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SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

Research output: Contribution to journalArticlepeer-review

Honghan Wu, Giulia Toti, Katherine I Morley, Zina Ibrahim, Amos Folarin, Richard Jackson, Ismail Kartoglu, Asha Agrawal, Clive Stringer, Darren Gale, Genevieve Gorrell, Angus Roberts, Matthew Broadbent, Robert Stewart, Richard JB Dobson

Original languageEnglish
Pages (from-to)530-537
JournalJournal of the American Medical Informatics Association : JAMIA
Volume25
Issue number5
Early online date19 Jan 2018
DOIs
Accepted/In press19 Jan 2018
E-pub ahead of print19 Jan 2018
PublishedMay 2018

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King's Authors

Abstract

Objective: Unlocking the data contained within both structured and unstructured components of Electronic Health Records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management and trial recruitment. To achieve this, we implemented SemEHR - a semantic search and analytics, open source tool for EHRs.

Methods: SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts within EHRs. Natural Language Processing (NLP) annotations are further assembled at patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data is serviced via ontology-based search and analytics interfaces.

Results: SemEHR has been deployed to a number of UK hospitals including the Clinical Record Interactive Search (CRIS), an anonymised replica of the EHR of the UK South London and Maudsley (SLaM) NHS Foundation Trust, one of Europes largest providers of mental health services. In two CRIS-based studies, SemEHR achieved 93% (Hepatitis C case) and 99% (HIV case) F-Measure results in identifying true positive patients. At King’s College Hospital in London, as part of the CogStack programme (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100k Genome Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast in searching phenotypes - time for recruitment criteria checking reduced from days to minutes. Validated on an open intensive care EHR data - MIMIC-III, the vital signs extracted by SemEHR can achieve around 97% accuracy.

Conclusion: Results from the multiple case studies demonstrate SemEHR’s efficiency - weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of a patient, bringing in more and unexpected insight compared to study-oriented bespoke information extraction systems.

SemEHR is open source available at https://github.com/CogStack/SemEHR.

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