Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China.
Nantong Hospital to Nanjing University of Chinese Medicine, China.
Ecotoxicol Environ Saf. 2024 Sep 15;283:116842. doi: 10.1016/j.ecoenv.2024.116842. Epub 2024 Aug 5.
Numerous studies have highlighted the correlation between metal intake and deteriorated pulmonary function, emphasizing its pivotal role in the progression of Chronic Obstructive Pulmonary Disease (COPD). However, the efficacy of traditional models is often compromised due to overfitting and high bias in datasets with low-level exposure, rendering them ineffective in delineating the contemporary risk trends associated with pulmonary diseases. To address these limitations, we embarked on developing advanced, interpretable models, crucial for elucidating the intricate mechanisms of metal toxicity and enriching the domain knowledge embedded in toxicity models. In this endeavor, we scrutinized extensive, long-term metal exposure datasets from NHANES to explore the interplay between metal and pulmonary functionality. Employing a variety of machine-learning approaches, we opted for the "Mixer of Experts" model for its proficiency in identifying a myriad of toxicological trends and sensitivities. We conceptualized and illustrated the TSAP (Toxicity Score at Population-level), a metal interpretable scoring system offering performance nearly equivalent to the amalgamation of standard interpretable methods addressing the "black box" conundrum. This streamlined, bifurcated procedural analysis proved instrumental in discerning established risk factors, thereby uncovering Tungsten as a novel contributor to COPD risk. SYNOPSIS: TSAP achieved satisfied performance with transparent interpretability, suggesting tungsten intake need further action for COPD prevention.
许多研究都强调了金属摄入与肺功能恶化之间的相关性，突出了其在慢性阻塞性肺疾病（COPD）进展中的关键作用。然而，由于传统模型在低水平暴露数据集中存在过度拟合和高偏差，其有效性往往受到限制，无法描绘与肺部疾病相关的当代风险趋势。为了解决这些限制，我们着手开发先进的、可解释的模型，这些模型对于阐明金属毒性的复杂机制和丰富毒性模型中嵌入的领域知识至关重要。在这项努力中，我们仔细研究了来自 NHANES 的广泛的、长期的金属暴露数据集，以探讨金属与肺功能之间的相互作用。我们使用了各种机器学习方法，选择了“专家混合器”模型，因为它能够识别出许多毒理学趋势和敏感性。我们提出并说明了 TSAP（人群水平的毒性评分），这是一种金属可解释的评分系统，其性能几乎等同于合并标准可解释方法来解决“黑箱”难题。这种简化的、分叉的程序分析有助于辨别已确定的风险因素，从而发现钨是 COPD 风险的一个新因素。概述：TSAP 具有透明的可解释性和令人满意的性能，表明摄入钨需要采取进一步行动来预防 COPD。
