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

• URL: http://arxiv.org/abs/2412.00245v1
• Date: Fri, 29 Nov 2024 20:35:01 GMT
• Title: Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare
• Authors: Tianqi Shang, Weiqing He, Tianlong Chen, Ying Ding, Huanmei Wu, Kaixiong Zhou, Li Shen,
• Abstract summary: Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored.<n>This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG.
• Score: 47.23120247002356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formulation for graph embeddings, focusing on invariance with respect to sensitive SDoH information. Via employing a heterogeneous-GCN model for drug-disease link prediction, we detect biases related to various SDoH factors. To mitigate these biases, we propose a post-processing method that strategically reweights edges connected to SDoHs, balancing their influence on graph representations. This approach represents one of the first comprehensive investigations into fairness issues within biomedical knowledge graphs incorporating SDoH. Our work not only highlights the importance of considering SDoH in medical informatics but also provides a concrete method for reducing SDoH-related biases in link prediction tasks, paving the way for more equitable healthcare recommendations. Our code is available at \url{https://github.com/hwq0726/SDoH-KG}.

Related papers

• AI analysis of medical images at scale as a health disparities probe: a feasibility demonstration using chest radiographs [1.8351424954311537]
  Social determinants of health (SDOH) are domains frequently studied for potential association with health disparities. We developed a pipeline for using quantitative measures automatically extracted from medical images as inputs into health disparities index calculations. Large-scale AI analysis of medical images can serve as a probe for a novel data source for health disparities research.
  arXiv  Detail & Related papers  (2025-04-08T12:53:14Z)
• MedGNN: Capturing the Links Between Urban Characteristics and Medical Prescriptions [2.5415925266871184]
  We propose MedGNN, a graph neural network that integrates positional and locational node embeddings with urban characteristics in a graph neural network. MedGNN improved predictions by over 25% on average compared to baseline methods. These results demonstrate MedGNN's potential, and more broadly, of carefully applied machine learning to advance transdisciplinary public health research.
  arXiv  Detail & Related papers  (2025-04-07T05:35:16Z)
• DualMAR: Medical-Augmented Representation from Dual-Expertise Perspectives [20.369746122143063]
  We propose DualMAR, a framework that enhances prediction tasks through both individual observation data and public knowledge bases. By retrieving and angular coordinates upon polar space, DualMAR enables accurate predictions based on rich hierarchical and semantic embeddings from KG.
  arXiv  Detail & Related papers  (2024-10-25T20:25:22Z)
• Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs [33.755845172595365]
  Growing evidence suggests that social determinants of health (SDoH) affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. This study presents a novel, automated framework to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas.
  arXiv  Detail & Related papers  (2024-10-04T21:39:30Z)
• HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks [2.2026317523029193]
  HealthGAT is a graph attention network framework that generates embeddings from EHR. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. Our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications.
  arXiv  Detail & Related papers  (2024-03-26T22:17:01Z)
• Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification [45.65374554914359]
  We aim to achieve fact-checking of the knowledge embedded in biological graphs that were contrived from ChatGPT contents. We adopted a biological networks approach that enables the systematic interrogation of ChatGPT's linked entities. This study demonstrated high accuracy of aggregate disease-gene links relationships found in ChatGPT-generated texts.
  arXiv  Detail & Related papers  (2023-08-07T22:13:30Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [52.31710895034573]
  This work provides the first comprehensive review of healthcare knowledge graphs (HKGs) It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches. At the application level, we delve into the successful integration of HKGs across various health domains.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs [44.897533778944094]
  textscGraphCare is an open-world framework that uses external knowledge graphs to improve EHR-based predictions. Our method extracts knowledge from large language models (LLMs) and external biomedical KGs to build patient-specific KGs. textscGraphCare surpasses baselines in four vital healthcare prediction tasks.
  arXiv  Detail & Related papers  (2023-05-22T07:35:43Z)
• Systematic Design and Evaluation of Social Determinants of Health Ontology (SDoHO) [19.90090257979115]
  Social determinants of health (SDoH) have a significant impact on health outcomes and well-being. We propose an SDoH ontology (SDoHO) which represents fundamental SDoH factors and their relationships in a standardized and measurable way.
  arXiv  Detail & Related papers  (2022-12-04T22:23:30Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Heterogeneous Similarity Graph Neural Network on Electronic Health Records [74.66674469510251]
  We propose Heterogeneous Similarity Graph Neural Network (HSGNN) to analyze EHRs with a novel heterogeneous GNN. Our framework consists of two parts: one is a preprocessing method and the other is an end-to-end GNN. The GNN takes all homogeneous graphs as input and fuses all of them into one graph to make a prediction.
  arXiv  Detail & Related papers  (2021-01-17T23:14:29Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.