Deriving an actionable patient phenome from healthcare data

Lead Research Organisation: University of Edinburgh
Department Name: Centre of Population Health Sciences

Abstract

Translating routinely collected health data into knowledge is a requirement of a "learning health system". Since joining the Biomedical Research Centre at the South London and Maudsley Hospital, Kings College London, my research has been focused on developing 'CogStack and SemEHR'. This is an integrated health informatics platform which aims to to unlock unstructured health records and assist in clinical decision making and research. The system does much to surface the deep data within the NHS, for example through providing a patient-centric search on semantically annotated clinical notes to support studies such as the recruitment of patients for Genomics England's 100,000 Genomes project [1,2] and predicting adverse drug reactions [3].

However, there is considerable further potential for the generation of knowledge and action, for example through the application of machine learning to the data from this platform. For instance, the data returned through these systems needs to be integrated, verified and cleaned with biomedical knowledge, enriched with an accurate clinical context (to enhance the current sentence-level language context) and aligned with the patient timeline to derive a comprehensive patient phenome. Clinical knowledge needs to be formalised from clinical ontologies and integrated with relevant open data, which will drive automated inferences to lift lower-level features (e.g. numeric blood pressure readings) up to higher-level clinical variables (e.g. hypertension) for supporting decision making.

A pilot study of the comprehensive phenome model, SemEHR's medical profiles [2], evaluated on publicly accessible data from the Medical Information Mart for Intensive Care (MIMIC), has proven that better contextual information can lead to much better accuracy in making clinical conclusions - e.g. using patient medical history for subtyping atrial fibrillation where we demonstrated that such phenome data is within the top 10 key features in identifying clinically-sensible patient clusters. For 'action' generation in clinical settings, we have demonstrated the feasibility of alerts through a number of simple examples using CogStack. For example, at Kings College Hospital, we have detected abnormal pathology results for 25 patients being prescribed methotrexate for rheumatoid arthritis, preventing potentially fatal renal failure.

The proposed research will devise a semantic electronic health record toolkit that is able to derive a consistent and comprehensive patient phenome from unstructured and structured electronic health records and provide semantic computation upon it to support decision making for tailored care, trial recruitment and research.

References:
1. Wu H, et al. SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research. Lancet. 2017;390: S97.
2. Wu H, et al. A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research. Journal of the American Medical Informatics Association. 2017; doi: https://doi.org/10.1101/235622.
3. Bean DM, Wu H, et al. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Sci Rep. 2017;7: 16416.

Technical Summary

For objective 1, at the data layer, my research will focus on a semantic phenome model that is able to detect/correct erroneous and inconsistent phenotypes, associate accurate contextual and temporal information with each phenotype mention and also support rule based reasoning to complete missing data. For objective 2, I will be devising and applying artificial intelligence models to derive unknown clinical knowledge from large scale, longitudinal and interlinked phenome data. potential use cases include predicting outcomes of septic shock treatments within intensive care units; predicting unknown adverse drug reactions in depression patients with comorbidities; subtyping atrial fibrillation to deliver tailored care. For objective 3, my research will provide actionable suggestions in clinical settings with applications of clinical trial recruitment and automated alerting for ensuring patient safety. Key challenges to be tackled here include how to make action suggestions explainable and reliable.

This project aims to deliver enabling technologies for The University of Edinburgh's HDR UK focus including deriving and applying health-related phenotypes at scale; computational tools for genetic and environmental risk prediction and causal inference. It will develop national leadership, partnerships, and interdisciplinary skills and capacity through the development of semantic computation infrastructure on top of deep and accurate patient phenome data, which if successful, can be disseminated to a wide range of healthcare service providers nationally/internationally and achieve high impact in research and patient care.

Publications

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