Miguel Ángel Armengol de la Hoz
Research Associate: Critical Data - Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology.
Co-Chair: Big Data and Machine Learning, Shaping the Future of Healthcare - Harvard Study Group
Miguel Ángel Armengol de la Hoz is a Research Associate at the Division of Clinical Informatics of Beth Israel Deaconess Medical Center (Harvard Medical School) and at the Institute for Medical Engineering and Science (Massachusetts Institute of Technology).
His work at DCI involves the use of data engineering methods (optimizing tokenization and record linkage algorithms, transforming data into a canonical models to support temporal analysis of clinical events) and the development of novel techniques to measure clinical effectiveness and efficiency of health service.
As an affiliate at LCP, (Dir.: Professor Dr. Roger Mark) and MIT Critical Data group, (Dir. Leo Celi) he has experience working with large and complex data sets related to critically ill patients (Intensive Care Unity and Operating Room). He has being performing non-routine analysis problems by applying machine learning and robust statistical methods and supporting the 2018.HST.953 Course: 'Collaborative Data Science in Medicine' at MIT as Faculty. He also has the privilege of mentoring and organizing datathons taking place in Asia, South America, Europe and North America; inter-institutional sprint-like events where international experts, clinicians and data scientists are brought together to generate new knowledge employing data science and derive new clinical insights in an interdisciplinary atmosphere.
His research expertise involves: applying state-of-the-art advanced analytic, quantitative tools and modeling techniques to derive insights, solve complex problems and improve decisions about both patients and providers using intensive care unit, anesthesia and health economics data.
His other fields of interest are: innovation, design, marketing, cutting-edge technologies, medical devices, machine learning, intellectual property protection, internationalization and e-health project management.