Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.
Objective: Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this tim...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 26; no. 12; pp. 1493 - 1505 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2019
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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=139822566&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139822566 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Dec2019 vid: 26 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 139822566 139822566 NLM31532490 139822566 10.1093/jamia/ocz140 NLM31532490 139822566 ppf: 1493 ppct: 12 formats: tig: atl: Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions. aug: au: Park, Jihyun Kotzias, Dimitrios Kuo, Patty IV, Robert L Logan Merced, Kritzia Singh, Sameer Tanana, Michael Taniskidou, Efi Karra Lafata, Jennifer Elston Atkins, David C Tai-Seale, Ming Imel, Zac E Smyth, Padhraic Logan Iv, Robert L Karra Taniskidou, Efi affil: Department of Computer Science, University of California, Irvine, Irvine, California, USA sug: subj: Natural Language Processing Physician-Patient Relations Communication Data Collection Medical Records Human Middle Age Primary Health Care Office Visits Audiorecording Aged Validation Studies Comparative Studies Evaluation Research Multicenter Studies Ferrans and Powers Quality of Life Index Scales Funding Source Middle Aged: 45-64 years Aged: 65+ years ab: Objective: Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this time is allocated has implications for patient satisfaction, payments, and quality of care. We investigate the effectiveness of machine learning methods for automated annotation of medical topics in patient-provider dialog transcripts.Materials and Methods: We used dialog transcripts from 279 primary care visits to predict talk-turn topic labels. Different machine learning models were trained to operate on single or multiple local talk-turns (logistic classifiers, support vector machines, gated recurrent units) as well as sequential models that integrate information across talk-turn sequences (conditional random fields, hidden Markov models, and hierarchical gated recurrent units).Results: Evaluation was performed using cross-validation to measure 1) classification accuracy for talk-turns and 2) precision, recall, and F1 scores at the visit level. Experimental results showed that sequential models had higher classification accuracy at the talk-turn level and higher precision at the visit level. Independent models had higher recall scores at the visit level compared with sequential models.Conclusions: Incorporating sequential information across talk-turns improves the accuracy of topic prediction in patient-provider dialog by smoothing out noisy information from talk-turns. Although the results are promising, more advanced prediction techniques and larger labeled datasets will likely be required to achieve prediction performance appropriate for real-world clinical applications. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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