ArticleCirculation. Heart failure2022
Improving Fairness in the Prediction of Heart Failure Length of Stay and Mortality by Integrating Social Determinants of Health.
Article in Circulation. Heart failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.International journal of medical informatics · 2026Article
- Feasibility Assessment of Extracting Social Determinants of Health From Electronic Medical Records: A Multicenter Study Across Chinese Healthcare Institutions.Health care science · 2026Article
- Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction.JAMIA open · 2026Article
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.Current heart failure reports · 2026Review
- Article
- Beyond Composite Indices: Comprehensive Social Determinants Improve Heart Failure Readmission Prediction.Journal of the American Heart Association · 2026Article
- Risk-Adjusted Excess Length of Stay for Patients With Heart Failure Across Facilities: A Large US Cohort Study.Journal of the American Heart Association · 2026Article
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Addressing the balance between fairness and performance in glioma grade prediction using bias mitigation techniques.Scientific reports · 2026Article
- Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records.Journal of healthcare informatics research · 2026Article
- Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework.medRxiv : the preprint server for health sciences · 2025Article
- A New Risk Score Based on Machine Learning in Patients with Acute Heart Failure: The ML-HF score.Arquivos brasileiros de cardiologia · 2025Article
- Great debate: artificial intelligence will replace much of what cardiologists do.European heart journal · 2025Article
- Continuous vital sign monitoring for predicting hospital length of stay: a feasibility study in chronic obstructive pulmonary disease and chronic heart failure patients.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Validation of the American Heart Association Predicting Risk of Cardiovascular Disease Events Equations in Diverse Socioeconomic Groups: The All of Us Cohort.Journal of the American Heart Association · 2025Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health.Journal of medical Internet research · 2025Article
- The outcome prediction method of football matches by the quantum neural network based on deep learning.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMachine learning (ML) approaches have been broadly applied to the prediction of length of stay and mortality in hospitalized patients. ML may also reduce societal health burdens, assist in health resources planning and improve health outcomes. However, the fairness of these ML models across ethnoracial or socioeconomic subgroups is rarely assessed or discussed. In this study, we aim (1) to quantify the algorithmic bias of ML models when predicting the probability of long-term hospitalization or in-hospital mortality for different heart failure (HF) subpopulations, and (2) to propose a novel method that can improve the fairness of our models without compromising predictive power.
methodsWe built 5 ML classifiers to predict the composite outcome of hospitalization length-of-stay and in-hospital mortality for 210 368 HF patients extracted from the Get With The Guidelines-Heart Failure registry data set. We integrated 15 social determinants of health variables, including the Social Deprivation Index and the Area Deprivation Index, into the feature space of ML models based on patients' geographies to mitigate the algorithmic bias.
resultsThe best-performing random forest model demonstrated modest predictive power but selectively underdiagnosed underserved subpopulations, for example, female, Black, and socioeconomically disadvantaged patients. The integration of social determinants of health variables can significantly improve fairness without compromising model performance.
conclusionsWe quantified algorithmic bias against underserved subpopulations in the prediction of the composite outcome for HF patients. We provide a potential direction to reduce disparities of ML-based predictive models by integrating social determinants of health variables. We urge fellow researchers to strongly consider ML fairness when developing predictive models for HF patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.