Tianxi Cai

About

Tianxi Cai, ScD, is a Professor of Biomedical Informatics at Harvard Medical School and the Director of the Translational Data Science Center for a Learning Health System (CELEHS). His research focuses on the development and application of statistical and computational methods for analyzing large-scale biomedical data, with an emphasis on translational and population health sciences. He holds a joint appointment as the John Rock Professor of Population and Translational Data Sciences at Harvard T.H. Chan School of Public Health. Dr. Cai's work involves leveraging data science to improve health outcomes through innovative approaches in biomedical discovery, infrastructure, and clinical decision making. His contributions include advancing methodologies for analyzing complex biomedical data and fostering translational research that bridges data science and clinical practice.

Research topics

• Internal medicine
• Medicine
• Emergency medicine
• Pediatrics
• Genetics
• Pathology
• Intensive care medicine

Selected publications

• International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

  npj Digital Medicine · 2020-08-19 · 214 citations

  articleOpen access

  We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on tem…

  PublisherOA PDFDOI

• Evolving phenotypes of non-hospitalized patients that indicate long COVID

  BMC Medicine · 2021 · 151 citations

  BACKGROUND: For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a CO…

  DOI

• What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

  Journal of Medical Internet Research · 2021-01-10 · 116 citations

  articleOpen accessSenior author

  Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws…

  PublisherOA PDFDOI

• Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: an international multi-centre observational cohort study

  EClinicalMedicine · 2022 · 79 citations

  Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine valu…

  DOI

• Label efficient phenotyping for Long COVID using electronic health records

  npj Digital Medicine · 2025-07-04 · 4 citations

  articleOpen accessSenior author

  Long COVID poses a significant disease burden globally, but its heterogeneous presentation and unreliable coding practices render it difficult to study. Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. We introduce the LAbel-efficienT Long COVID pHenotyping (LATCH) algorithm, which synthesizes a small number of gold-standard labels and a large, unlabeled dataset with many electronic health record (EHR) features. Both inter…

  PublisherOA PDFDOI

Recent grants

• Theory and Methods for Estimation of Nonsmooth Functionals and Detection of Simultaneous Signals

  NSF · $486k · 2014–2017

• FRG: Collaborative Research: Statistical Inference for High-Dimensional Data: Theory, Methodology and Applications

  NSF · $143k · 2009–2013

• Random Matrix Theory and High Dimensional Statistics

  NSF · $255k · 2012–2016

Frequent coauthors

• Shawn N. Murphy

  381 shared

• Vivian S. Gainer

  281 shared

• Katherine P. Liao

  Harvard University

  266 shared

• Víctor M. Castro

  Mass General Brigham

  242 shared

• Isaac S. Kohane

  Harvard University

  226 shared

• Guergana Savova

  Harvard University

  220 shared

• Sheng Yu

  Tsinghua University

  219 shared

• Anil Can

  Brigham and Women's Hospital

  214 shared

Labs

• Cai LabPI

Similar researchers at Harvard University

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• Shamil Sunyaevh-122