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When my patient is not my patient : inferring primary-care relationships using machine learning

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dc.contributor.advisor Henry C. Chueh and G. Octo Barnett. en_US
dc.contributor.author Lasko, Thomas A. (Thomas Anton), 1965- en_US
dc.contributor.other Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.date.accessioned 2005-09-27T17:10:48Z
dc.date.available 2005-09-27T17:10:48Z
dc.date.copyright 2004 en_US
dc.date.issued 2004 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/28587
dc.description Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2004. en_US
dc.description Includes bibliographical references (p. 37-39). en_US
dc.description.abstract This paper demonstrates that one can infer with respectable accuracy a physician's view of the therapeutic relationship that he or she has with a given patient, using data available in the patient's electronic medical record. In this study, we differentiate between the active primary relationship, the inactive primary or non-attending relationship, and the coverage relationship. We demonstrate that a single model built using the Averaged One-Dependence Estimator (AODE) classifier and learned with six attributes taken from patient visit history and physician practice characteristics can, for most of the 18 physicians tested, differentiate patients with a coverage relationship to a given physician from those with a primary relationship, achieving accuracies of 0.90 or greater as determined by the area under the receiver operating characteristic curve. Three of the 18 datasets had too few coverage patients to adequately characterize. We also demonstrate that, surprisingly, physicians are generally of like mind when assessing the therapeutic relationship that they have with a given patient. We find that for all physicians in our sample, a model built individually with any one physician's assessments performs statistically identically to the model built from the assessments of all other physicians combined. As a sub-goal of this research, we test the performance of different attribute selection methods on our dataset, comparing greedy vs. randomized search and wrapper vs. filter evaluators and finding no practical difference between them for our data. We also test the performance of several different classifiers, with AODE emerging as the best choice for this dataset. Lastly, we test the performance of linear vs. non-linear meta-learners for Stacked en_US
dc.description.abstract (cont.) Generalization on our dataset, and find no increase in accuracy for the more complex meta-learners. en_US
dc.description.statementofresponsibility by Thomas A. Lasko. en_US
dc.format.extent 45 p. en_US
dc.format.extent 2778670 bytes
dc.format.extent 2781919 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.title When my patient is not my patient : inferring primary-care relationships using machine learning en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.identifier.oclc 57489996 en_US


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