Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.

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ID: 46857
2019
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Ranked #7 of 76 articles by views in Journal of the American Medical Informatics Association : JAMIA

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Abstract
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.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).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.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.
Reference Key
park2019detectingjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Park, Jihyun;Kotzias, Dimitrios;Kuo, Patty;Logan Iv, Robert L;Merced, Kritzia;Singh, Sameer;Tanana, Michael;Karra Taniskidou, Efi;Lafata, Jennifer Elston;Atkins, David C;Tai-Seale, Ming;Imel, Zac E;Smyth, Padhraic;
Journal Journal of the American Medical Informatics Association : JAMIA
Year 2019
DOI
ocz140
URL
Keywords
communication classification supervised machine learning patient care

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