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Purpose <p>Hematological Signatures are vital in clinical-decision making for complex disorders, and have proven transformative for both medicine and research. Advances in computational techniques have enabled improved analysis of hematological data, enhanced diagnostic accuracy, efficient management and treatment, beyond the limitations of traditional approaches. High-altitude exposure imposes significant physiological stress to human body, inducing systemic and cellular adaptations, among which hematological changes are particularly prominent. Despite extensive data available, a quantitative framework for assessing acclimatization and its inter-individual variability remains lacking. </p> Method <p> This review compiles evidence on the integration of Machine Learning (ML) with hematological signatures, drawing methodological insights from its successful application for complex, multifactorial conditions. This is followed by an in-depth review of altitude-induced hematological changes and, influenced by factors such as ethnicity, exposure duration, geolocation, demographics and elevation, to understand their role in acclimatisation mechanism. </p> Findings <p> Integration of hematology with ML has demonstrated significant potential in improving diagnostic precision and management of complex disorders. Extensive hematological data related to high-altitude exposure and acclimatization reflects variability in parameters like hemoglobin, hematocrit, plasma volume, etc., reflects the heterogenous nature of acclimatization across populations. </p> Conclusion <p> This review consolidates recent advances in the application of ML and hematology, and building on these insights, we propose a hematology-informed, semi-quantitative framework that integrates hematological signatures with ML-based analysis to support phase-wise and longitudinal interpretation of acclimatization-related variability. This integrative approach aims to contribute to a better understanding of acclimatization heterogeneity in populations exposed to high-altitude.</p>

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Developing High-Altitude Acclimatization Metrics from Hematological Signatures

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Sandhya Pathak,
  • Pankaj Khurana

摘要

Purpose

Hematological Signatures are vital in clinical-decision making for complex disorders, and have proven transformative for both medicine and research. Advances in computational techniques have enabled improved analysis of hematological data, enhanced diagnostic accuracy, efficient management and treatment, beyond the limitations of traditional approaches. High-altitude exposure imposes significant physiological stress to human body, inducing systemic and cellular adaptations, among which hematological changes are particularly prominent. Despite extensive data available, a quantitative framework for assessing acclimatization and its inter-individual variability remains lacking.

Method

This review compiles evidence on the integration of Machine Learning (ML) with hematological signatures, drawing methodological insights from its successful application for complex, multifactorial conditions. This is followed by an in-depth review of altitude-induced hematological changes and, influenced by factors such as ethnicity, exposure duration, geolocation, demographics and elevation, to understand their role in acclimatisation mechanism.

Findings

Integration of hematology with ML has demonstrated significant potential in improving diagnostic precision and management of complex disorders. Extensive hematological data related to high-altitude exposure and acclimatization reflects variability in parameters like hemoglobin, hematocrit, plasma volume, etc., reflects the heterogenous nature of acclimatization across populations.

Conclusion

This review consolidates recent advances in the application of ML and hematology, and building on these insights, we propose a hematology-informed, semi-quantitative framework that integrates hematological signatures with ML-based analysis to support phase-wise and longitudinal interpretation of acclimatization-related variability. This integrative approach aims to contribute to a better understanding of acclimatization heterogeneity in populations exposed to high-altitude.