{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108322"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108322","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Health-AIM: An artificial intelligence approach for inference with clinical health datasets","abstract":"In the clinical health domain, artificial intelligence (AI) models need to use data to make reliable decisions regarding patient trajectories and treatments. Incorrect decisions can lead to strain on both caregiver and patient, and clinical datasets often have low volume, high dimensionality, and many missing values. This thesis presents Health-AIM, a Python platform designed to address these challenges by leveraging well-known AI techniques to make effective inferences using clinical health datasets. Health-AIM introduces a four-step workflow consisting of data preprocessing, feature extraction, inferencing, and results visualization, with comprehensive functionalities to handle a variety of scenarios in each step. Health-AIM supports the usage of custom probabilistic graphical models and of classifiers from Python’s sklearn library. Naïve Bayes networks and Bayesian networks specified by edge lists can be dynamically constructed and trained using Health-AIM. Accepting labeled patient data and user-parameters as input, Health-AIM enables runtime selection of data imputation, feature selection, classification, and testing methods with a single function call. Thus, different inferences can be performed rapidly in succession through simple adjustment of parameters. A real-life case study on prediction of postoperative survival in patients with metastatic epidural spinal cord compression is used to illustrate the various functionalities of Health-AIM.","abstract_html":"In the clinical health domain, artificial intelligence (AI) models need to use data to make reliable decisions regarding patient trajectories and treatments. Incorrect decisions can lead to strain on both caregiver and patient, and clinical datasets often have low volume, high dimensionality, and many missing values. This thesis presents Health-AIM, a Python platform designed to address these challenges by leveraging well-known AI techniques to make effective inferences using clinical health datasets. Health-AIM introduces a four-step workflow consisting of data preprocessing, feature extraction, inferencing, and results visualization, with comprehensive functionalities to handle a variety of scenarios in each step. Health-AIM supports the usage of custom probabilistic graphical models and of classifiers from Python’s sklearn library. Naïve Bayes networks and Bayesian networks specified by edge lists can be dynamically constructed and trained using Health-AIM. Accepting labeled patient data and user-parameters as input, Health-AIM enables runtime selection of data imputation, feature selection, classification, and testing methods with a single function call. Thus, different inferences can be performed rapidly in succession through simple adjustment of parameters. A real-life case study on prediction of postoperative survival in patients with metastatic epidural spinal cord compression is used to illustrate the various functionalities of Health-AIM.","abstract_has_math":false,"creators":["Anjur, Vikram Sriram"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Iyer, Ravishankar K","Arnold, Paul"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-27T00:51:26Z","date_published":"2020-08-27T00:51:26Z","updated_at":"2026-07-22T22:24:48Z","subjects":["data science, artificial intelligence, inference, classification, feature extraction, data imputation, machine learning, statistics, data preprocessing, probabilistic graphical models, clinical health prediction"],"languages":["en"],"rights":["Copyright 2020 Vikram Sriram Anjur"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108322","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Iyer, Ravishankar K","Arnold, Paul"]},{"key":"dc:creator","label":"Author","values":["Anjur, Vikram Sriram"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-27T00:51:26Z","2022-08-27T00:51:40Z","2020-05-11","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["data science, artificial intelligence, inference, classification, feature extraction, data imputation, machine learning, statistics, data preprocessing, probabilistic graphical models, clinical health prediction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Vikram Sriram Anjur"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108322"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In the clinical health domain, artificial intelligence (AI) models need to use data to make reliable decisions regarding patient trajectories and treatments. Incorrect decisions can lead to strain on both caregiver and patient, and clinical datasets often have low volume, high dimensionality, and many missing values. This thesis presents Health-AIM, a Python platform designed to address these challenges by leveraging well-known AI techniques to make effective inferences using clinical health datasets. Health-AIM introduces a four-step workflow consisting of data preprocessing, feature extraction, inferencing, and results visualization, with comprehensive functionalities to handle a variety of scenarios in each step. Health-AIM supports the usage of custom probabilistic graphical models and of classifiers from Python’s sklearn library. Naïve Bayes networks and Bayesian networks specified by edge lists can be dynamically constructed and trained using Health-AIM. Accepting labeled patient data and user-parameters as input, Health-AIM enables runtime selection of data imputation, feature selection, classification, and testing methods with a single function call. Thus, different inferences can be performed rapidly in succession through simple adjustment of parameters. A real-life case study on prediction of postoperative survival in patients with metastatic epidural spinal cord compression is used to illustrate the various functionalities of Health-AIM.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Vikram Anjur, accepted the attached license on 2020-05-06 at 10:40.","The student, Vikram Anjur, submitted this Thesis for approval on 2020-05-06 at 11:57.","This Thesis was approved for publication on 2020-05-11 at 08:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15245 on 2020-08-25 at 17:43:24","Made available in DSpace on 2020-08-27T00:51:26Z (GMT). No. of bitstreams: 2 ANJUR-THESIS-2020.pdf: 4245094 bytes, checksum: 6686be6871c2f06813d0b3007790c4e8 (MD5) LICENSE.txt: 4209 bytes, checksum: 60828e32a6e54acd4c0872f6c36b19c7 (MD5) Previous issue date: 2020-05-11","Embargo set by: Seth Robbins for item 115937 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Health-AIM: An artificial intelligence approach for inference with clinical health datasets"]}]}],"canonical_facts":{"dc:contributor":["Iyer, Ravishankar K","Arnold, Paul"],"dc:creator":["Anjur, Vikram Sriram"],"dc:date":["2020-08-27T00:51:26Z","2022-08-27T00:51:40Z","2020-05-11","2020-05"],"dc:description":["In the clinical health domain, artificial intelligence (AI) models need to use data to make reliable decisions regarding patient trajectories and treatments. Incorrect decisions can lead to strain on both caregiver and patient, and clinical datasets often have low volume, high dimensionality, and many missing values. This thesis presents Health-AIM, a Python platform designed to address these challenges by leveraging well-known AI techniques to make effective inferences using clinical health datasets. Health-AIM introduces a four-step workflow consisting of data preprocessing, feature extraction, inferencing, and results visualization, with comprehensive functionalities to handle a variety of scenarios in each step. Health-AIM supports the usage of custom probabilistic graphical models and of classifiers from Python’s sklearn library. Naïve Bayes networks and Bayesian networks specified by edge lists can be dynamically constructed and trained using Health-AIM. Accepting labeled patient data and user-parameters as input, Health-AIM enables runtime selection of data imputation, feature selection, classification, and testing methods with a single function call. Thus, different inferences can be performed rapidly in succession through simple adjustment of parameters. A real-life case study on prediction of postoperative survival in patients with metastatic epidural spinal cord compression is used to illustrate the various functionalities of Health-AIM.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Vikram Anjur, accepted the attached license on 2020-05-06 at 10:40.","The student, Vikram Anjur, submitted this Thesis for approval on 2020-05-06 at 11:57.","This Thesis was approved for publication on 2020-05-11 at 08:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15245 on 2020-08-25 at 17:43:24","Made available in DSpace on 2020-08-27T00:51:26Z (GMT). No. of bitstreams: 2 ANJUR-THESIS-2020.pdf: 4245094 bytes, checksum: 6686be6871c2f06813d0b3007790c4e8 (MD5) LICENSE.txt: 4209 bytes, checksum: 60828e32a6e54acd4c0872f6c36b19c7 (MD5) Previous issue date: 2020-05-11","Embargo set by: Seth Robbins for item 115937 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108322"],"dc:language":["en"],"dc:rights":["Copyright 2020 Vikram Sriram Anjur"],"dc:subject":["data science, artificial intelligence, inference, classification, feature extraction, data imputation, machine learning, statistics, data preprocessing, probabilistic graphical models, clinical health prediction"],"dc:title":["Health-AIM: An artificial intelligence approach for inference with clinical health datasets"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:48Z"}