Bibhas Chakraborty

Adjunct Associate Professor in the Department of Biostatistics & Bioinformatics

Biostatistics & Bioinformatics, Division of Biostatistics

bibhas.chakraborty@duke.edu

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Credentials
1. Education

Academic Experience
1. Current Duke Appointments & Affiliations

Global Scholarship

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Research

Scholarly Works

Teaching & Mentoring

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Scholarly Works - Preprints

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data

Preprint · March 8, 2024 Link to item Cite

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare

Preprint · March 8, 2024 Link to item Cite

Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

Preprint · April 14, 2023 Link to item Cite

FedScore: A privacy-preserving framework for federated scoring system development

Preprint · March 1, 2023 Link to item Cite

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

Preprint · October 15, 2022 Link to item Cite

AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes

Preprint · February 16, 2022 Link to item Cite

A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study

Preprint · January 10, 2022 Link to item Cite

Benchmarking emergency department triage prediction models with machine learning and large public electronic health records

Preprint · November 22, 2021 Link to item Cite

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation (Preprint)

Preprint · October 11, 2021 Full text Cite

Shapley variable importance clouds for interpretable machine learning

Preprint · October 5, 2021 Link to item Cite

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Preprint · July 21, 2021 Link to item Cite

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

Preprint · July 13, 2021 Link to item Cite

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

Preprint · June 13, 2021 Link to item Cite

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (Preprint)

Preprint · June 25, 2020 Full text Cite