Identifying Symptom Information in Clinical Notes Using Natural Language Processing (Record no. 85176)

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Language code of text/sound track or separate title eng
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Preferred name for the person Koleck, Theresa A.
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Title Identifying Symptom Information in Clinical Notes Using Natural Language Processing
264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Date of publication, distribution, etc 2021
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Summary, etc Background <br/><br/>Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes.<br/><br/>Objectives <br/><br/>We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations.<br/><br/>Methods <br/><br/>First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types.<br/><br/>Results <br/><br/>Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent.<br/><br/>Discussion <br/><br/>Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.<br/>
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Topical term or geographic name as entry element Computational linguistics
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Topical term or geographic name as entry element Computerized medical records
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Topical term or geographic name as entry element Constipation
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Topical term or geographic name as entry element Constipation - diagnosis
650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Depression - diagnosis
650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Electronic health records
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Topical term or geographic name as entry element Electronic Health Records - statistics & numerical data
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Topical term or geographic name as entry element Fatigue - diagnosis
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Topical term or geographic name as entry element Health records
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Topical term or geographic name as entry element Language processing
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Topical term or geographic name as entry element Medical records
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Topical term or geographic name as entry element Natural language interfaces
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Topical term or geographic name as entry element Pattern Recognition, Automated - methods
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Topical term or geographic name as entry element Professional identity
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Topical term or geographic name as entry element Sleep problems
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Topical term or geographic name as entry element Sleep Wake Disorders - diagnosis
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Topical term or geographic name as entry element Symptom Assessment - nursing
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Topical term or geographic name as entry element Symptoms
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Topical term or geographic name as entry element Tachycardia - diagnosis
650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Natural Language Processing
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Personal name Tatonetti, Nicholas P.
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Personal name Bakken, Suzanne
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Personal name Mitha, Shazia
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Personal name Henderson, Morgan M.
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Personal name George, Maureen
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Personal name Miaskowski, Christine
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Personal name Smaldone, Arlene
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Personal name Topaz, Maxim
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Title Nursing Research
Main entry heading May/June 2021 - Volume 70 - Number 3, pages 173-183
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Source of classification or shelving scheme
Item type JOURNAL ARTICLE
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Date last seen Price effective from Item type
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