open-source community (MIMIC Critical Care Database) and academia-industry partnership. Alvin Ray O. Yu Rapporteur

Workshop 8

Datathons to Support Cross-Disciplinary Education and Research

Tom Pollard Facilitator

In this interactive workshop, participants explored how open data enables “datathons”. These are events that bring together teams of researchers to work together on unanswered clinical questions. They started the workshop by outlining the datathon model and describing their experiences in holding these events internationally. They then gave participants an opportunity to participate in a mini-datathon, working together to analyze highly detailed information collected from patients admitted to critical care units at a large tertiary care hospital. Participants had an opportunity to learn about open science in clinical research, as well as gaining experience in analyzing MIMIC-III, a freelyavailable critical care dataset collected from over >50,000 hospital stays.

Medical project ideas presented in the workshop are: 1) Mimicking MIMIC and its Utility for Health Research; 2) Predicting HIV Prevalence in Cebu using Social Media (Twitter, Facebook); 3) Crowdsourcing Model for Data Prof. Mary Gretchen Chaves Gathering of the Dengue Fever; and 4) Bloodbank. Rapporteur

One of the opportunities identified in the workshop is the access to a Philippine version of MIMIC which focuses on the creation, data analysis, and use. Pursuing projects presented in the workshop using a datathon model is highly encouraged. The importance of participating in health data collection, data sharing, and analyses is also highlighted. 29


Question & Answer

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QUESTION & ANSWER

Question 1: I’m into public health and one of the concerns I have is privacy. I’d like to be appraised on how MIT was able to de-identify data available for researchers. Alistair Johnson: One of the key aspects of us being able to release this data is that only 10 percent has all evidence –the rest is clinical institution. We got support from the hospitals to release this data. We were able to waive informed consent because this is retrospective data that will not cause harm to patients. We put our heads down, put out some algorithms, and deidentified the patients. If you have de-identified the data, you can release the data freely. There is data use agreement. Julietta Gabiola: In our project with Manila Doctors, patients signed an informed consent processed by Stanford.

Question 2: Outside the clinical care setting, how are machine learning and big data leveraged? In machine learning, it takes time to process the data. What are the specific cases in decision making? Alistair Johnson: Like in all fields, we do a lot of pre-processing. As a use case, essentially, just after his (the doctor’s) residency, he walked in a woman who has sepsis and is decompensating. He’s the most senior in the room and he had to make a decision on how to treat the woman. Lots of measures about her heart rate is needed. There’s a constraint on the amount of human resources you can provide. The use of machine learning is that it can summarize the patient’s health, and predict how a patient would respond to a certain treatment.

Question 3: In terms of lifestyle, how do we use big data modification in the public? Stephanie Ko: A lot of the modeling techniques done so far use the ICU data. For example, preventing chronic disease in the first place would also be a good question. If there is a way to extract data from their daily lives, dietary habits, etc., then it must be given to doctors to better identify and advise on their daily routine.

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QUESTION & ANSWER

Question 4: When it comes to using of sensitive information, should we opt in or opt out? Should there be a physical information consent on how the data will be used? Dr. Raymond Sarmiento: PGH created an opt-in form for uses related to the medical record. The usual protocol is a waiver of the consent form. For the use of sensitive information, draft and create a separate opt-in form. There is a need to address the use of specific data fields at this point.

Question 5: What would you recommend for the next steps in complying with the data privacy act? Or is there a need to hire a third party auditor? Dr. Raymond Sarmiento: Follow their 30-60-day plan. First is identify a data protection officer for your unit. Qualifications are on the NDPC website. Yes, there’s a need for a third party-auditor. Another thing is if your company developed a substantial or at least one system developed outside of your unit agency or entity as part of the IPRPA, have all the third part vendors answer a form similar to the google visa. We have tried to adopt that as part of the security council joint agreement.

Question 6: What is the impact of giving patients the right to privacy? Does the breach cover health data? Dr. Raymond Sarmiento: There’s no explicit clause in the IRR itself that deals with that. We’ve actually asked that question with the commissioners. At the end of the day, the patients or participants in the study will have the final say on how would they want their information to be handled, either recorded or disposed of. Dr. Portia Marcelo: As health professionals, we need to work together. A lot of poor communities are also challenged by social disadvantages. While the law is not explicit, we need to localize and engage our patients in understanding these things. Filipinos are not very private people. Engage them to understand the concept of privacy better - it has to be in our local context. This is a call for public health workers to let patients know their rights. Dr. Julietta Gabiola: With our experience in mobile clinics, it’s really hard to do this western form of privacy and connect it to what we know in the Philippines. We need to be careful about connecting this to the local context.

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Annexes

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Prepared by: Dr. Alvin Marcelo Kristin Chloe Pascual Layout by: Joseph Manalo (Cover) Charisse Orjalo (Inside Pages)

Proceedings - BD4H 2017 Cebu, Philippines  
Proceedings - BD4H 2017 Cebu, Philippines  
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