Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review
Notice bibliographique
Résumé
AI and predictive analytics have increased the speed of innovation in medicine. If left unchecked, however, algorithmic bias can exacerbate health disparities across race, class, or gender. Early bias mitigation literature has focused on addressing bias in the preparation and development phases of the algorithm life cycle (pre- and in-processing). Post-processing methods, applied at the point of implementation, are less computationally intensive and do not require re-building or training the model, allowing lower-resourced health systems to improve bias in off-the-shelf binary classification models, which are increasingly common within electronic medical records. This umbrella review sought to identify post-processing bias mitigation methods and tools applicable to binary healthcare classification models in healthcare and summarize bias reduction effectiveness and accuracy loss. This review was registered with PROSPERO and reported according to PRISMA 2020. PubMed and Scopus were searched in December 2023 for English-language reviews published post-2013 using an expanded search string from previous work on machine learning bias. Eligibility criteria followed the PICOT framework. Reviews were screened independently by two authors. Data were extracted from reviews using the Joanna Briggs Institute Extraction Form for Review of Reviews, as well as from cited studies (hence, an “extended” umbrella review). Quality was assessed using the Critical Appraisal Checklist for Systematic Reviews. Evidence was synthesized by mitigation method and effectiveness. Searches yielded 184 records. After duplicate removal, title/abstract, and full text screening, 11 reviews were included, citing 16 eligible studies. Post-processing methods tested included threshold adjustment (9 studies, cited by 8 reviews), reject option classification (6 studies, cited by 4 reviews), and calibration (5 studies, cited by 4 reviews). Threshold adjustment reduced bias across 8/9 trials; reject option classification and calibration reduced bias in approximately half of trials (5/8 and 4/8). Results were reported with heterogeneous fairness and accuracy metrics, making comparison difficult. A lack of effectiveness evaluation was noted across reviews. Four reviews identified 16 software libraries for addressing bias. Quality of the majority of reviews was weak due to inadequate reporting on methods. Threshold adjustment showed significant promise in post-processing bias mitigation for healthcare algorithms, followed by reject option classification and calibration. Future research should empirically compare post-processing methods on binary classification models using real-world healthcare data. As commercial algorithms proliferate, health systems require proven, achievable strategies to maximize fairness.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».