2020
DOI: 10.1200/cci.19.00134
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Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research

Abstract: PURPOSE The Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics. METHODS We identified peer-reviewed publications that used EMERSE through online searches as well as … Show more

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Cited by 30 publications
(13 citation statements)
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“…In this work we used NLP algorithms to extract the method of tumor detection from free-text visit summaries. There is a growing body of literature in which computational approaches are applied for processing unstructured records to retrieve information and improve diagnosis performance [38,39], support treatment decisions [40], and improve cancer research by providing a better interface to existing knowledge platforms [41]. We believe that in our work, we have demonstrated the potential of integrating a computational approach for extracting information from oncological EMRs that outline the disease course of a patient, formatted in a completely unstructured way.…”
Section: Discussionmentioning
confidence: 91%
“…In this work we used NLP algorithms to extract the method of tumor detection from free-text visit summaries. There is a growing body of literature in which computational approaches are applied for processing unstructured records to retrieve information and improve diagnosis performance [38,39], support treatment decisions [40], and improve cancer research by providing a better interface to existing knowledge platforms [41]. We believe that in our work, we have demonstrated the potential of integrating a computational approach for extracting information from oncological EMRs that outline the disease course of a patient, formatted in a completely unstructured way.…”
Section: Discussionmentioning
confidence: 91%
“…Moreover, there are five important challenges related to concept recognition from clinical text, including negation, severity, abbreviation, ambiguity, and misspellings. These tasks are important for clinical research, and particularly for electronic phenotyping and cohort selection (Banda et al, 2018;Hanauer et al, 2020). Eligibility criteria in clinical cohort may include patients that: did not have an arrhythmia, were diagnosed with coronary artery disease, and are taking some statin.…”
Section: Discussionmentioning
confidence: 99%
“…We identified our patient cohort using the University of Michigan's Electronic Medical Record Search Engine (EMERSE). 15 This study was exempted from informed consent as determined by the University of Michigan IRB (identifier number: HUM00036763).…”
Section: Methodsmentioning
confidence: 99%