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Richard R. Fletcher. Mofor Nkaze, John. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-02-10T21:41:10Z 2020-02-10T21:41:10Z 2019 2019 https://hdl.handle.net/1721.1/123747 1138946998 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 84-87). Over the past decade, the widespread adoption of smart phones has enabled their use as a primary data collection platform for a wide variety of research studies, including areas such as global health field research. In addition, smart phones also provide a portable means of collecting labelled data for use in Machine Learning and Artificial Intelligence. Despite these important global trends, existing scalable mobile data collection platforms are not available for use with machine learning data collection. In order to address this need, I have created PyMedServer, which is an easy-to-use server framework designed for large scale medical research spanning multiple distinct institutions. The framework provides built-in abstractions for clinicians, patients, medical measurements, clinician diagnoses, and machine learning analyses. To further facilitate adoption, PyMedServer provides client libraries for Android and Web. These libraries are compatible with any server built using the PyMedServer framework and provide features such as Local Storage, API integration, User Authentication and Authorization, Multi-Group Support and Measurement Labelling, to name a few. PyMedServer is written in Python and is designed to permit and facilitate the addition of plugins. Developers are granted access to labeled data and can contribute with Feature Extraction and Machine Learning plugins, while the framework takes care of concerns such as of Group Isolation, Security, Scalability, and Deployability. by John Mofor Nkaze. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 87 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 Electrical Engineering and Computer Science. PyMedServer : a server framework for mobile data collection and machine learning Server framework for mobile data collection and machine learning Thesis application/pdf 2020-02-10T21:41:10Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e7fa7684-1d8d-4724-af36-e9de368fdfce"> <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>PyMedServer : a server framework for mobile data collection and machine learning</Title> <Subtitle>Server framework for mobile data collection and machine learning</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Mofor Nkaze, John.</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>Over the past decade, the widespread adoption of smart phones has enabled their use as a primary data collection platform for a wide variety of research studies, including areas such as global health field research. In addition, smart phones also provide a portable means of collecting labelled data for use in Machine Learning and Artificial Intelligence. Despite these important global trends, existing scalable mobile data collection platforms are not available for use with machine learning data collection. In order to address this need, I have created PyMedServer, which is an easy-to-use server framework designed for large scale medical research spanning multiple distinct institutions. The framework provides built-in abstractions for clinicians, patients, medical measurements, clinician diagnoses, and machine learning analyses. To further facilitate adoption, PyMedServer provides client libraries for Android and Web. These libraries are compatible with any server built using the PyMedServer framework and provide features such as Local Storage, API integration, User Authentication and Authorization, Multi-Group Support and Measurement Labelling, to name a few. PyMedServer is written in Python and is designed to permit and facilitate the addition of plugins. Developers are granted access to labeled data and can contribute with Feature Extraction and Machine Learning plugins, while the framework takes care of concerns such as of Group Isolation, Security, Scalability, and Deployability.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>