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Miguel Armengol, Ph.D.

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Biography
Polytechnic University of MadridPhD CandidateBiomed Eng - Promotion, Integration, Mgmt and Processing of Critical Inpatients’ Open Big Data Repos
Polytechnic University of Madrid, MadridM.S.07/2013Biomedical Engineering - Specialization: Biomedical Imaging and Devices
Universidad Alfonso X el Sabio, MadridB.S. 07/2012in Telecommunication Engineering
2017
GPU Machine Learning Grant
2017
NATED Nanotechnology diagnostics
2019
Datathon - First Place

Overview
A Senior Research Associate at Harvard-MIT Division of Health Sciences and Technology, moreover a Chair of the study group 'Big Data and Machine Learning: Shaping the Future of Healthcare' at Harvard.

An ambition to save and improve lives by developing smarter healthcare solutions. A hard worker in order to become a digital pioneer, an adventurer who push the boundaries of what is possible and rise to every challenge.

At the Division of Clinical Informatics his work involves the use of data engineering (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 the LCP, Laboratory of Computational Physiology and MIT Critical Data group, he has experience working with large and complex data sets related to critically ill patients (Intensive Care Unity and Emergency Room). He has being performing non-routine analysis problems by applying machine learning and robust statistical methods.

Bibliographic
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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  1. Núñez Reiz A, Armengol de la Hoz MA, Sánchez García M. Big Data Analysis and Machine Learning in Intensive Care Units. Med Intensiva. 2018 Dec 24. PMID: 30591356.
    Citations:    
  2. Núñez Reiz A. Big data and machine learning in critical care: Opportunities for collaborative research. Med Intensiva. 2019 Jan - Feb; 43(1):52-57. PMID: 30077427.
    Citations:    Fields:    
  3. Serpa Neto A, Kugener G, Bulgarelli L, Rabello Filho R, Hoz MÁA, Johnson AE, Paik KE, Torres F, Xie C, Amaro Júnior E, Ferraz LJR, Celi LA, Deliberato RO. First Brazilian datathon in critical care. Rev Bras Ter Intensiva. 2018 Mar; 30(1):6-8. PMID: 29742215.
    Citations:    Fields:    Translation:Humans
  4. Deliberato RO, Ko S, Komorowski M, Armengol de La Hoz MA, Frushicheva MP, Raffa JD, Johnson AEW, Celi LA, Stone DJ. Severity of Illness Scores May Misclassify Critically Ill Obese Patients. Crit Care Med. 2018 03; 46(3):394-400. PMID: 29194147.
    Citations:    Fields:    
  5. Piza FMT, Celi LA, Deliberato RO, Bulgarelli L, de Carvalho FRT, Filho RR, de La Hoz MAA, Kesselheim JC. Assessing team effectiveness and affective learning in a datathon. Int J Med Inform. 2018 04; 112:40-44. PMID: 29500020.
    Citations:    Fields:    Translation:Humans
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Funded by the NIH/NCATS Clinical and Translational Science Award (CTSA) program, grant number UL1TR001102, and through institutional support from Harvard University, Harvard Medical School, Harvard T.H. Chan School of Public Health, Beth Israel Deaconess Medical Center, Boston Children's Hospital, Brigham and Women's Hospital, Massachusetts General Hospital and the Dana Farber Cancer Institute.