Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension

Hypertension patients' mortality associated with heavy metal exposure was predicted by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Cadmium, cobalt, lead, tungsten in urine, and mercury in blood are positively correlated with mortality, while barium, molybdenum, antimony, tin, thallium in urine, and lead, selenium, manganese in blood is negatively correlated with mortality.

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Publication Details

Journal
High Blood Pressure & Cardiovascular Prevention
Published
2024-08-12
DOI
https://doi.org/10.1007/s40292-024-00666-w
Citations
4
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
1.39
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article

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension

Site Xu, Mu Sun
4 citations
High Blood Pressure & Cardiovascular Prevention
Machine Learning in Healthcare
1.39
article

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension

Site Xu, Mu Sun
article en
4 citations

Abstract

Hypertension patients' mortality associated with heavy metal exposure was predicted by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Cadmium, cobalt, lead, tungsten in urine, and mercury in blood are positively correlated with mortality, while barium, molybdenum, antimony, tin, thallium in urine, and lead, selenium, manganese in blood is negatively correlated with mortality.

High Blood Pressure & Cardiovascular PreventionVol. 31(5)
Ruijin Hospital (CN)
Good health and well-being
Openalex Percentile: Top 16%
Machine Learning in Healthcare
1.39
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