CogStack-SemEHR

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CogStack-SemEHR

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Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research

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# Archived

This repo is no longer maintained. For questions on this reop please email Honghan directly (honghan.wu@gmail.com), or for broader CogStack enquiries please reach out to contact@cogstack.org

# SemEHR
Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research
- [Tutorial: use dockerised SemEHR on (dockerised) Elasticsearch (an example on 20 discharge summaries)](https://semehr.cogstack.systems/tutorials.html) 
- [Dockerised SemEHR](https://github.com/CogStack/CogStack-SemEHR/tree/master/docker)
- [Documentation on running SemEHR](https://github.com/CogStack/SemEHR/wiki)
- [Documentation on API calls to SemEHR](https://github.com/CogStack/SemEHR/wiki/API-calls-to-SemEHR-index)
- [Minimised SemEHR + nlp2phenome bundle installation](https://github.com/CogStack/CogStack-SemEHR/tree/safehaven_mini/installation)

## updates
- (02 Dec 2020) Minimised SemEHR / nlp2phenome bundle for restricted safe haven env (no Docker) has been created: https://github.com/CogStack/CogStack-SemEHR/tree/safehaven_mini/installation
- (03 June 2019) A tutorial to apply SemEHR on 20 discharge summaries and deploy the system on an Elasticsearch Cluster: [Here](https://semehr.cogstack.systems/tutorials.html)
- (20 Dec 2018) Dead easy to run SemEHR NOW - Docker version of SemEHR released! Check [docker](https://github.com/CogStack/CogStack-SemEHR/tree/master/docker).
- (26 Feb 2018) An actionable transparency model has been implemented to derive confidence/accuracy value for [each annotation on a cohort basis](https://github.com/CogStack/SemEHR/blob/master/cohortanalysis.py#L334). Such value is based on the syntactic/semantic/contextual characteristics of the containing sentence/document of annotations. (a working paper about this technique will be shared soon.)
- (9 Feb 2018) [Patient Phenome UI](https://github.com/CogStack/SemEHR/tree/master/UI/patient_phenome_ui) implemented - to support 100k Genomics England (GeL) phenome model population for patients recruited for rare disease studies. ![HPO Phenome Model](https://raw.githubusercontent.com/CogStack/SemEHR/master/resources/HPO_Phenome_Model_sample.png "HPO Phenome Model")
- (22 Dec 2017)  An application paper describing SemEHR has been accepted by [JAMIA](https://academic.oup.com/jamia), titled “SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research”.
- (17 Nov 2017) Documentations for running SemEHR pipeline and API access have been put in the wiki: https://github.com/CogStack/SemEHR/wiki.
- (14 Sept 2017) An abstract describing SemEHR toolkit has been accepted to present at [UK Publich Health Science Conference 2017](http://www.ukpublichealthscience.org/) and to be published by [The Lancet](http://www.thelancet.com/).
- (24 Apr 2017) An SemEHR instance has been deployed on [MIMICIII data](https://mimic.physionet.org/) on 3 VMs of KCL's Rosalind HPC cluster to facilitate researches on this open ICU EHRs.
- (19 Apr 2017) The instance of SemEHR on [SLaM's CRIS data](http://www.slam.nhs.uk/research/cris) is supportingt the discovery of associations between liver diseases and addictions. 
- (21 Mar 2017) An SemEHR instance has been deployed at [King's College Hospital](https://www.kch.nhs.uk/) to support patient recruitments for rare diseases in [Genomics England's 100,000 Genome Project](https://www.genomicsengland.co.uk/the-100000-genomes-project/).
- (12 Oct 2016) SemEHR is initated as an effort to make use of EU KConnect results for supporting researches on anonymised EHR data of [South London and Maudsley Hospital](http://www.slam.nhs.uk/).

## Intro
Built upon off-the-shelf toolkits including a Natural Language Processing (NLP) pipeline ([Bio-Yodie](https://gate.ac.uk/applications/bio-yodie.html])) and an enterprise search system ([CogStack](https://github.com/CogStack/CogStack)), SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts from unstructured clinical notes. Its IE functionality features an adaptive and iterative NLP mechanism where specific requirements and fine-tuning can be fulfilled and realised on a study basis. NLP annotations are further assembled at patient level and extended with clinical and EHR-specific knowledge to populate a panorama for each patient, which comprises a) longitudinal semantic data views and b) structured medical profile(s). The semantic data is serviced via ontology-based search and analytics interfaces to facilitate clinical studies.

![System Achitecture](https://raw.githubusercontent.com/CogStack/SemEHR/master/resources/SystemArch.png "System Achitecture")

With SemEHR, the clinical variables hidden in clinical notes are surfaced via a set of search interfaces. A typical process to answer a clinical question (e.g. patients with hepatitis c), which previously might involve NLP, turns into one or a few google-style searches, for which SemEHR will pull out the cohort of relevant patients, populate patient-level summaries - numbers of contextualised concept mentions (e.g. 2nd patient has 16 total mentions of the disease, 15 of them were positive and 1 was historical), and link each mention to its original clinical note.

## Publications
SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research. Honghan Wu, Giulia Toti, Katherine I Morley, Zina Ibrahim, Amos Folarin, Ismail Kartoglu, Richard Jackson, Asha Agrawal, Clive Stringer, Darren Gale, Genevieve M Gorrell, Angus Roberts, Matthew Broadbent, Robert Stewart, Richard J B Dobson. The Lancet , Volume 390 , S97. [10.1016/S0140-6736(17)33032-5](http://dx.doi.org/10.1016/S0140-6736(17)33032-5)

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research. Honghan Wu, Giulia Toti, Katherine I Morley, Zina Ibrahim, Amos Folarin, Ismail Kartoglu, Richard Jackson, Asha Agrawal, Clive Stringer, Darren Gale, Genevieve M Gorrell, Angus Roberts, Matthew Broadbent, Robert Stewart, Richard J B Dobson.Journal of the American Medical Informatics Association, 2017. [10.1093/jamia/ocx160](http://dx.doi.org/10.1093/jamia/ocx160)

## Questions?
Email Honghan Wu (honghan.wu@gmail.com)

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