AUTOMATED CLINICAL NOTES ANNOTATION — IMPROVING EHR MANAGEMENT AND CLINICAL DECISION MAKING
Artificial Intelligence (AI)-based Approaches to Clinical Text Mining Dr. Sophie Laurenson (BSc Hons., Ph.D. Cantab.) Human-driven codification and annotation of clinical text is time- and costintensive. AI-based approaches using Natural Language Processing (NPL) and Machine Learning (ML) can aid in entity identification and codification.
Introduction The information stored in electronic health records (EHRs) is often recorded as clinical narratives in free text format. This data is essentially unstructured, making the extraction and analysis of useful data features difficult. To access the wealth of clinical data stored in EHRs, order and structure must be imposed on free text data. This can be achieved by codification, applying rules and algorithms to unstructured information. Such processes can be implemented manually using human-driven codification systems based on established standards and rules-based guidance systems. Alternatively, Artificial Intelligence (AI)based approaches leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques can convert clinical documents into data elements that can be identified and analysed31.
AI-Based Approaches to Clinical Text Mining Some aspects of digital health information are recorded in structured formats. Even records that included unstructured free text fields can retain an overall structured format which aids in data extraction and analysis. Indeed, many computerized provider order entry (CPOE) systems use controlled vocabularies to avoid unstructured narrative. However, in instances where an obvious structure cannot be superimposed by annotation, AI-based approaches can be employed to detect and extract key terms from narrative data. NLP techniques are derived from computer science and computational linguistics disciplines. They include processing tasks such as named entity recognition, tokenisation and character gazetteer. Advanced NLP systems are built on the basis of word or phrase recognition mapping
to medical terms that represent domain concepts as well as understanding the relationships between concepts. Modern NLP methods employ a combination of rule-based and supervised machine learning approaches. Once developed, NLP techniques have the advantage of scalability and may be adapted and applied to a range of datasets.
Text Mining in EHR Analytics Narrative or free text data represent a large fraction of the patient data contained within an EHR. Examples of free text data include physicians’ notes describing physical examination, symptoms and medical interventions. This unstructured data poses a challenge for extraction by automated computer processing. Several frameworks have been developed to facilitate clinical language processing and link data to scientific and medical knowledge bases. Examples include the National Library of Medicine’s Unified Medical Language System (UMLS)32, General Architecture for Text Engineering (GATE)33, Unstructured Information Management applications (UIMA)34 and provided by the Open Health Natural Language Processing (OHNLP) Consortium35. Recently, several NLP techniques have been developed to facilitate information extraction from the free text in EHRs. Applications have included diagnostic classification, identifying patient cohorts, identifying co-morbidities and postoperative complications, reporting of notifiable diseases, syndrome surveillance, medication event extraction, adverse event detection and disease management24. NPL has been used in a number of proof-ofconcept studies to extract and analyse data from EHRs. Examples have included using NLP to extract cancer staging data36, formulating oncology treatment summaries37, automation WWW.HOSPITALREPORTS.EU | 11
