Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Mingxuan, Ning, Yilin, Ke, Yuhe, Shang, Yuqing, Chakraborty, Bibhas, Ong, Marcus Eng Hock, Vaughan, Roger, Liu, Nan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
by: Wang, Ziwen, et al.
Published: (2024)

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
by: Li, Siqi, et al.
Published: (2024)

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
by: Ning, Yilin, et al.
Published: (2023)

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
by: Xie, Feng, et al.
Published: (2021)

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
by: Li, Siqi, et al.
Published: (2024)

Towards Clinical AI Fairness: Filling Gaps in the Puzzle
by: Liu, Mingxuan, et al.
Published: (2024)

A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
by: Ning, Yilin, et al.
Published: (2022)

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
by: Xie, Feng, et al.
Published: (2021)

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
by: Liu, Mingxuan, et al.
Published: (2022)

Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications
by: Liu, Mingxuan, et al.
Published: (2025)

Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomes
by: Wang, Ziwen, et al.
Published: (2026)

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
by: Yuan, Han, et al.
Published: (2021)

seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness
by: Ning, Yilin, et al.
Published: (2025)

Evaluating Fair Feature Selection in Machine Learning for Healthcare
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024)

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
by: Yesmin, Farjana
Published: (2026)

Fair Machine Learning in Healthcare: A Review
by: Feng, Qizhang, et al.
Published: (2022)

Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
by: Li, Siqi, et al.
Published: (2023)

Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2025)

FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records
by: Wang, Yuqing, et al.
Published: (2024)

RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
by: Yang, Yang, et al.
Published: (2025)

Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
by: Liu, Mingxuan, et al.
Published: (2025)

Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study
by: Valentine, Alissa A., et al.
Published: (2024)

A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare
by: Aljohani, Manar, et al.
Published: (2025)

An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness
by: Huang, Bin, et al.
Published: (2024)

Attention-Guided Fair AI Modeling for Skin Cancer Diagnosis
by: Zhu, Mingcheng, et al.
Published: (2025)

Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare
by: Shang, Tianqi, et al.
Published: (2024)

Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action
by: Ong, Jasmine Chiat Ling, et al.
Published: (2025)

Learning Interpretable Fair Representations
by: Wang, Tianhao, et al.
Published: (2024)

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints
by: Kumar, Vagish, et al.
Published: (2026)

Fairness Evaluation with Item Response Theory
by: Xu, Ziqi, et al.
Published: (2024)

Trust@Health: A Trust-Based Multilayered Network for Scalable Healthcare Service Management
by: Gayen, Avijit, et al.
Published: (2025)

Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling
by: Wang, Xiaoyang, et al.
Published: (2025)

Towards Public Administration Research Based on Interpretable Machine Learning
by: Liu, Zhanyu, et al.
Published: (2026)

Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning
by: Weerts, Hilde, et al.
Published: (2022)

Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop
by: Bürger, Valerie, et al.
Published: (2026)

A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition
by: Cherkassky, Vladimir, et al.
Published: (2024)

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
by: Li, Siqi, et al.
Published: (2024)

What is Fair? Defining Fairness in Machine Learning for Health
by: Gao, Jianhui, et al.
Published: (2024)

Towards Harmless Rawlsian Fairness Regardless of Demographic Prior
by: Wang, Xuanqian, et al.
Published: (2024)

Evac-Cast: An Interpretable Machine-Learning Framework for Evacuation Forecasts Across Hurricanes and Wildfires
by: Li, Bo, et al.
Published: (2025)