Application of machine learning in clinical predictive models for infectious diseases: a review
10.16250/j.32.1374.2023084
- VernacularTitle:机器学习在感染性疾病临床预测模型中的应用进展
- Author:
Ruiying ZHENG
1
,
2
;
Genyan LIU
1
,
2
Author Information
1. Department of Laboratory Medicine, the First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu 210029, China
2. Branch of National Clinical Research Center for Laboratory Medicine, the First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Publication Type:Journal Article
- Keywords:
Infectious disease;
Machine learning;
Predictive model
- From:
Chinese Journal of Schistosomiasis Control
2023;35(3):317-321
- CountryChina
- Language:Chinese
-
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.
