Development and validation of an AI approach for hyperbilirubinemia assessment from routine haematology in high-altitude settings.
4 affiliations
- 1Department of Laboratory Medicine, The Affiliated Dazu’s Hospital of Chongqing Medical University, Chongqing, China
- 2College of Artificial Intelligence Medicine, Chongqing Medical University, Chongqing, China
- 3Department of Blood Transfusion, The Affiliated Dazu’s Hospital of Chongqing Medical University, Chongqing, China
- 4Department of Blood Transfusion, The People’s Hospital of Chaya County, Tibet, China
Abstract
Background
Hypobaric hypoxia at extreme altitudes induces compensatory erythropoiesis, alters bilirubin metabolism, and increases the risk of high-altitude hyperbilirubinemia (HBIL). Conventional serum bilirubin screening in these regions is frequently constrained by limited medical infrastructure. This study aims to develop a machine learning pipeline that uses universally accessible routine haematological parameters to infer serum bilirubin status and stratify disease severity.Methods
We conducted a retrospective cohort study of permanent high-altitude residents (>2,900 m) to develop and validate diagnostic models using routine haematological parameters. We employed a Tabular Prior-Data Fitted Network for primary diagnosis and clinical severity stratification (latent vs. overt jaundice). We evaluated model performance using the area under the receiver operating characteristic curve (AUROC) and validated the approach in an independent temporal hold-out validation cohort. We interpreted feature importance using SHapley Additive exPlanations (SHAP).Results
The model exhibited strong discriminative capacity, achieving an AUROC of 0.918 for primary diagnosis and 0.888 for disease severity stratification in the internal validation set. It maintained predictive efficacy in the temporal hold-out validation cohort, with AUROC values of 0.923 and 0.905, respectively. Interpretability analysis revealed that the magnitude of compensatory erythrocytosis (Hb and Hct) was primarily associated with the initial onset of HBIL. Furthermore, erythrocyte morphological heterogeneity (RDW-SD) distinguished the transition to overt jaundice.Conclusions
This study establishes a scalable, artificial intelligence-driven framework for screening and risk-stratifying high-altitude HBIL using routine blood parameters. This approach provides a practical, low-cost strategy for proactive clinical management in resource-constrained extreme environments.
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BibTeX
@article{liu2026development,
title = {{Development and validation of an AI approach for hyperbilirubinemia assessment from routine haematology in high-altitude settings.}},
author = {Yulong Liu and Mao Huang and Juanjuan Mou and Dexi Yongzong and Bo Lin and Xuelian Zhou and Fan Li and Jin Li and Ruoying Huang},
journal = {Journal of global health},
year = {2026},
month = oct,
doi = {10.7189/jogh.16.04347},
url = {https://europepmc.org/article/MED/42960969},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development and validation of an AI approach for hyperbilirubinemia assessment from routine haematology in high-altitude settings.
AU - Yulong Liu
AU - Mao Huang
AU - Juanjuan Mou
AU - Dexi Yongzong
AU - Bo Lin
AU - Xuelian Zhou
AU - Fan Li
AU - Jin Li
AU - Ruoying Huang
PY - 2026
DA - 2026/10/30
JO - Journal of global health
DO - 10.7189/jogh.16.04347
UR - https://europepmc.org/article/MED/42960969
AB - Background Hypobaric hypoxia at extreme altitudes induces compensatory erythropoiesis, alters bilirubin metabolism, and increases the risk of high-altitude hyperbilirubinemia (HBIL). Conventional serum bilirubin screening in these regions is frequently constrained by limited medical infrastructure. This study aims to develop a machine learning pipeline that uses universally accessible routine haematological parameters to infer serum bilirubin status and stratify disease severity.Methods We conducted a retrospective cohort study of permanent high-altitude residents (>2,900 m) to develop and validate diagnostic models using routine haematological parameters. We employed a Tabular Prior-Data Fitted Network for primary diagnosis and clinical severity stratification (latent vs. overt jaundice). We evaluated model performance using the area under the receiver operating characteristic curve (AUROC) and validated the approach in an independent temporal hold-out validation cohort. We interpreted feature importance using SHapley Additive exPlanations (SHAP).Results The model exhibited strong discriminative capacity, achieving an AUROC of 0.918 for primary diagnosis and 0.888 for disease severity stratification in the internal validation set. It maintained predictive efficacy in the temporal hold-out validation cohort, with AUROC values of 0.923 and 0.905, respectively. Interpretability analysis revealed that the magnitude of compensatory erythrocytosis (Hb and Hct) was primarily associated with the initial onset of HBIL. Furthermore, erythrocyte morphological heterogeneity (RDW-SD) distinguished the transition to overt jaundice.Conclusions This study establishes a scalable, artificial intelligence-driven framework for screening and risk-stratifying high-altitude HBIL using routine blood parameters. This approach provides a practical, low-cost strategy for proactive clinical management in resource-constrained extreme environments.
SN - 2047-2978
SN - 2047-2986
ER -