Dataset Information

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

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.

SUBMITTER: Wu H

PROVIDER: S-EPMC6019046 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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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.

Wu Honghan H Toti Giulia G Morley Katherine I KI Ibrahim Zina M ZM Folarin Amos A Jackson Richard R Kartoglu Ismail I Agrawal Asha A Stringer Clive C Gale Darren D Gorrell Genevieve G Roberts Angus A Broadbent Matthew M Stewart Robert R Dobson Richard J B RJB

Journal of the American Medical Informatics Association : JAMIA 20180501 5

<h4>Objective</h4>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.<h4>Methods</h4>SemEHR implements a generic information extraction (IE) and retrieval infra ...[more]

PMID: 29361077

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Project description:BackgroundThe increasingly widespread use of next generation sequencing protocols has brought the need for the development of user-friendly raw data processing tools. Here, we explore 2FAST2Q, a versatile and intuitive standalone program capable of extracting and counting feature occurrences in FASTQ files. Despite 2FAST2Q being previously described as part of a CRISPRi-seq analysis pipeline, in here we further elaborate on the program's functionality, and its broader applicability and functions.Methods2FAST2Q is built in Python, with published standalone executables in Windows MS, MacOS, and Linux. It has a familiar user interface, and uses an advanced custom sequence searching algorithm.ResultsUsing published CRISPRi datasets in which Escherichia coli and Mycobacterium tuberculosis gene essentiality, as well as host-cell sensitivity towards SARS-CoV2 infectivity were tested, we demonstrate that 2FAST2Q efficiently recapitulates published output in read counts per provided feature. We further show that 2FAST2Q can be used in any experimental setup that requires feature extraction from raw reads, being able to quickly handle Hamming distance based mismatch alignments, nucleotide wise Phred score filtering, custom read trimming, and sequence searching within a single program. Moreover, we exemplify how different FASTQ read filtering parameters impact downstream analysis, and suggest a default usage protocol. 2FAST2Q is easier to use and faster than currently available tools, efficiently processing not only CRISPRi-seq / random-barcode sequencing datasets on any up-to-date laptop, but also handling the advanced extraction of de novo features from FASTQ files. We expect that 2FAST2Q will not only be useful for people working in microbiology but also for other fields in which amplicon sequencing data is generated. 2FAST2Q is available as an executable file for all current operating systems without installation and as a Python3 module on the PyPI repository (available at https://veeninglab.com/2fast2q).

Dataset Information

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

Publications

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

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