ArticleBMC bioinformatics2022
Using machine learning to determine the correlation between physiological and environmental parameters and the induction of acute mountain sickness.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- An empirical study on the relationship between state anxiety, acute mountain sickness, oxygen saturation, and rating of perceived exertion among graduate students during high-altitude mountaineering.Frontiers in psychology · 2026Article
- Physiological responses to short-term high-altitude acclimatization: Insights from predictive modeling approaches.Physiological reports · 2026Article
- Baseline indices of cardiorespiratory fitness for predicting acute mountain sickness: an interpretable four-task ensemble model.Frontiers in public health · 2026Article
- Closing the loop: autonomous intelligent control for hypoxia pre-acclimatization and high-altitude health management.National science review · 2025Review
- Intelligent monitoring and individualized strategies for preventing altitude sickness during altitude training.Frontiers in physiology · 2025Review
- Article
- Review
- Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications.Frontiers in physiology · 2024Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundRecent studies on acute mountain sickness (AMS) have used fixed-location and fixed-time measurements of environmental and physiological variable to determine the influence of AMS-associated factors in the human body. This study aims to measure, in real time, environmental conditions and physiological variables of participants in high-altitude regions to develop an AMS risk evaluation model to forecast prospective development of AMS so its onset can be prevented.
resultsThirty-two participants were recruited, namely 25 men and 7 women, and they hiked from Cuifeng Mountain Forest Park parking lot (altitude: 2300 m) to Wuling (altitude: 3275 m). Regression and classification machine learning analyses were performed on physiological and environmental data, and Lake Louise Acute Mountain Sickness Scores (LLS) to establish an algorithm for AMS risk analysis. The individual R
conclusionThe experiment results indicate the use of machine learning multivariate analysis have higher AMS prediction accuracies than analyses utilizing single varieties. The developed AMS evaluation model can serve as a reference for the future development of wearable devices capable of providing timely warnings of AMS risks to hikers.
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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.