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Collin M. Stultz. Chong Rodriguez, Alicia Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Engineering and Management Program Massachusetts Institute of Technology. Integrated Design and Management Program. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-10-15T20:25:03Z 2018-10-15T20:25:03Z 2018 2018 http://hdl.handle.net/1721.1/118555 1055204246 Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-48). Women who present with symptoms consistent with an Acute Coronary Syndrome (ACS) are often under-diagnosed and under-represented in clinical trials. Moreover, there are data to suggest that women with cardiovascular disease have worse outcomes, poorer prognoses, and higher mortality rates than men. Determining the risk of future adverse cardiovascular events for women who have previously suffered an ACS is therefore a problem of paramount importance in the field of cardiovascular medicine. The identification of high-risk patient subgroups typically begins with an evaluation of the patient's history, physical exam, and the surface electrocardiogram (ECG). Indeed, the ECG plays a central role in the assessment and management of patients post ACS. In this study, we develop and test a technique for automatically assessing the risk of death in women who presented with an ACS. The method combines both patient history and an automated analysis of the surface ECG to accurately quantify that patient's future risk. The clustering of patients into subgroups, each having a different level of risk, is used to develop an algorithm to quantify the risk of new patients who present with an ACS. In this work, a comprehensive comparison between clustering female only data and traditional, female and male data is demonstrated as risk stratification methodologies for learning the significance or impact of our test and its inputs. The model trained on the entire population always performs worse for female population and the model trained only on female patients always provides a better performance for these patients. Comparing to existing risk scores, the female-specific model performs better. by Alicia Chong Rodriguez. S.M. in Engineering and Management S.M. 48 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Engineering and Management Program. Integrated Design and Management Program. Electrical Engineering and Computer Science. Sex-specific computationally generated biomarkers for cardiovascular risk stratification post acute coronary syndrome Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7c3358f5-5ebb-46f8-8253-b9e646bd4c9e"> <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>Sex-specific computationally generated biomarkers for cardiovascular risk stratification post acute coronary syndrome</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Chong Rodriguez, Alicia</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>Engineering and Management Program.</Keyword> <Keyword>Integrated Design and Management Program.</Keyword> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Women who present with symptoms consistent with an Acute Coronary Syndrome (ACS) are often under-diagnosed and under-represented in clinical trials. Moreover, there are data to suggest that women with cardiovascular disease have worse outcomes, poorer prognoses, and higher mortality rates than men. Determining the risk of future adverse cardiovascular events for women who have previously suffered an ACS is therefore a problem of paramount importance in the field of cardiovascular medicine. The identification of high-risk patient subgroups typically begins with an evaluation of the patient&apos;s history, physical exam, and the surface electrocardiogram (ECG). Indeed, the ECG plays a central role in the assessment and management of patients post ACS. In this study, we develop and test a technique for automatically assessing the risk of death in women who presented with an ACS. The method combines both patient history and an automated analysis of the surface ECG to accurately quantify that patient&apos;s future risk. The clustering of patients into subgroups, each having a different level of risk, is used to develop an algorithm to quantify the risk of new patients who present with an ACS. In this work, a comprehensive comparison between clustering female only data and traditional, female and male data is demonstrated as risk stratification methodologies for learning the significance or impact of our test and its inputs. The model trained on the entire population always performs worse for female population and the model trained only on female patients always provides a better performance for these patients. Comparing to existing risk scores, the female-specific model performs better.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>