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20212024
most citedDeep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

160 citations · 491 across the 22 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CY2024★ 4 cited

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction

Siqi Li, Qiming Wu, Xin Li +10

Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research, where ensuring equity and fairness is paramount. While large-scale healthcare data exi…

cs.LG2024★ 4 cited

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis

Siqi Li, Xin Li, Kunyu Yu +12

Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effecti…

cs.AI2024★ 6 cited

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

Rui Yang, Yilin Ning, Emilia Keppo +6

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-a…

cs.AI2024

Towards Clinical AI Fairness: Filling Gaps in the Puzzle

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong +16

The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive st…

cs.LG2024★ 1 cited

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

Mingxuan Liu, Yilin Ning, Yuhe Ke +5

The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fa…

cs.AI2024★ 1 cited

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

Siqi Li, Yuqing Shang, Ziwen Wang +7

Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain dise…

cs.LG2024★ 3 cited

Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission

Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty +5

Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analys…