机器学习技术在环境健康领域中的应用进展

摘要   随着环境和健康研究数据共享及可用性的不断提升，涉及环境与人体健康的数据集数量急剧增加。然而，这些环境健康大型数据集多样且复杂，传统的流行病学和环境健康模型难以有效分析，因此催生了一个环境健康研究的新手段。人工智能（AI）技术在环境健康领域的应用正迅速发展，为新污染物筛选和毒性预测、生物监测、风险评估和健康保护提供了新颖且强大的工具。其中，先进的机器学习（ML）算法能够揭示人类难以察觉的规律，在生物标志物识别、疾病预防和环境工程优化等方面表现出重要潜力，为环境健康研究和技术创新提供新的思路和突破口。然而，ML技术在环境健康领域的应用仍面临数据质量、模型解释性以及跨学科合作等挑战。本文将综述ML技术在环境健康领域的最新应用进展，探讨其优势、挑战以及未来的发展方向，以期为环境保护和公共健康领域的研究和实践提供有价值的参考。

Abstract   As the data sharing and availability in environmental and health research continue to improve, the number of large datasets for environmental and human health has increased dramatically. However, these large environmental health datasets are diverse and complex, and traditional epidemiological and environmental health models are difficult to effectively analyze, leading to the development of a new approach to environmental health research. The application of artificial intelligence (AI) technology in environmental health is rapidly developing, providing novel and powerful tools for new pollutant screening and toxicity prediction, biomonitoring, risk assessment, and health protection. Among them, advanced machine learning (ML) algorithms can reveal laws that are difficult for humans to detect, showing important potential in biomarker identification, disease prevention, and environmental engineering optimization. This can provide new ideas and breakthroughs for environmental health research and technological innovation. However, the application of ML technology in the field of environmental health still faces challenges such as data quality, model interpretability, and interdisciplinary cooperation. This paper will review the latest progress in the application of ML technology in the field of environmental health, discuss its advantages, challenges, and future development directions, with the aim of providing valuable references for research and practice in the fields of environmental protection and public health.

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