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. 2018 May 1;25(5):530-537.
doi: 10.1093/jamia/ocx160.

SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research

Honghan Wu  1   2 , Giulia Toti  3 , Katherine I Morley  3   4 , Zina M Ibrahim  1   5 , Amos Folarin  1   5 , Richard Jackson  1 , Ismail Kartoglu  6 , Asha Agrawal  7 , Clive Stringer  7 , Darren Gale  7 , Genevieve Gorrell  8 , Angus Roberts  8 , Matthew Broadbent  9 , Robert Stewart  9   10 , Richard J B Dobson  1   5
Affiliations

SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research

Honghan Wu et al. J Am Med Inform Assoc. .

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, an open source semantic search and analytics tool for EHRs.

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

Results: SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care 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 patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR.

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Figures

Figure 1.
Figure 1.
(A) SemEHR data model: entities (patient, clinical note, concept, and concept mentions) and their associations. (B) SemEHR generates 2 longitudinal views for each patient: concept mentions grouped in typed and dated documents (upper part), and concept mentions grouped in structured (discharge) summaries (lower part).
Figure 2.
Figure 2.
The architecture of SemEHR is composed of 3 subsystems: (1) the producing subsystem (upper part of the figure), creation of SemEHR semantic index by harmonizing, natural language processing, and indexing EHR data; (2) the continuous learning subsystem, addressing study-specific requirements and supporting fine-tuning for separate studies; and (3) the consuming subsystem (lower part), supporting tailored care, patient recruitment, and clinical research by semantic searching and study-based continuous learning.
Figure 3.
Figure 3.
Screenshots of key functionalities provided by the consuming subsystem. (A) Identifying query concepts (UMLS CUIs): facilities to ensure the correct and complete concepts are used in the query to derive accurate clinical findings. (A1) Concept search for matching a user search term to one or more ontology (UMLS) concepts; logical reasoning is implemented to enable the automated inclusion of semantically related concepts (eg, hepatocellular damage is liver damage). (A2) Concept validation component for checking and approving the automated inferred concepts based on the aim and criteria of the clinical study (eg, only retain alcohol-related liver conditions for addiction analytics). (B) Selecting and summarizing cohort (the full text in the screenshot has been deliberately rewritten to avoid leaking sensitive patient data). A summary table is generated for a user query where each row summarizes the numbers of total mentions and contextualized mentions for one patient. (C) Patient timeline: longitudinal document view (upper), structured medical profile view (based on FHIR discharge summary format), and the view of latest vital signs and other measurements.
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