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dc.contributor.advisorHammond, Tracy A
dc.contributor.advisorChoe, Yoonsuck
dc.creatorPolsley, Seth C
dc.date.accessioned2018-02-05T21:22:40Z
dc.date.available2019-08-01T06:51:39Z
dc.date.created2017-08
dc.date.issued2017-08-08
dc.date.submittedAugust 2017
dc.identifier.urihttp://hdl.handle.net/1969.1/166089
dc.description.abstractBetween appointments, healthcare providers have limited interaction with their patients, but patients have similar patterns of care. Medications have common side effects; injuries have an expected healing time; and so on. By modeling patient interventions with outcomes, healthcare systems can equip providers with better feedback. In this work, we present a pipeline for analyzing medical records according to an ontology directed at allowing closed-loop feedback between medical encounters. Working with medical data from multiple domains, we use a combination of data processing, machine learning, and clinical expertise to extract knowledge from patient records. While our current focus is on technique, the ultimate goal of this research is to inform development of a system using these models to provide knowledge-driven clinical decision-making.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectbiomedical informaticsen
dc.subjectontologyen
dc.subjectnatural language processingen
dc.subjectmachine learningen
dc.subjectclusteringen
dc.subjectoutcomes of careen
dc.subjectredactionen
dc.subjectepisodes of careen
dc.subjectcorpus annotationen
dc.titleIdentifying Outcomes of Care from Medical Records to Improve Doctor-Patient Communicationen
dc.typeThesisen
thesis.degree.departmentComputer Science and Engineeringen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberOry, Marcia
dc.type.materialtexten
dc.date.updated2018-02-05T21:22:40Z
local.embargo.terms2019-08-01
local.etdauthor.orcid0000-0002-8805-8375


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