2026-09-21T20:13:59Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/49672 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Gunter, Carl A. Lu, Xun 2014-05-30T17:04:03Z 2014-05-30T17:04:03Z 2016-09-22T20:59:22Z 2014-05 2014-05-30T17:04:03Z 2014-05 Medical specialties provide essential information about which providers have the skills needed to carry out key procedures or make critical judgments. They are useful for training and staffing and provide confidence to patients that their providers have the experience needed to address their problems. This work evaluates how machine learning classifiers can be trained on treatment histories to recognize medical specialties. Such classifiers can be used to evaluate staffing and workflows and have applications to safety and security. We focus on treatment histories that consist of the patient diagnoses. We find that some specialties, such as a urologist, can be learned with good precision and recall, while other specialties, such as anesthesiology, are less easily recognized. We call the former diagnosis specialties and explore four machine learning techniques for them, which we compare to a naive baseline based on the diagnoses most commonly treated by specialists in a training set. We find that these techniques can improve substantially on the baseline and that the best technique, which uses Latent Dirichlet Allocation (LDA), provides precision and recall above 80% for many diagnosis specialties based on a study with one year of chart accesses and discharge diagnoses from a major hospital. Furthermore, we explored several data mining techniques to discover valid but unlisted diagnosis specialties. We present the diagnosis specialty discoveries and their associated attributes that corroborate the discoveries. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-04-24T17:37:18Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 UIUC_MS_THESIS_TEX_LU_XUN.zip: 919958 bytes, checksum: 0afa7c6c62b0473e992858295eecf8fe (MD5) Lu_Xun.pdf: 914494 bytes, checksum: b5288c4b04fcfbb43506b2e82bfa391b (MD5) Made available in DSpace on 2014-05-30T17:04:03Z (GMT). No. of bitstreams: 3 Xun_Lu.pdf: 914494 bytes, checksum: b5288c4b04fcfbb43506b2e82bfa391b (MD5) UIUC_MS_THESIS_TEX_LU_XUN.zip: 919958 bytes, checksum: 0afa7c6c62b0473e992858295eecf8fe (MD5) license.txt: 4054 bytes, checksum: 60397f7b421b01fae25ee34ae752ae02 (MD5) Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Seth Robbins (robbins.sd@gmail.com) on 2014-05-30T17:09:28Z Item is restricted until 2016-05-30T17:09:03Z Restriction data tranferred 2014-07-01T11:38:24-05:00 Original Data Group with Access UIUC Users [automated] Release Date: 2016-05-30 12:09:03 UTC Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 49723 on 2016-09-22T20:59:22Z. http://hdl.handle.net/2142/49672 en Copyright 2014 Xun Lu Medical Informatics Security and Privacy Machine Learning Diagnosis based specialist identification in the hospital text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. MS:Computer Science -UIUC 10KS0112MS