{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105013"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105013","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Making augmented human intelligence in medicine practical: A case study of treating major depressive disorder","abstract":"Individualized medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on highest reported efficacy in clinical trials, which is often not effective for all individuals. In this dissertation, we assert that treatment selection and management can be individualized when clinicians assessment of disease symptoms are augmented with a few analytically identified patient-specific measures (e.g., genomics, metabolomics) that are prognostic or predictive of treatment outcomes. Patient-derived biological, clinical and symptom measures are sufficiently complex, i.e., heterogeneous, noisy and high-dimensional. The question for research then becomes: “Which few among these large complex measures are sufficient to augment the clinician’s disease assessment and treatment logic to individualize treatment decisions?” This dissertation introduces, ALMOND — Analytics and Machine Learning Framework for Actionable Intelligence from Clinical and Omics Data. As a case study, this dissertation describes how ALMOND addresses the unmet need for individualized medicine in treating major depressive disorder — the leading cause of medical disabilities worldwide. The biggest challenge in individualizing treatment of depression is in the heterogeneity of how depressive symptoms manifest between individuals, and in their varied response to the same treatment. ALMOND comprises a systematic analytical workflow to individualize antidepressant treatment by addressing the challenge of heterogeneity of major depressive disorder. First, “right patients” are identified by stratifying patients using unsupervised learning, that serves as a foundation to associate their disease states with multiple pharmacological (drug-associated) measures. Second, “right drug” selection is shown to be feasible by demonstrating that psychiatrists’ depression severity assessments augmented with pharmacogenomic measures can accurately predict remission of depressive symptoms using supervised learning. Finally, probabilistic graphs provide early and easily interpretable prognoses at the “right time” to a psychiatrist by accounting for changes in routinely assessed depressive symptoms’ severity. By choosing antidepressants that have the highest-likelihood of the patient achieving remission, the chances of persisting depressive symptoms are reduced, which is often the leading medical conditions in those who commit suicide or develop chronic illnesses.","abstract_html":"Individualized medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on highest reported efficacy in clinical trials, which is often not effective for all individuals. In this dissertation, we assert that treatment selection and management can be individualized when clinicians assessment of disease symptoms are augmented with a few analytically identified patient-specific measures (e.g., genomics, metabolomics) that are prognostic or predictive of treatment outcomes. Patient-derived biological, clinical and symptom measures are sufficiently complex, i.e., heterogeneous, noisy and high-dimensional. The question for research then becomes: “Which few among these large complex measures are sufficient to augment the clinician’s disease assessment and treatment logic to individualize treatment decisions?” This dissertation introduces, ALMOND — Analytics and Machine Learning Framework for Actionable Intelligence from Clinical and Omics Data. As a case study, this dissertation describes how ALMOND addresses the unmet need for individualized medicine in treating major depressive disorder — the leading cause of medical disabilities worldwide. The biggest challenge in individualizing treatment of depression is in the heterogeneity of how depressive symptoms manifest between individuals, and in their varied response to the same treatment. ALMOND comprises a systematic analytical workflow to individualize antidepressant treatment by addressing the challenge of heterogeneity of major depressive disorder. First, “right patients” are identified by stratifying patients using unsupervised learning, that serves as a foundation to associate their disease states with multiple pharmacological (drug-associated) measures. Second, “right drug” selection is shown to be feasible by demonstrating that psychiatrists’ depression severity assessments augmented with pharmacogenomic measures can accurately predict remission of depressive symptoms using supervised learning. Finally, probabilistic graphs provide early and easily interpretable prognoses at the “right time” to a psychiatrist by accounting for changes in routinely assessed depressive symptoms’ severity. By choosing antidepressants that have the highest-likelihood of the patient achieving remission, the chances of persisting depressive symptoms are reduced, which is often the leading medical conditions in those who commit suicide or develop chronic illnesses.","abstract_has_math":false,"creators":["Athreya, Arjun Prasanna"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Iyer, Ravishankar K.","Hwu, Wen-Mei","Robinson, Gene","Sanders, William H.","Roth, Dan","Weinshilboum, Richard M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:35:51Z","date_published":"2019-08-23T20:35:51Z","updated_at":"2026-07-22T22:24:44Z","subjects":["artificial intelligence","machine learning","depression","genomics"],"languages":["en"],"rights":["Copyright 2019 Arjun Prasanna Athreya"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105013","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Iyer, Ravishankar K.","Hwu, Wen-Mei","Robinson, Gene","Sanders, William H.","Roth, Dan","Weinshilboum, Richard M."]},{"key":"dc:creator","label":"Author","values":["Athreya, Arjun Prasanna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:35:51Z","2021-08-24T09:15:35Z","2019-04-12","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["artificial intelligence","machine learning","depression","genomics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Arjun Prasanna Athreya"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105013"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Individualized medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on highest reported efficacy in clinical trials, which is often not effective for all individuals. In this dissertation, we assert that treatment selection and management can be individualized when clinicians assessment of disease symptoms are augmented with a few analytically identified patient-specific measures (e.g., genomics, metabolomics) that are prognostic or predictive of treatment outcomes. Patient-derived biological, clinical and symptom measures are sufficiently complex, i.e., heterogeneous, noisy and high-dimensional. The question for research then becomes: “Which few among these large complex measures are sufficient to augment the clinician’s disease assessment and treatment logic to individualize treatment decisions?” This dissertation introduces, ALMOND — Analytics and Machine Learning Framework for Actionable Intelligence from Clinical and Omics Data. As a case study, this dissertation describes how ALMOND addresses the unmet need for individualized medicine in treating major depressive disorder — the leading cause of medical disabilities worldwide. The biggest challenge in individualizing treatment of depression is in the heterogeneity of how depressive symptoms manifest between individuals, and in their varied response to the same treatment. ALMOND comprises a systematic analytical workflow to individualize antidepressant treatment by addressing the challenge of heterogeneity of major depressive disorder. First, “right patients” are identified by stratifying patients using unsupervised learning, that serves as a foundation to associate their disease states with multiple pharmacological (drug-associated) measures. Second, “right drug” selection is shown to be feasible by demonstrating that psychiatrists’ depression severity assessments augmented with pharmacogenomic measures can accurately predict remission of depressive symptoms using supervised learning. Finally, probabilistic graphs provide early and easily interpretable prognoses at the “right time” to a psychiatrist by accounting for changes in routinely assessed depressive symptoms’ severity. By choosing antidepressants that have the highest-likelihood of the patient achieving remission, the chances of persisting depressive symptoms are reduced, which is often the leading medical conditions in those who commit suicide or develop chronic illnesses.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Arjun Athreya, accepted the attached license on 2019-04-12 at 15:37.","The student, Arjun Athreya, submitted this Dissertation for approval on 2019-04-12 at 15:45.","This Dissertation was approved for publication on 2019-04-12 at 16:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13592 on 2019-08-22 at 15:05:58","Made available in DSpace on 2019-08-23T20:35:51Z (GMT). No. of bitstreams: 2 ATHREYA-DISSERTATION-2019.pdf: 9134888 bytes, checksum: 5f819734cce55c32e589a06aed2678a1 (MD5) LICENSE.txt: 4210 bytes, checksum: bace0472d85925aaf9c9802ced5375ea (MD5) Previous issue date: 2019-04-12","Embargo set by: Seth Robbins for item 112132 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112132 on 2021-08-24T09:15:35Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Making augmented human intelligence in medicine practical: A case study of treating major depressive disorder"]}]}],"canonical_facts":{"dc:contributor":["Iyer, Ravishankar K.","Hwu, Wen-Mei","Robinson, Gene","Sanders, William H.","Roth, Dan","Weinshilboum, Richard M."],"dc:creator":["Athreya, Arjun Prasanna"],"dc:date":["2019-08-23T20:35:51Z","2021-08-24T09:15:35Z","2019-04-12","2019-05"],"dc:description":["Individualized medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on highest reported efficacy in clinical trials, which is often not effective for all individuals. In this dissertation, we assert that treatment selection and management can be individualized when clinicians assessment of disease symptoms are augmented with a few analytically identified patient-specific measures (e.g., genomics, metabolomics) that are prognostic or predictive of treatment outcomes. Patient-derived biological, clinical and symptom measures are sufficiently complex, i.e., heterogeneous, noisy and high-dimensional. The question for research then becomes: “Which few among these large complex measures are sufficient to augment the clinician’s disease assessment and treatment logic to individualize treatment decisions?” This dissertation introduces, ALMOND — Analytics and Machine Learning Framework for Actionable Intelligence from Clinical and Omics Data. As a case study, this dissertation describes how ALMOND addresses the unmet need for individualized medicine in treating major depressive disorder — the leading cause of medical disabilities worldwide. The biggest challenge in individualizing treatment of depression is in the heterogeneity of how depressive symptoms manifest between individuals, and in their varied response to the same treatment. ALMOND comprises a systematic analytical workflow to individualize antidepressant treatment by addressing the challenge of heterogeneity of major depressive disorder. First, “right patients” are identified by stratifying patients using unsupervised learning, that serves as a foundation to associate their disease states with multiple pharmacological (drug-associated) measures. Second, “right drug” selection is shown to be feasible by demonstrating that psychiatrists’ depression severity assessments augmented with pharmacogenomic measures can accurately predict remission of depressive symptoms using supervised learning. Finally, probabilistic graphs provide early and easily interpretable prognoses at the “right time” to a psychiatrist by accounting for changes in routinely assessed depressive symptoms’ severity. By choosing antidepressants that have the highest-likelihood of the patient achieving remission, the chances of persisting depressive symptoms are reduced, which is often the leading medical conditions in those who commit suicide or develop chronic illnesses.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Arjun Athreya, accepted the attached license on 2019-04-12 at 15:37.","The student, Arjun Athreya, submitted this Dissertation for approval on 2019-04-12 at 15:45.","This Dissertation was approved for publication on 2019-04-12 at 16:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13592 on 2019-08-22 at 15:05:58","Made available in DSpace on 2019-08-23T20:35:51Z (GMT). No. of bitstreams: 2 ATHREYA-DISSERTATION-2019.pdf: 9134888 bytes, checksum: 5f819734cce55c32e589a06aed2678a1 (MD5) LICENSE.txt: 4210 bytes, checksum: bace0472d85925aaf9c9802ced5375ea (MD5) Previous issue date: 2019-04-12","Embargo set by: Seth Robbins for item 112132 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112132 on 2021-08-24T09:15:35Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105013"],"dc:language":["en"],"dc:rights":["Copyright 2019 Arjun Prasanna Athreya"],"dc:subject":["artificial intelligence","machine learning","depression","genomics"],"dc:title":["Making augmented human intelligence in medicine practical: A case study of treating major depressive disorder"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}