Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shang, Tianqi, He, Weiqing, Chen, Tianlong, Ding, Ying, Wu, Huanmei, Zhou, Kaixiong, Shen, Li
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
by: Shang, Tianqi, et al.
Published: (2024)

SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)
by: Consoli, Bernardo, et al.
Published: (2024)

FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
by: Luo, Yiqin, et al.
Published: (2024)

FairWire: Fair Graph Generation
by: Kose, O. Deniz, et al.
Published: (2024)

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

The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs
by: Kraft, Angelie, et al.
Published: (2022)

From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment
by: Zezulka, Sebastian, et al.
Published: (2024)

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

Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
by: He, Erhu, et al.
Published: (2024)

Integrating Social Determinants of Health and Established Risk Factors to Predict Cardiovascular Disease Risk Among Healthy Older Adults
by: Achamyeleh Birhanu Teshale, et al.
Published: (2025)

What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias
by: Mohammadshahi, Aida, et al.
Published: (2024)

Position: Open and Closed Large Language Models in Healthcare
by: Xu, Jiawei, et al.
Published: (2025)

Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
by: Khan, Sharim, et al.
Published: (2025)

Fairness and/or Privacy on Social Graphs
by: Surma, Bartlomiej, et al.
Published: (2025)

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by: Liu, Mingxuan, et al.
Published: (2024)

SARHAchat: An LLM-Based Chatbot for Sexual and Reproductive Health Counseling
by: Yang, Jiaye, et al.
Published: (2025)

Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
by: Zhang, Yixuan, et al.
Published: (2024)

Cross-Lingual Multi-Hop Knowledge Editing
by: Khandelwal, Aditi, et al.
Published: (2024)

FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning
by: Roy, Arjun, et al.
Published: (2023)

Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
by: Li, Xinyue, et al.
Published: (2023)

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

Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios
by: Tan, Bryan Chen Zhengyu, 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)

FairAdapter: Detecting AI-generated Images with Improved Fairness
by: Ding, Feng, et al.
Published: (2024)

Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
by: Li, Chengyu, et al.
Published: (2024)

Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration
by: Ding, Kexin, et al.
Published: (2025)

Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models
by: Lin, Yi-Cheng, et al.
Published: (2024)

CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experiments
by: Liu, Xuan, et al.
Published: (2025)

Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness
by: Simson, Jan, et al.
Published: (2025)

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

Disclosure and Evaluation as Fairness Interventions for General-Purpose AI
by: Raman, Vyoma, et al.
Published: (2025)

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

Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
by: Zhang, He, et al.
Published: (2023)

The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairness
by: Ferrara, Emilio
Published: (2023)

AI Gender Bias, Disparities, and Fairness: Does Training Data Matter?
by: Latif, Ehsan, et al.
Published: (2023)

The AI Fairness Myth: A Position Paper on Context-Aware Bias
by: Nepomuceno, Kessia, et al.
Published: (2025)

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
by: Fabris, Alessandro, et al.
Published: (2023)

Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis
by: Lu, Hsiao-Ying, et al.
Published: (2025)

You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time
by: Han, Xiaotian, et al.
Published: (2025)

Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation
by: Wang, Yifei, et al.
Published: (2023)