Mit Critical Data

MIT Critical Data MIT Critical Data consists of data scientists and clinicians from around the globe brought together by a vision to engender a data-driven healthcare system supported by clinical informatics without walls . In this ecosystem, the creation of evidence and clinical decision support tools is initiated, updated, honed and enhanced by scaling the access to and meaningful use of clinical data. Leo Anthony Celi Leo has practiced medicine in three continents, giving him broad...See more

MIT Critical Data MIT Critical Data consists of data scientists and clinicians from around the globe brought together by a vision to engender a data-driven healthcare system supported by clinical informatics without walls . In this ecosystem, the creation of evidence and clinical decision support tools is initiated, updated, honed and enhanced by scaling the access to and meaningful use of clinical data. Leo Anthony Celi Leo has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. His research is on secondary analysis of electronic health records and global health informatics. He founded and co-directs Sana at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology. He also holds a faculty position at Harvard Medical School as an intensivist at the Beth Israel Deaconess Medical Center and is the clinical research director for the Laboratory of Computational Physiology at MIT.Finally, he is one of the course directors for HST.936 at MIT - innovations in global health informatics and HST.953 - secondary analysis of electronic health records. Peter Charlton Peter gained the degree of MEng in Engineering Science in 2010 from the University of Oxford. Since then he held a research position, working jointly with Guy's and St Thomas' NHS Foundation Trust, and King's College London. Peter's research focuses on physiological monitoring of hospital patients, divided into three areas. The first area concerns the development of signal processing techniques to estimate clinical parameters from physiological signals. He has focused on unobtrusive estimation of respiratory rate for use in ambulatory settings, invasive estimation of cardiac output for use in critical care, and novel techniques for analysis of the pulse oximetry (photoplethysmogram) signal. Secondly, he is investigating the effectiveness of technologies for the acquisition of continuous and intermittent physiological measurements in ambulatory and intensive care settings. Thirdly, he is developing techniques to transform continuous monitoring data into measurements that are appropriate for real-time alerting of patient deteriorations. Mohammad Ghassemi Mohammad is a doctoral candidate at the Massachusetts Institute of Technology. As an undergraduate, he studied Electrical Engineering and graduated as both a Goldwater scholar and the University's "Outstanding Engineer". In 2011, Mohammad received an MPhil in Information Engineering from the University of Cambridge where he was also a recipient of the Gates-Cambridge Scholarship. Since arriving at MIT, he has perused research at the interface of machine learning and medical informatics. Mohammad's doctoral focus is on signal processing and machine learning techniques in the context of multi-modal, multi-scale datasets. He has helped put together the largest collection of post-anoxic coma EEGs inthe world. In addition to his thesis work, Mohammad has worked with the Samsung corporation, and several entities across campus building "smart devices" including: a multi-sensor wearable that passively monitors the physiological, audio and video activity of a user to estimate a latent emotional state. Alistair Johnson Alistair joined the Laboratory for Computational Physiology as a postdoctoral associate in 2015. He received his B.Eng in Biomedical and Electrical Engineering at McMaster University, Canada, and subsequently read for a D.Phil in Healthcare Innovation at the University of Oxford. His thesis was titled "Mortality and acuity assessment in critical care", and its focus included using machine learning techniques to predict... See less