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

Abstract

ObjectiveUnlocking 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.MethodsSemEHR 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.ResultsSemEHR 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.ConclusionResults 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.

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

“…al. [33]), many of which are made freely available and open source, have been intensively investigated in mining free-text medical records [10,[34][35][36]. To provide guidance in the efficient reuse of pre-trained NLP models, we have here proposed an approach that can automatically (i) identify easy cases in a new task for the reused model, on which it can achieve good performance with high confidence; (ii) classify the remainder of the cases so that the validation or retraining on them can be conducted much more efficiently, compared to adapting the model on all cases.…”

“…Automated approaches are essential to surface such deep data from free-text clinical notes at scale. To make NLP tools accessible for clinical applications, various approaches have been proposed, including generic, user-friendly tools [8][9][10] and web services or cloud based solutions [11][12][13]. Among these approaches, perhaps the most efficient way to facilitate clinical NLP projects is to adapt pre-trained NLP models in new but similar settings [14], i.e.…”

UK phenomics platform for developing and validating EHR phenotypes: CALIBER

“…in the UK cross-referencing against multiple EHR sources, prognostic validation and risk factor validation are all made possible by nationwide population-based records [28][29][30][31][32]. In contrast with the US, only recently have scalable methods been developed to access the entire hospital record for expert review [33] and text corpora are not available at scale [34]. There have been few previous studies [35] of the validity of International Classification of Disease and Health Related Problems, 10th Revision (ICD-10)…”

Evaluation of Patient-Level Retrieval from Electronic Health Record Data for a Cohort Discovery Task

“…Another thread of work has focused on making querying easier to carry out, typically through development of natural language or other structured interfaces to the patient data [22][23][24][25]. Other approaches focus on normalizing semantic representation of patient data within the EHR itself [26] and applying deep learning to non-topical characteristics of studies and researchers [27]. A related area to cohort discovery is patient phenotyping, one of the goals of which is to identify patients for clinical studies [28][29][30].…”