ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
SemanticFind: Locating What You Want in a Patient Record, Not Just What You Ask For.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- quEHRy: a question answering system to query electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Reducing Physicians' Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.JAMA network open · 2020Article
- Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- A Semantic Parsing Method for Mapping Clinical Questions to Logical Forms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a new model of patient record search, called SemanticFind, which goes beyond traditional textual and medical synonym matches by locating patient data that a clinician would want to see rather than just what they ask for. The new model is implemented by making extensive use of the UMLS semantic network, distributional semantics, and NLP, to match query terms along several dimensions in a patient record with the returned matches organized accordingly. The new approach finds all clinically related concepts without the user having to ask for them. An evaluation of the accuracy of SemanticFind shows that it found twice as many relevant matches compared to those found by literal (traditional) search alone, along with very high precision and recall. These results suggest potential uses for SemanticFind in clinical practice, retrospective chart reviews, and in automated extraction of quality metrics.
Identifiers
28815139PMC5543371W2787792765What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.