{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84074"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84074","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Interpretable Learning for Hospital Readmission Prediction from Healthcare Data","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Jiang, Jialiang"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Chandola, Varun","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:39Z","date_published":"2022-06-21T15:47:39Z","updated_at":"2026-07-27T19:05:30Z","subjects":["computer science","computer engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84074","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chandola, Varun","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Jiang, Jialiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:39Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science","computer engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84074"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Rapid and accurate identification of hospitalized patients at high risk for readmission, disease, extended length of stay (LOS), mortality, etc., has the potential to improve quality of care and reduce avoidable harm and costs. However, most data driven studies for risk prediction in a healthcare setting have produced non-interpretable black boxes, which precludes them from being used effectively within the decision support systems in the hospitals. The focus of this dissertation is on developing techniques to improve the interpretability and explainability of machine learning models in the context of healthcare by incorporating domain knowledge, specifically for the task of predicting the risk of hospital readmission. Preventable hospital readmissions have been identified as one of the primary targets for reducing costs and improving healthcare delivery, and advancements in data-driven approaches for this critical task can potentially have a significant impact on the healthcare system.Three approaches have been proposed in this dissertation to predict readmission risk. In the first two approaches, we focus on incorporating domain knowledge, in the form of hierarchical taxonomies available for disease codes, to improve the interpretability of a linear readmission prediction model. Two models are proposed with accuracies that are comparable to state of art methods. However, both models produce a highly interpretable output, which allows medical experts to draw clinically relevant insights and identify key factors associated with hospital readmissions. In both models, we exploit the domain induced hierarchical structure available for the disease codes which are the features for the classification algorithm. In the first approach, a structured sparsity regularization based model is applied. In the second approach, to improve the interpretability, a novel tree-structured sparsity-inducing regularization norm is proposed. Furthermore, a quantitative evaluation metric to assess the interpretability of any machine learning model in which the features are arranged in a non-flat structure is proposed based on the idea of Shannon Entropy.For the third approach, a novel deep learning architecture is proposed, that incorporates domain knowledge in the learning mechanism, and yields a predictive model that is highly accurate and provides model interpretability as well as explainability of the output predictions. The proposed solution provides both interpretability of the learnt model and explainability of the prediction outcomes. By leveraging both hidden domain knowledge and proper state-of-art methodologies, the proposed model can make full use of all available information to reach better performance as well. Results on two real healthcare claims data sets show that the proposed model outperforms state of art methods proposed for this task, both in terms of accuracy and interpretability/explainability.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Interpretable Learning for Hospital Readmission Prediction from Healthcare Data"]}]}],"canonical_facts":{"dc:contributor":["Chandola, Varun","Computer Science and Engineering"],"dc:creator":["Jiang, Jialiang"],"dc:date":["2022-06-21T15:47:39Z","2020"],"dc:description":["Ph.D.","Rapid and accurate identification of hospitalized patients at high risk for readmission, disease, extended length of stay (LOS), mortality, etc., has the potential to improve quality of care and reduce avoidable harm and costs. However, most data driven studies for risk prediction in a healthcare setting have produced non-interpretable black boxes, which precludes them from being used effectively within the decision support systems in the hospitals. The focus of this dissertation is on developing techniques to improve the interpretability and explainability of machine learning models in the context of healthcare by incorporating domain knowledge, specifically for the task of predicting the risk of hospital readmission. Preventable hospital readmissions have been identified as one of the primary targets for reducing costs and improving healthcare delivery, and advancements in data-driven approaches for this critical task can potentially have a significant impact on the healthcare system.Three approaches have been proposed in this dissertation to predict readmission risk. In the first two approaches, we focus on incorporating domain knowledge, in the form of hierarchical taxonomies available for disease codes, to improve the interpretability of a linear readmission prediction model. Two models are proposed with accuracies that are comparable to state of art methods. However, both models produce a highly interpretable output, which allows medical experts to draw clinically relevant insights and identify key factors associated with hospital readmissions. In both models, we exploit the domain induced hierarchical structure available for the disease codes which are the features for the classification algorithm. In the first approach, a structured sparsity regularization based model is applied. In the second approach, to improve the interpretability, a novel tree-structured sparsity-inducing regularization norm is proposed. Furthermore, a quantitative evaluation metric to assess the interpretability of any machine learning model in which the features are arranged in a non-flat structure is proposed based on the idea of Shannon Entropy.For the third approach, a novel deep learning architecture is proposed, that incorporates domain knowledge in the learning mechanism, and yields a predictive model that is highly accurate and provides model interpretability as well as explainability of the output predictions. The proposed solution provides both interpretability of the learnt model and explainability of the prediction outcomes. By leveraging both hidden domain knowledge and proper state-of-art methodologies, the proposed model can make full use of all available information to reach better performance as well. Results on two real healthcare claims data sets show that the proposed model outperforms state of art methods proposed for this task, both in terms of accuracy and interpretability/explainability.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84074"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computer science","computer engineering"],"dc:title":["Interpretable Learning for Hospital Readmission Prediction from Healthcare Data"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:30Z"}