Developing a Classification Algorithm for Prediabetes Risk Detection From Home Care Nursing Notes: Using Natural Language Processing.
This study developed and validated a rule-based classification algorithm for prediabetes risk detection using natural language processing from home care nursing notes. First, we developed prediabetes-related symptomatic terms in English and Korean. Second, we used natural language processing to prep...
| Publicado en: | CIN: Computers, Informatics, Nursing Vol. 41; no. 7; pp. 539 - 548 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jul2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164818769&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164818769 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15382931 KXN jtl: CIN: Computers, Informatics, Nursing issn: 15382931 maglogo: N pubinfo: dt: Jul2023 vid: 41 iid: 7 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 164818769 164818769 164818769 10.1097/CIN.0000000000001000 164818769 ppf: 539 ppct: 9 formats: tig: atl: Developing a Classification Algorithm for Prediabetes Risk Detection From Home Care Nursing Notes: Using Natural Language Processing. aug: au: Jeon, Eunjoo Kim, Aeri Lee, Jisoo Heo, Hyunsook Lee, Hana Woo, Kyungmi affil: Author Affiliations: Technology Research, SamsungSDS (Dr Jeon) sug: subj: Classification Algorithms Prediabetic State Risk Factors Home Health Nurses Natural Language Processing Human Validity Precision Home Health Care Nursing Records Interrater Reliability Confidence Intervals ab: This study developed and validated a rule-based classification algorithm for prediabetes risk detection using natural language processing from home care nursing notes. First, we developed prediabetes-related symptomatic terms in English and Korean. Second, we used natural language processing to preprocess the notes. Third, we created a rule-based classification algorithm with 31 484 notes, excluding 315 instances of missing data. The final algorithm was validated by measuring accuracy, precision, recall, and the F1 score against a gold standard testing set (400 notes). The developed terms comprised 11 categories and 1639 words in Korean and 1181 words in English. Using the rule-based classification algorithm, 42.2% of the notes comprised one or more prediabetic symptoms. The algorithm achieved high performance when applied to the gold standard testing set. We proposed a rule-based natural language processing algorithm to optimize the classification of the prediabetes risk group, depending on whether the home care nursing notes contain prediabetes-related symptomatic terms. Tokenization based on white space and the rule-based algorithm were brought into effect to detect the prediabetes symptomatic terms. Applying this algorithm to electronic health records systems will increase the possibility of preventing diabetes onset through early detection of risk groups and provision of tailored intervention. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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