Scholarlink

<p>This study developed and validated a machine learning (ML) model to predict in-hospital cardiac mortality in 18,727 atrial fibrillation (AF) patients using electronic medical record data. Four ML algorithms—random forest, extreme gradient boosting (XGBoost), deep neural network, and logistic regression—were applied to 79 clinical variables, including demographics, vital signs, comorbidities, lifestyle factors, and laboratory parameters. The XGBoost model achieved the best performance, with an area under the curve of 0.964 ± 0.014 in the training set and 0.932 ± 0.057 in the validation set, alongside precision, accuracy, and recall of 0.909 ± 0.021, 0.910 ± 0.021, and 0.897 ± 0.038, respectively. Shapley Additive Explanations identified key predictors such as thyroid function indices (e.g., total triiodothyronine, total thyroxine), procalcitonin, N-terminal pro-brain natriuretic peptide, and international normalized ratio. This interpretable model holds promise for improving early risk stratification and individualized care in AF patients. Prospective, multi-center validation is needed to confirm its generalizability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月12日

查看全文
  • Huasheng Lv,
  • Xuehua Bi,
  • Shuai Shang,
  • Meng Wei,
  • Xianhui Zhou,
  • Kai Wang,
  • Baopeng Tang,
  • Yanmei Lu

摘要

This study developed and validated a machine learning (ML) model to predict in-hospital cardiac mortality in 18,727 atrial fibrillation (AF) patients using electronic medical record data. Four ML algorithms—random forest, extreme gradient boosting (XGBoost), deep neural network, and logistic regression—were applied to 79 clinical variables, including demographics, vital signs, comorbidities, lifestyle factors, and laboratory parameters. The XGBoost model achieved the best performance, with an area under the curve of 0.964 ± 0.014 in the training set and 0.932 ± 0.057 in the validation set, alongside precision, accuracy, and recall of 0.909 ± 0.021, 0.910 ± 0.021, and 0.897 ± 0.038, respectively. Shapley Additive Explanations identified key predictors such as thyroid function indices (e.g., total triiodothyronine, total thyroxine), procalcitonin, N-terminal pro-brain natriuretic peptide, and international normalized ratio. This interpretable model holds promise for improving early risk stratification and individualized care in AF patients. Prospective, multi-center validation is needed to confirm its generalizability.