[Application of machine learning in clinical predictive models for infectious diseases: a review].

Journal: Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control
Published Date:

Abstract

Infectious diseases are one of the major threats to global public health. Inconvenience of diagnosis and treatment frequently causes misdiagnosis, missing diagnosis or overtreatment, resulting in serious clinical outcomes. As an important branch of artificial intelligence, machine learning has been widely used in multiple fields. Predictive models created based on patients' clinical characteristics, laboratory tests, and imaging examinations are effective for prediction and evaluation of clinical diagnosis, therapeutic efficacy and prognosis, as well as detection of outbreaks. Machine learning modeling has the advantages of high efficiency, high accuracy and interpretability as compared to traditional modeling approaches, which provides a new tool for diagnosis and treatment of infectious diseases. This review summarizes the advances of applications of machine learning in clinical predictive models for infectious diseases.

Authors

  • R Zheng
    Department of Laboratory Medicine, the First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu 210029, China.
  • G Liu
    Department of Infectious Diseases, Beijing Children's Hospital, Beijing, China. Electronic address: liugang10@hotmail.com.