Using Natural Language Processing and Machine Learning to Classify Health Literacy from Secure Messages : The ECLIPPSE Study / Renu Balyan, Scott A. Crossley and William Brown, III.

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of t...

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Bibliographic Details
Online Access:	Full Text (via ERIC)
Main Authors:	Balyan, Renu, Crossley, Scott A. (Author), Brown, William, III (Author), Karter, Andrew J. (Author), McNamara, Danielle S. (Author), Liu, Jennifer Y. (Author), Lyles, Courtney R. (Author), Schillinger, Dean (Author)
Format:	eBook
Language:	English
Published:	[Place of publication not identified] : Distributed by ERIC Clearinghouse, 2019.
Subjects:	Patients. Literacy. Health Services. Profiles. Barriers. Outcomes of Treatment. Measurement Techniques. Health. Classification. Natural Language Processing. Computational Linguistics. Artificial Intelligence. Computer Mediated Communication. Physician Patient Relationship. Comorbidity. Gender Differences. Racial Differences. Biochemistry. Correlation. Minority Groups. Drug Therapy. Diabetes. Predictor Variables.

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Full Text (via ERIC)

Using Natural Language Processing and Machine Learning to Classify Health Literacy from Secure Messages : The ECLIPPSE Study / Renu Balyan, Scott A. Crossley and William Brown, III.

Internet

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