By integrating state-of-the-art data science techniques with novel biological insights, the Sinha Lab aims to uncover distinct subgroups within broad critical illness syndromes—paving the way for precision medicine. Their research program is dedicated to improving patient outcomes by tailoring treatments based on individual biological profiles, offering a more personalized and effective approach to critical care.

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To redefine the way critically ill patients are identified and treated.

Research

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Recent Publications

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Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials
Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis
Development and validation of parsimonious algorithms to classify acute respiratory distress syndrome phenotypes: a secondary analysis of randomised controlled trials
Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials
Molecular Phenotypes of Acute Respiratory Distress Syndrome in the ROSE Trial Have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally over Time
Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls
Physiologic Analysis and Clinical Performance of the Ventilatory Ratio in Acute Respiratory Distress Syndrome
Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data