Diego Martinez, PhD
PUCV Industrial Engineering & Johns Hopkins Medicine
I am an assistant professor in the School of Industrial Engineering at Pontificia Universidad Católica de Valparaíso (PUCV) in Chile, working at the intersection of data science and medicine. I am also a visiting professor at Johns Hopkins University in Baltimore, Maryland, with the Department of Emergency Medicine, the Center for Data Science in Emergency Medicine, the Malone Center for Engineering in Healthcare, and the Division of Biomedical Informatics and Data Science. I am an associate editor of IISE Transactions on Healthcare Systems Engineering. I teach probabilistic modeling (EII301) and machine learning (EII7446) at PUCV and a seminar in medical informatics at Johns Hopkins (ME 250.860). My Google Scholar keeps the most up-to-date list of publications from my group, and here is a list of current projects and collaborations:
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​COVID-19 RESPONSE
  • Health Disparities (with Kathleen Page): We are creating data and systems to monitor and minimize the impact of COVID-19 in socially marginalized groups through enhanced testing and vaccination campaigns. With the support of the NIH.​

THE SCIENCE OF CLINICAL DECISION SUPPORT
  • Connected Emergency Care (with Scott Levin): We are developing predictive EMR-integrated decision support tools to reduce health and financial harm for patients with respiratory tract infections seen in the ED. With the support of the AHRQ.
  • AI for Acute Kidney Injury Management (with Jeremiah Hinson): We are developing algorithms to predict the onset of acute kidney injury in ED patients, and deploying EMR-integrated decision support for ED clinicians. With the support of the AHRQ.

THE DESIGN OF HEALTH SYSTEMS
  • Waiting Lists Management (with Diana Prieto, Jose Zayas, and Felipe Feijoo): We are investigating the effects of prolonged waiting in countries with universal healthcare, and developing data-driven waiting list management systems. With the support of the AHW.
  • Hospital Epidemiology (with Eili Klein): We are investigating the epidemic processes by which infectious diseases spread over networks of contact between healthcare workers and patients, and developing decision support to minimize hospital-acquired infection risks. With the support of the CDC.

TEACHING
  • ME 250.860.0 Biomedical Informatics & Data Science grand rounds and seminar (at JHU with Harold Lehmann): We present Grand Rounds throughout the US academic year. Join us virtually on the 2nd Thursday of each month from 12-1 PM EST, where we hear from local and international informatics leaders and researchers.​​
  • EII7446-01 Applied Data Science (at PUCV): I provide both theoretical and practical coverage of statistical learning. Modest statistics and advanced linear algebra background is required. I include multiple design challenges in the class to contextualize theory with practice. Topics covered include classification and clustering.
I always have time for cool project ideas, collaborations or speaking -- write me at diego.martinez.c at pucv dot cl (or at diego.martinez.c at jhu dot edu)
Disclaimer: Any opinions, findings, conclusions, or recommendations expressed herein are those of the author and do not necessarily reflect the views of Johns Hopkins University or any other organization supporting our research.