About

Chirag Patel is an Associate Professor of Biomedical Informatics at Harvard Medical School, based at the Department of Biomedical Informatics in Boston. His long-term research goal is to address problems in human health and disease by developing computational and bioinformatics methods to reproducibly and efficiently reason over high-throughput data streams spanning molecules to populations. His group aims to dissect inter-individual differences in human phenomes through strategies that integrate data sources capturing the comprehensive clinical experience, environmental exposure (including high-throughput measures of the exposome), and inherited genomic variation. Patel received his doctorate in biomedical informatics from Stanford University.

Research topics

• Medicine
• Internal medicine
• Genetics
• Pediatrics

Selected publications

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

  BMC Medicine · 2021 · 151 citations

  • Medicine
  • Internal medicine
  • Pediatrics

  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 COVID-19 infection. METHODS: In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3-6 and 6-9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. RESULTS: We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients' medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94-3.46]), alopecia (OR 3.09, 95% CI [2.53-3.76]), chest pain (OR 1.27, 95% CI [1.09-1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22-2.10]), shortness of breath (OR 1.41, 95% CI [1.22-1.64]), pneumonia (OR 1.66, 95% CI [1.28-2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22-1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. CONCLUSIONS: The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.

  DOI

Recent grants

• Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan Africa

  NIH · $683k · 2017–2021

• Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan Africa

  NIH · $2.0M · 2017–2022

• Data science tools to identify robust exposure-phenotype associations for precision medicine

  NIH · $3.5M · 2021–2026

• Data-driven identification of environmental factors in cardiovascular disease

  NIH · $492k · 2016–2018

• Augmented mapping of the Extreme Heat and Cold Events (EHE/ECE) at continental scale with cloud-based computing

  NIH · $2.6M · 2022–2025

Frequent coauthors

• Arjun K. Manrai

  Harvard University

  101 shared

• Alicia R. Martin

  Massachusetts General Hospital

  71 shared

• Yixuan He

  First Affiliated Hospital of Xi'an Jiaotong University

  71 shared

• James A. Diao

  Harvard University

  70 shared

• Michael H. Cho

  Brigham and Women's Hospital

  67 shared

• Edwin K. Silverman

  Harvard University

  67 shared

• Luke Melas-Kyriazi

  66 shared

• Emma Pierson

  65 shared

Labs

• Avillach LabPI

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