Construction of metal interpretable scoring system and identification of tungsten as a novel risk factor in COPD

Xuehai Wang

Abstract:

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