no code implementations • 8 Mar 2024 • Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness.
1 code implementation • 8 Mar 2024 • Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu
We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States.
1 code implementation • 10 Dec 2023 • Shraddha M. Naik, Tanujit Chakraborty, Abdenour Hadid, Bibhas Chakraborty
This paper introduces an imbalanced data-oriented approach using probabilistic neural networks (PNNs) with a skew normal probability kernel to address this major challenge.
no code implementations • 24 Nov 2023 • Xueqing Liu, Nina Deliu, Tanujit Chakraborty, Lauren Bell, Bibhas Chakraborty
Mobile health (mHealth) technologies aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions.
no code implementations • 14 Apr 2023 • Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang, Fabio Renato Manzolli Leite, Bibhas Chakraborty, Chuan Hong, Yilin Ning, Feng Xie, Zhen Ling Teo, Daniel Shu Wei Ting, Hamed Haddadi, Marcus Eng Hock Ong, Marco Aurélio Peres, Nan Liu
Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice.
1 code implementation • 1 Mar 2023 • Siqi Li, Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Chuan Hong, Feng Xie, Han Yuan, Mingxuan Liu, Daniel M. Buckland, Yong Chen, Nan Liu
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
no code implementations • 15 Oct 2022 • Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu
We found that model backbone(s) differed among data types as well as the imputation strategy.
no code implementations • 4 Mar 2022 • Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly popular tool for delivering adaptive interventions (AIs).
1 code implementation • 17 Feb 2022 • Seyed Ehsan Saffari, Yilin Ning, Xie Feng, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes.
1 code implementation • 10 Jan 2022 • Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models.
1 code implementation • 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.
1 code implementation • 6 Oct 2021 • Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
Interpretable machine learning has been focusing on explaining final models that optimize performance.
no code implementations • 21 Jul 2021 • Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
To some extent, current deep learning solutions can address these challenges.
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).
no code implementations • 13 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.
1 code implementation • 13 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