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publications
Snippet policy network for multi-class varied-length ECG early classification
Published in IEEE Transactions on Knowledge and Data Engineering, 2022
Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases. The prevailing studies formulate arrhythmia detection from ECG as a time series classification problem. Meanwhile, early detection of arrhythmia presents a real-world demand for early prevention and diagnosis. In this paper, we address a problem of cardiovascular diseases early classification, which is a varied-length and long-length time series early classification problem as well.
Snippet policy network v2: Knee-guided neuroevolution for multi-lead ecg early classification
Published in IEEE Transactions on Neural Networks and Learning Systems, 2022
Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU, early treatment contributes to the patient’s fast recovery, and early warning could even save lives. Hence, in these cases, it is worthy of trading, to some extent, classification accuracy in favor of earlier decisions when the time series data are collected over time. In this article, we propose a novel deep reinforcement learning-based framework, snippet policy network V2 (SPN-V2), for long and varied-length multi-lead electrocardiogram (ECG) early classification.
Neural Network to Identifying Type 2 Diabetes (T2D) Progression Subphenotypes
Published in American Diabetes Association, 2024
T2D is a heterogeneous disease with variations in presentation, progression, and response to treatments across individuals. We developed a novel GNN-based framework to identify distinct T2D progression pathways using electronic health records (EHR) data.
A scoping review of fair machine learning techniques when using real-world data
Published in Journal of Biomedical Informatics, 2024
The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains.
talks
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
Published:
This is a presentation of the study “Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)”
Preconference Course: Artificial Intelligence for Pharmacoepidemiology Research: An Introduction (Types of machine learning methods and algorithms)
Published:
This is a course about foundation of machine learning methods.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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