ArticleHigh blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension2024
Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.
Article in High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Burden and risk factors of depression in seniors from 1990 to 2021: a multi-database study based on EMR mining methods.Translational psychiatry · 2025Article
- Global, regional, and national trends in type 2 diabetes mellitus burden among adolescents and young adults aged 10-24 years from 1990 to 2021: a trend analysis from the Global Burden of Disease Study 2021.World journal of pediatrics : WJP · 2025Article
- Natural Language Processing (NLP): Identifying Linguistic Gender Bias in Electronic Medical Records (EMRs).Journal of patient experience · 2025Article
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Abstract
introductionThere are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients.
aimWe intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that monitors mortality based on heavy metal exposure among hypertension patients.
methodsOur datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2018). We developed 5 ML models for mortality prediction among hypertension patients by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, we chose the optimally performing model after parameter adjustment by genetic algorithm (GA) for prediction. Finally, in order to visualize the model's ability to make decisions, we used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 2347 participants in total.
resultsA best-performing eXtreme Gradient Boosting (XGB) with GA for mortality prediction among hypertension patients by 13 heavy metals was selected (AUC 0.959; 95% CI 0.953-0.965; accuracy 96.8%). According to sum of SHAP values, cadmium (0.094), cobalt (2.048), lead (1.12), tungsten (0.129) in urine, and lead (2.026), mercury (1.703) in blood positively influenced the model, while barium (- 0.001), molybdenum (- 2.066), antimony (- 0.398), tin (- 0.498), thallium (- 2.297) in urine, and selenium (- 0.842), manganese (- 1.193) in blood negatively influenced the model.
conclusionsHypertension 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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