Abstract

High-altitude illnesses, encompassing a spectrum of health threats including Acute Mountain Sickness (AMS), pose significant challenges to individuals exposed to high altitude environments, necessitating effective prophylaxis and immediate management. Given the variability in individual responses to these conditions, accurate prediction of high-altitude illnesses onset is of paramount importance. This review systematically consolidates recent advancements in research on predicting AMS by evaluating existing cohort data, predictive models, and methodologies, while also delving into the application of emerging technologies. Through a thorough analysis of scholarly literature, we discuss traditional prediction methods anchored in physiological parameters (e.g., heart rate, respiratory frequency, blood pressure) and biochemical markers, as well as the integration and utility of novel technologies such as biosensors, genetic testing, and artificial intelligence within high-altitude prediction research. While conventional pre-diction techniques have been extensively used, they are often constrained by limitations in accuracy, reliability, and multifactorial influences. The advent of these innovative technologies holds promise for more precise individual risk assessments and personalized preventive and therapeutic strategies across various forms of AMS. Future research endeavors must pivot decisively towards the meticulous identification and stringent validation of innovative predictive biomarkers and models. This strategic re-direction should catalyze intensified interdisciplinary cooperation to significantly deepen our mechanistic insights into the pathogenesis of AMS while refining existing prediction methodologies. These groundbreaking advancements harbor the potential to fundamentally transform preventive and therapeutic frameworks for high-altitude illnesses, ultimately securing augmented safety standards and wellbeing for individuals operating at elevated altitudes with far-reaching global implications.

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