ArticleScientific reports2023
Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development and validation of a machine learning model for predicting liver metastasis in pancreatic cancer: a retrospective cohort study.Gland surgery · 2026Article
- Baseline indices of cardiorespiratory fitness for predicting acute mountain sickness: an interpretable four-task ensemble model.Frontiers in public health · 2026Article
- Multi-cohort transcriptomics integration for building and validating a diagnostic model of peripheral blood septic shock.Frontiers in immunology · 2026Article
- Clinical decision support for vestibular diagnosis: large-scale machine learning with lived experience coaching.NPJ digital medicine · 2025Article
- Acute mountain sickness prediction: a concerto of multidimensional phenotypic data and machine learning strategies in the framework of predictive, preventive, and personalized medicine.The EPMA journal · 2025Article
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Article
- Altitude illnesses.Nature reviews. Disease primers · 2024Review
- Transcriptomic signatures of severe acute mountain sickness during rapid ascent to 4,300 m.Frontiers in physiology · 2024Article
- Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications.Frontiers in physiology · 2024Review
- Machine learning potential predictor of idiopathic pulmonary fibrosis.Frontiers in genetics · 2024Article
- Potential plasma biomarkers at low altitude for prediction of acute mountain sickness.Frontiers in immunology · 2023Article
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Authors and funding
8 authors.
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Abstract
Severe acute mountain sickness (sAMS) can be life-threatening, but little is known about its genetic basis. The study was aimed to explore the genetic susceptibility of sAMS for the purpose of prediction, using microarray data from 112 peripheral blood mononuclear cell (PBMC) samples of 21 subjects, who were exposed to very high altitude (5260 m), low barometric pressure (406 mmHg), and hypobaric hypoxia (VLH) at various timepoints. We found that exposure to VLH activated gene expression in leukocytes, resulting in an inverted CD4/CD8 ratio that interacted with other phenotypic risk factors at the genetic level. A total of 2286 underlying risk genes were input into the support vector machine recursive feature elimination (SVM-RFE) system for machine learning, and a model with satisfactory predictive accuracy and clinical applicability was established for sAMS screening using ten featured genes with significant predictive power. Five featured genes (EPHB3, DIP2B, RHEBL1, GALNT13, and SLC8A2) were identified upstream of hypoxia- and/or inflammation-related pathways mediated by microRNAs as potential biomarkers for sAMS. The established prediction model of sAMS holds promise for clinical application as a genetic screening tool for sAMS.
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