{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117829"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117829","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Temporal analysis of emergency department frequent users using machine learning approaches","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Fernandes, Lloyd"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Kang, Hyojung","Sreenivas, Ramavarapu S"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Emergency Department","Healthcare Machine Learning","Machine Learning","Ehr"],"languages":["en","eng"],"rights":["Copyright 2022 Lloyd Fernandes"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117829","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kang, Hyojung","Sreenivas, Ramavarapu S"]},{"key":"dc:creator","label":"Author","values":["Fernandes, Lloyd"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-07"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"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":["Emergency Department","Healthcare Machine Learning","Machine Learning","Ehr"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Lloyd Fernandes"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117829"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Lloyd Fernandes, accepted the attached license on 2022-12-02 at 14:42.","The student, Lloyd Fernandes, submitted this Thesis for approval on 2022-12-02 at 14:56.","This Thesis was approved for publication on 2022-12-07 at 16:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18743 on 2023-04-12 at 07:37:47","With a wealth of electronic health records (EHRs) available, there is an opportunity to better utilize the existing healthcare infrastructure by anticipating patient utilization to reduce healthcare costs. Emergency Department (ED) is one such expensive healthcare facility. However, emergency services are disproportionately used by a small set of users known as frequent ED users (EDFU) also called frequent flyers. They tend to be heavy users of other parts of the health care system and unable to get the appropriate care they need. By identifying these patients, programs can be developed to improve their health outcomes and reduce overall costs. In our study, we develop a prediction model which identifies frequent ED users based on their past electronic health records (EHR). We make use of regional trauma level 1 hospital data from 2011-2015, to predict whether the patient is an EDFU. We try to do that by leveraging varied pieces of information from the patient electronic health record for a year and predicting their behavior for the next year. We propose an end-to-end deep learning framework that uses past EHR to classify patients into frequent and non-frequent/occasional users. A diagnosis embedding inspired by the Skip-gram-based word2vec embedding framework was developed as input to the EDFU prediction model. We find that logistic regression models perform comparably well than recurrent neural networks and gradient-boosting machines. Embeddings perform slightly better than one-hot encoded diagnosis codes for both the logistic regression model and gradient boosting machine. However, presence of embeddings do not have any effect on recurrent neural networks. The second goal of this study is to investigate frequent users by analyzing their primary diagnosis codes for the first four visits. We define distance between any two visits based on their primary diagnosis. We do that by making use of similarity measures developed for semantic trees in natural language processing (NLP). We make use of the same dataset as before and isolate visits when the patient first became a frequent user. A proximity score is then defined which is the average of distances between all possible combinations of the first four visits when the patient first became a frequent user. The patients are then clustered into three distinct classes based on their proximity score. On analyzing these clusters for various visit and patient parameters, we find that patients with similar visits tend to be a higher frequency of visits than patients with dissimilar visits. Patients in the first cluster are discharged more often and arrive by Walk-In than other modes. We further elaborate on the diagnosis codes that characterize the patients in each of these three clusters. Such an analysis of patients can help EDs find patterns of diagnosis codes and help them develop strategies to address patients who come to ED with similar diagnoses."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Temporal analysis of emergency department frequent users using machine learning approaches"]}]}],"canonical_facts":{"dc:contributor":["Kang, Hyojung","Sreenivas, Ramavarapu S"],"dc:creator":["Fernandes, Lloyd"],"dc:date":["2022-12","2022-12-07"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Lloyd Fernandes, accepted the attached license on 2022-12-02 at 14:42.","The student, Lloyd Fernandes, submitted this Thesis for approval on 2022-12-02 at 14:56.","This Thesis was approved for publication on 2022-12-07 at 16:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18743 on 2023-04-12 at 07:37:47","With a wealth of electronic health records (EHRs) available, there is an opportunity to better utilize the existing healthcare infrastructure by anticipating patient utilization to reduce healthcare costs. Emergency Department (ED) is one such expensive healthcare facility. However, emergency services are disproportionately used by a small set of users known as frequent ED users (EDFU) also called frequent flyers. They tend to be heavy users of other parts of the health care system and unable to get the appropriate care they need. By identifying these patients, programs can be developed to improve their health outcomes and reduce overall costs. In our study, we develop a prediction model which identifies frequent ED users based on their past electronic health records (EHR). We make use of regional trauma level 1 hospital data from 2011-2015, to predict whether the patient is an EDFU. We try to do that by leveraging varied pieces of information from the patient electronic health record for a year and predicting their behavior for the next year. We propose an end-to-end deep learning framework that uses past EHR to classify patients into frequent and non-frequent/occasional users. A diagnosis embedding inspired by the Skip-gram-based word2vec embedding framework was developed as input to the EDFU prediction model. We find that logistic regression models perform comparably well than recurrent neural networks and gradient-boosting machines. Embeddings perform slightly better than one-hot encoded diagnosis codes for both the logistic regression model and gradient boosting machine. However, presence of embeddings do not have any effect on recurrent neural networks. The second goal of this study is to investigate frequent users by analyzing their primary diagnosis codes for the first four visits. We define distance between any two visits based on their primary diagnosis. We do that by making use of similarity measures developed for semantic trees in natural language processing (NLP). We make use of the same dataset as before and isolate visits when the patient first became a frequent user. A proximity score is then defined which is the average of distances between all possible combinations of the first four visits when the patient first became a frequent user. The patients are then clustered into three distinct classes based on their proximity score. On analyzing these clusters for various visit and patient parameters, we find that patients with similar visits tend to be a higher frequency of visits than patients with dissimilar visits. Patients in the first cluster are discharged more often and arrive by Walk-In than other modes. We further elaborate on the diagnosis codes that characterize the patients in each of these three clusters. Such an analysis of patients can help EDs find patterns of diagnosis codes and help them develop strategies to address patients who come to ED with similar diagnoses."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117829"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Lloyd Fernandes"],"dc:subject":["Emergency Department","Healthcare Machine Learning","Machine Learning","Ehr"],"dc:title":["Temporal analysis of emergency department frequent users using machine learning approaches"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}