{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/38326"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/38326","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Predicting the risk and trajectory of intensive care patients using survival models","abstract":"Using artificial intelligence to assist physicians in patient care has received sustained interest over the past several decades. Recently, with automated systems at most bedsides, the amount of patient information collected continues to increase, providing specific impetus for intelligent systems that can interpret this information. In fact, the large set of sensors and test results, often measured repeatedly over long periods of time, make it challenging for caregivers to quickly utilize all of the data for optimal patient treatment. This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are constructed that predict individual patient survival probabilities at fixed intervals in the future. These models seek to help physicians interpret the large amount of data available in order to provide optimal patient care. We find that the survival predictions from our models are comparable to survival predictions using the SAPS score, but are available throughout the patient's ICU course instead of only at 24 hours after admission. Additionally, we demonstrate effective prediction of patient mortality over fixed windows in the future.","abstract_html":"Using artificial intelligence to assist physicians in patient care has received sustained interest over the past several decades. Recently, with automated systems at most bedsides, the amount of patient information collected continues to increase, providing specific impetus for intelligent systems that can interpret this information. In fact, the large set of sensors and test results, often measured repeatedly over long periods of time, make it challenging for caregivers to quickly utilize all of the data for optimal patient treatment. This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are constructed that predict individual patient survival probabilities at fixed intervals in the future. These models seek to help physicians interpret the large amount of data available in order to provide optimal patient care. We find that the survival predictions from our models are comparable to survival predictions using the SAPS score, but are available throughout the patient's ICU course instead of only at 24 hours after admission. Additionally, we demonstrate effective prediction of patient mortality over fixed windows in the future.","abstract_has_math":false,"creators":["Hug, Caleb W. (Caleb Wayne)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Peter Szolovits."],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006","date_published":"2006","updated_at":"2026-07-22T22:21:11Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/38326","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Peter Szolovits."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Hug, Caleb W. 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Recently, with automated systems at most bedsides, the amount of patient information collected continues to increase, providing specific impetus for intelligent systems that can interpret this information. In fact, the large set of sensors and test results, often measured repeatedly over long periods of time, make it challenging for caregivers to quickly utilize all of the data for optimal patient treatment. This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are constructed that predict individual patient survival probabilities at fixed intervals in the future. These models seek to help physicians interpret the large amount of data available in order to provide optimal patient care. We find that the survival predictions from our models are comparable to survival predictions using the SAPS score, but are available throughout the patient's ICU course instead of only at 24 hours after admission. Additionally, we demonstrate effective prediction of patient mortality over fixed windows in the future."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Predicting the risk and trajectory of intensive care patients using survival models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Peter Szolovits."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Hug, Caleb W. (Caleb Wayne)"],"dc:date.accessioned":["2007-08-03T18:30:16Z"],"dc:date.available":["2007-08-03T18:30:16Z"],"dc:date.issued":["2006"],"dc:description":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.","Includes bibliographical references (p. 119-126)."],"dc:description.abstract":["Using artificial intelligence to assist physicians in patient care has received sustained interest over the past several decades. Recently, with automated systems at most bedsides, the amount of patient information collected continues to increase, providing specific impetus for intelligent systems that can interpret this information. In fact, the large set of sensors and test results, often measured repeatedly over long periods of time, make it challenging for caregivers to quickly utilize all of the data for optimal patient treatment. This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are constructed that predict individual patient survival probabilities at fixed intervals in the future. These models seek to help physicians interpret the large amount of data available in order to provide optimal patient care. We find that the survival predictions from our models are comparable to survival predictions using the SAPS score, but are available throughout the patient's ICU course instead of only at 24 hours after admission. Additionally, we demonstrate effective prediction of patient mortality over fixed windows in the future."],"dc:description.degree":["S.M."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/38326"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Predicting the risk and trajectory of intensive care patients using survival models"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:11Z"}