ArticleScientific reports2024
Fairness and bias correction in machine learning for depression prediction across four study populations.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Analysis of postpartum and other depression detection using machine learning and deep learning techniques: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Explainable Machine Learning for Predicting Student Depression Risk.Bioengineering (Basel, Switzerland) · 2026Article
- AI Mental Health in Asia: Cultural Misalignment, Clinical Risks and the Critical Role of Psychiatric Nursing.International journal of mental health nursing · 2026Article
- Article
- Dataset effects outweigh algorithmic effects in determining fairness of healthcare machine learning.NPJ digital medicine · 2026Article
- Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability.JMIR medical informatics · 2026Article
- Fairness analysis of machine learning predictions of aggression in acute psychiatric care.Npj mental health research · 2026Article
- Addressing the balance between fairness and performance in glioma grade prediction using bias mitigation techniques.Scientific reports · 2026Article
- Artificial intelligence, autism care, and health equity: a public health narrative review.Frontiers in public health · 2026Review
- Evaluating the impact of data biases on algorithmic fairness and clinical utility of machine learning models for prolonged opioid use prediction.JAMIA open · 2025Article
- Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare.Journal of healthcare informatics research · 2025Article
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ (Clinical research ed.) · 2025Article
- Data Hazards as An Ethical Toolkit for Neuroscience.Neuroethics · 2025Article
- Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.Neuro-oncology · 2024Review
- Evaluating and mitigating unfairness in multimodal remote mental health assessments.PLOS digital health · 2024Article
- Fair Canonical Correlation Analysis.Advances in neural information processing systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.
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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.