Search Results for author: Bibhas Chakraborty

Found 10 papers, 4 papers with code

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions

no code implementations4 Mar 2022 Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty

Reinforcement learning (RL) is acquiring a key role in the space of adaptive interventions (AIs), attracting a substantial interest within methodological and theoretical literature and becoming increasingly popular within health sciences.

Causal Inference reinforcement-learning

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

no code implementations22 Nov 2021 Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu

In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400, 000 ED visits from 2011 to 2019.

A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens

no code implementations13 Jul 2021 Palash Ghosh, Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty

A dynamic treatment regimen (DTR) is a set of decision rules to personalize treatments for an individual using their medical history.

Q-Learning

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

1 code implementation13 Jul 2021 Han Yuan, Feng Xie, Marcus Eng Hock Ong, Yilin Ning, Marcel Lucas Chee, Seyed Ehsan Saffari, Hairil Rizal Abdullah, Benjamin Alan Goldstein, Bibhas Chakraborty, Nan Liu

All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).

Decision Making Interpretable Machine Learning +1

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

1 code implementation13 Jun 2021 Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty

We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.

BIG-bench Machine Learning Interpretable Machine Learning +1

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