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Terry Knight and Randall Davis. Terry Knight and Randall Davis. Berry, Jaclyn(Jaclyn Elizabeth Hom) Massachusetts Institute of Technology. Department of Architecture. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Architecture Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-01-23T16:57:50Z 2020-01-23T16:57:50Z 2019 2019 https://hdl.handle.net/1721.1/123578 1135802053 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: S.M., Massachusetts Institute of Technology, Department of Architecture, 2019 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 131-136). Medical patients face significant challenges for managing their health information. In particular, cancer patients have a uniquely difficult experience where they must endure the physical and emotional effects of their illness while simultaneously navigating overwhelming amounts of medical information. In this thesis, I focus on the challenge of capturing, reviewing and extracting information from medical appointments for patients enduring serious health conditions such as cancer. First, I propose a novel multimodal-interface to help patients review and understand information they received from conversations with their doctors. This interface captures medical conversations as text and audio, with important positive and negative information highlighted. I conducted 25 user studies where I enacted fictional conversations between a doctor and a patient to evaluate whether this method of representing information would help patients review and understand their appointments. Results from the user studies show that the web interface serves as a useful tool for reviewing the content of the conversations, however its effect on patient understanding cannot yet be determined. Second, I propose a machine learning algorithm to automatically classify the positive and negative information in medical conversations based on analysis of the text and prosody in speech. The model with the highest performance on my dataset achieved an accuracy of 90.6% and Fl-score of 0.888. While I focus on challenges within the medical field, findings from this thesis may be relevant to emotional conversations in any setting such as sportscasting, political debates and more. by Jaclyn Berry. S.M. S.M. S.M. Massachusetts Institute of Technology, Department of Architecture S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 136 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Architecture. Electrical Engineering and Computer Science. The good, the bad, and the facts : multimodal representation of medical conversations for patient understanding Multimodal representation of medical conversations for patient understanding Thesis application/pdf 2020-03-09T19:58:21Z Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="53178c10-5f41-4a04-a0c0-5b3186252c2c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>The good, the bad, and the facts : multimodal representation of medical conversations for patient understanding</Title> <Subtitle>Multimodal representation of medical conversations for patient understanding</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Berry, Jaclyn(Jaclyn Elizabeth Hom)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Architecture.</Keyword> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Medical patients face significant challenges for managing their health information. In particular, cancer patients have a uniquely difficult experience where they must endure the physical and emotional effects of their illness while simultaneously navigating overwhelming amounts of medical information. In this thesis, I focus on the challenge of capturing, reviewing and extracting information from medical appointments for patients enduring serious health conditions such as cancer. First, I propose a novel multimodal-interface to help patients review and understand information they received from conversations with their doctors. This interface captures medical conversations as text and audio, with important positive and negative information highlighted. I conducted 25 user studies where I enacted fictional conversations between a doctor and a patient to evaluate whether this method of representing information would help patients review and understand their appointments. Results from the user studies show that the web interface serves as a useful tool for reviewing the content of the conversations, however its effect on patient understanding cannot yet be determined. Second, I propose a machine learning algorithm to automatically classify the positive and negative information in medical conversations based on analysis of the text and prosody in speech. The model with the highest performance on my dataset achieved an accuracy of 90.6% and Fl-score of 0.888. While I focus on challenges within the medical field, findings from this thesis may be relevant to emotional conversations in any setting such as sportscasting, political debates and more.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>