When my patient is not my patient : inferring primary-care relationships using machine learning
Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2004.
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| Format: | Thesis |
| Language: | en_US |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/28587 |
| _version_ | 1826188400068132864 |
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| author | Lasko, Thomas A. (Thomas Anton), 1965- |
| author2 | Henry C. Chueh and G. Octo Barnett. |
| author_facet | Henry C. Chueh and G. Octo Barnett. Lasko, Thomas A. (Thomas Anton), 1965- |
| author_sort | Lasko, Thomas A. (Thomas Anton), 1965- |
| collection | MIT |
| description | Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2004. |
| first_indexed | 2024-09-23T07:59:03Z |
| format | Thesis |
| id | mit-1721.1/28587 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:59:03Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/285872022-01-13T07:53:45Z When my patient is not my patient : inferring primary-care relationships using machine learning Lasko, Thomas A. (Thomas Anton), 1965- Henry C. Chueh and G. Octo Barnett. Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology Harvard University--MIT Division of Health Sciences and Technology. Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2004. Includes bibliographical references (p. 37-39). 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 (cont.) Generalization on our dataset, and find no increase in accuracy for the more complex meta-learners. by Thomas A. Lasko. S.M. 2005-09-27T17:10:48Z 2005-09-27T17:10:48Z 2004 2004 Thesis http://hdl.handle.net/1721.1/28587 57489996 en_US 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. http://dspace.mit.edu/handle/1721.1/7582 45 p. 2778670 bytes 2781919 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Harvard University--MIT Division of Health Sciences and Technology. Lasko, Thomas A. (Thomas Anton), 1965- When my patient is not my patient : inferring primary-care relationships using machine learning |
| title | When my patient is not my patient : inferring primary-care relationships using machine learning |
| title_full | When my patient is not my patient : inferring primary-care relationships using machine learning |
| title_fullStr | When my patient is not my patient : inferring primary-care relationships using machine learning |
| title_full_unstemmed | When my patient is not my patient : inferring primary-care relationships using machine learning |
| title_short | When my patient is not my patient : inferring primary-care relationships using machine learning |
| title_sort | when my patient is not my patient inferring primary care relationships using machine learning |
| topic | Harvard University--MIT Division of Health Sciences and Technology. |
| url | http://hdl.handle.net/1721.1/28587 |
| work_keys_str_mv | AT laskothomasathomasanton1965 whenmypatientisnotmypatientinferringprimarycarerelationshipsusingmachinelearning |