A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.

Journal: BMC medical informatics and decision making
Published Date: Jun 7, 2024

Abstract

BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.

Authors

  • Haoran Chen
    Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, 77843.
  • Fengchun Yang
    Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China.
  • Yifan Duan
    Beijing Key Laboratory of Big Data Technology for Food Safety, School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
  • Lin Yang
    National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Jiao Li
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301 China yinhao@scsio.ac.cn.

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