Why AI Is a Practical Solution for Pharma

8/27/2019

Why AI Is a Practical Solution for Pharma - usm systems - Medium

Why AI Is a Practical Solution for Pharma usm systems Aug 27 ¡ 6 min read

Artificial Intelligence, or AI, is gaining more attention in the pharma space these days. At a time when images were inspired by futuristic science fiction, it now plays an important role in smartphones, cars, search engines, and financial transactions. But, what is artificial intelligence? It is a set of very specialized algorithms that are trained to solve very specific problems. These algorithms allow applications on our phones to perform tasks such as answering our spoken questions or giving turn-by-turn driving directions to a destination. AI is constantly evolving as its applications expand into widespread use.

How can we use it to improve the pharma business? Can it really make a difference? The answer is yes. In fact, AI is key to the future success of the industry and the biotech industry. To understand how valuable this is to pharma, it is important to understand what kind of problems it can solve, what parts of AI are practically useful to pharma, and how it can change drug discovery and development.

Let’s start by examining how we get reliable clinical results. Current approaches to developmental development rely solely on identifying biomarkers, molecular targets or appropriate drugs in healthy and diseased patients, which often fail to deliver biologically useful results. Although these results are statistically significant, they are not the only guarantee of success. Significant differences can help ICT determine which https://medium.com/@usmsystems23/why-ai-is-a-practical-solution-for-pharma-879b76be9267

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patients have a gene, receptor, or protein that is misregulated, but most differences are not associated with the disease, so those results are considered false-positive. However, statistically significant results validated with previously established knowledge or data can lead to success.

Although we use machines to great advantage in the early stages of development, machines can only generate data and do not know how to verify that data. This means that to be successful, we must rely on human validation, human intervention, and human contextualization. When we get validated results, we publish information for the use of others, not for machines, which means that most of the information published in the world is structured storytelling. However, machines cannot interpret information in story format. They can interpret the text and sometimes tables, but they need structured information with clearly defined entities and relationships to understand the data.

If we want to tackle this problem, we have a fundamental challenge: Do we change people through the standardization of data reporting, or do we need to change machines to work the way research scientists and data analysts do? If we change people, we must force them to conform to the way a machine works. Who wants to take up so little of the company, the whole industry? If we can get the machines to see and work the way we do it is very easy and requires very little time and financial cost. With information architecture, in particular, we need machines to adapt and process the way we view information.

Managing data The main challenge in data analytics is managing data. Because most data are in structured formats, analytics professionals are able to understand the computer by spending 80% of their time collecting, cleaning and managing it. This lack of consistency does not change with the way information is processed. We can make efforts to bring organizations together to bring about the standard method of data presentation, but the reality is that the lack of uniformity in the way data is presented remains unchanged.

Even in a pharma company, where multiple software programs are often in use, analytics professionals need to devote more time to cleaning and managing data so that comparisons can be made.

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8/27/2019

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Adding to the complexity of this problem is the fact that industry change is moving relatively slowly and the volume of data is constantly increasing. In 2020, the amount of medical data is expected to double every 73 days. Any static ontology created to handle this data is old before it can be used. Question: How will pharma data drive growth if ontologies cannot sustain it? Lack of data is not a problem, but the use of data involves.

To understand how AI can solve this and other problems, we need to understand its components. In pharma, practical applications of AI require four key elements that work together to make use of data by machines: computer vision, data extraction, life sciences language processing, and entity clutter.

Computer vision With a computer view, you can pull information from tables, graphs, photos, text, and so on. Unlike standard optical character recognition From a computer view, information can be pulled from tables, graphs, photos, text, etc.

Unlike standard optical character recognition software that captures only words, computer vision can collect information in the context of the text. For example, InnoPlexus has developed computer vision technologies that can detect segments of text, whether from an abstract, introduction, results, or discussion section. This is valuable because it allows our AI algorithms to rank the strength of relationships found in an article. The relationship in the results or discussion section is more novel than the one mentioned in the introduction.

Information extraction Computer vision allows the computer to extract information from a variety of files, such as pdf, powerpoint files, excel document or web pages. It is important to have the ability to detect and collect information from a variety of files.

Data-recovery Life Sciences Language Processing Although natural language processing (NLP) is the core of AI, it is not useful for the life sciences industry. InnoPlexus has built an extensive Life Sciences ontology into the framework of the NLP and the result is a Life Science Language Processing System.

Ontology is like a dictionary, which includes not only the definition of a word or concept https://medium.com/@usmsystems23/why-ai-is-a-practical-solution-for-pharma-879b76be9267

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but also the relationships associated with the word or concept. It is not a table or a thesaurus, it is a set of concepts and word combinations. It is important to have a useful ontology that allows machines to understand human inputs and translate them into the life sciences context. Furthermore, it helps with concept-based searches instead of keyword searches.

With NLP alone, machines cannot differentiate between similar terms like EGFR and EGFR. A data set containing irrelevant entities is useless to the life sciences industry.

Great computing power or a well-designed algorithm will not serve any purpose if you do not understand the data. This has already happened in the industry. There are times when major cancer research centers with great computing power and best algorithms are unable to understand the data they have created because they do not have language processing that can be effective in the life sciences field.

At Innoplex, we understand the data challenge. In order to deliver the life sciences effectively, we have realized that we need to build an AI machine that can constantly learn from the growing information that the industry is producing. Since 2011, we have been crawling through 97% of publicly available data on the web. Today, we have the largest life-sciences research ontology in the world. The AI technology behind this is auto-scaling and It is constantly growing. Having this ontology means that computers are close enough that researchers using AI will not have to change their work and change their data and their terminology to meet someone else’s standards. It’s hard to ask people to change when they are asked to change everything they do. With Life Sciences Ontology, this is not necessary.

Entity Clutter Entity ambiguity spells out the meaning between two or more different words or concepts. For example, searching for “EGFR” on two different major search platforms gives us two different results: one gives us an “eGFR,” an estimated glomerular filtration rate, and the other gives us an “EGFR,” epithelial growth factor receptor. The lack of both search platforms is a system where the user can select their subject’s interest.

The purpose of AI for Pharma

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The last important thing about AI: it’s not magic. It does not solve the problems that humans cannot solve if they have unlimited time. What AI can do for pharma is to enable data scientists to spend less time on less cognitive tasks — manual searches and data validation — and make them available for more valuable, more cognitive tasks. These tasks can detect, manage, and analyze large amounts of information in less time than a human can take. In fact, AI machines can be achieved by humans in a matter of months or more in a matter of days.

As daily life sciences data grows, pharma companies need to keep up with AI so that they can stay on top of new research findings and innovations. In a short span of time, the AI image can be rendered from more compelling sources than manually possible, and the human analyst can find what is missing. While AI reduces the need for manual labor procurement, curation, and analysis, it simultaneously opens the door to further innovation and new opportunities for those involved in the research

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