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Una-May O'Reilly and Kalyan Veeramachaneni. Dernoncourt, Franck Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2015-06-10T19:10:07Z 2015-06-10T19:10:07Z 2014 2015 http://hdl.handle.net/1721.1/97328 910342015 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February 2015. Cataloged from PDF version of thesis. Includes bibliographical references (pages 109-114). Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months. In response we design a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining knowledge from waveforms. BeatDB radically shrinks the time an investigation takes by: * supporting fast, flexible investigations by offering a multi-level parameterization, allowing the user to define the condition to predict, the features, and many other investigation parameters. * precomputing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates. In this thesis, we present BeatDB and demonstrate how it supports flexible investigations on the entire set of arterial blood pressure data in the MIMIC II Waveform Database, which contains over 5000 patients and 1 billion of blood pressure beats. We focus on the usefulness of wavelets as features in the context of blood pressure prediction and use Gaussian process to accelerate the search of the feature yielding the highest AUROC. by Franck Dernoncourt. S.M. 153 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. BeatDB : an end-to-end approach to unveil saliencies from massive signal data sets End-to-end approach to unveil saliencies from massive signal data sets Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="df098167-cdc0-47ea-b783-9c8890538334"> <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>BeatDB : an end-to-end approach to unveil saliencies from massive signal data sets</Title> <Subtitle>End-to-end approach to unveil saliencies from massive signal data sets</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Dernoncourt, Franck</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>Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months. In response we design a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining knowledge from waveforms. BeatDB radically shrinks the time an investigation takes by: * supporting fast, flexible investigations by offering a multi-level parameterization, allowing the user to define the condition to predict, the features, and many other investigation parameters. * precomputing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates. In this thesis, we present BeatDB and demonstrate how it supports flexible investigations on the entire set of arterial blood pressure data in the MIMIC II Waveform Database, which contains over 5000 patients and 1 billion of blood pressure beats. We focus on the usefulness of wavelets as features in the context of blood pressure prediction and use Gaussian process to accelerate the search of the feature yielding the highest AUROC.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>