We found that model backbone(s) differed among data types as well as the imputation strategy.
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.
This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes.
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models.
no code implementations • 22 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.
Interpretable machine learning has been focusing on explaining final models that optimize performance.
To some extent, current deep learning solutions can address these challenges.
A dynamic treatment regimen (DTR) is a set of decision rules to personalize treatments for an individual using their medical history.
1 code implementation • 13 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).
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.