Julia Chaves Neuenschwander Magalhães, Alexandre Dias Porto Chiavegatto Filho
Abstract
Machine learning (ML) algorithms are increasingly used in healthcare to support clinical decision-making. While models with similar overall performance are often considered interchangeable for deployment, they may produce divergent predictions, a phenomenon known as algorithmic multiplicity.
Introduction
In recent years, rapid advances in artificial intelligence (AI), especially in its machine learning (ML) subfield, have driven the increasing adoption of these technologies in medicine [1], particularly in the development of computer vision systems for medical imaging analysis [2].
Methods
A multicohort retrospective study, aiming to investigate the effects of algorithmic multiplicity on mortality prediction for covid-19 in hospitals across Brazil, was conducted with 4,377 adult patients (≥18 years) with RT-PCR–confirmed covid-19, who were followed between March and August 2020.
Results
As presented in Table 1, the analyzed sample consisted of 4,377 adult patients with RT-PCR confirmed covid-19 admitted across five different hospitals. The list of hospitals and their respective locations is presented in S1 Table.
Discussion
This study demonstrates that all five evaluated algorithms achieved consistently high performance across diverse clinical settings, with TabPFN emerging as the top-performing and most stable model in most scenarios. Notably, TabPFN required no hyperparameter tuning and exhibited the lowest performance variance across hospitals.
Conclusion
Using a large, multicentric cohort of hospitalized covid-19 patients in Brazil, this study provides empirical evidence that ML models with comparable overall performance can produce substantially divergent predictions at both the individual and subgroup levels when applied to mortality prediction.
Citation: Magalhães JCN, Chiavegatto Filho ADP (2026) Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients. PLoS One 21(3): e0344354. https://doi.org/10.1371/journal.pone.0344354
Editor: Marcela Pagano, Federal University of Minas Gerais, Brazil
Received: September 21, 2025; Accepted: February 19, 2026; Published: March 6, 2026
Copyright: © 2026 Magalhães, Chiavegatto Filho. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data used in this study were obtained from five distinct hospitals and are not publicly available due to restrictions imposed by the Brazilian General Data Protection Law (Lei Geral de Proteção de Dados – LGPD), which safeguards individual privacy even in de-identified datasets. Access to the data may be granted upon reasonable request to the Laboratory of Big Data and Predictive Analysis in Healthcare at the School of Public Health, University of São Paulo (labdaps@usp.br). Reasonable requests must be for legitimate research purposes and must include a clear plan for maintaining data confidentiality and complying with ethical standards. Analytical code can be found on https://github.com/labdaps/iacovbr_predictive_divergence.
Funding: Funding for this research was provided by the National Council for Scientific and Technological Development, under grant No. 444610/2024-3.
Competing interests: The authors have declared that no competing interests exist.
