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Gil Alterovitz. Liu, Jenny, M. Eng. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2016-01-04T20:00:32Z 2016-01-04T20:00:32Z 2015 2015 http://hdl.handle.net/1721.1/100632 933229654 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 45-50). Hospital Acquired Complications (HACs) are a serious problem affecting modern day healthcare institutions. It is estimated that in US hospitals, HACs cause an approximately 10% increase in total inpatient hospital costs. With US hospital spending totaling nearly $900 billion per year, the tremendous damages caused by HACs is no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve mortality rates. Here we show a machine-learning model for predicting the occurrence of HACs using clinical data limited to short periods following Intensive Care Unit (ICU) admission. In addition, we also identify several keystone features that demonstrate high predictive power HACs during certain time periods following patient admission. Based on our research, we can reduce excessive hospital costs due to HAC by at least $10 billion annually. We can also reduce the number of excessive hospital stay days by 4.6 million days, and potentially reduce patient mortality by at least 10k patients. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings long before the complication symptoms are manifested. These findings could provide a great aid to doctors and other healthcare professionals in containing the damages caused by HACs in healthcare institutions nationwide. by Jenny Liu. M.Eng. 114 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. An integrative predictive model for hospital acquired complications Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e55cbeb3-2244-49a2-ba2c-4b541b3a7435"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>An integrative predictive model for hospital acquired complications</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Liu, Jenny, M. Eng. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Hospital Acquired Complications (HACs) are a serious problem affecting modern day healthcare institutions. It is estimated that in US hospitals, HACs cause an approximately 10% increase in total inpatient hospital costs. With US hospital spending totaling nearly $900 billion per year, the tremendous damages caused by HACs is no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve mortality rates. Here we show a machine-learning model for predicting the occurrence of HACs using clinical data limited to short periods following Intensive Care Unit (ICU) admission. In addition, we also identify several keystone features that demonstrate high predictive power HACs during certain time periods following patient admission. Based on our research, we can reduce excessive hospital costs due to HAC by at least $10 billion annually. We can also reduce the number of excessive hospital stay days by 4.6 million days, and potentially reduce patient mortality by at least 10k patients. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings long before the complication symptoms are manifested. These findings could provide a great aid to doctors and other healthcare professionals in containing the damages caused by HACs in healthcare institutions nationwide.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>