Development and validation of HBV surveillance models using big data and machine learning.

Autor:	Dong W; Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Da Roza CC; Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Cheng D; Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China., Zhang D; Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China., Xiang Y; Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China., Seto WK; Department of Medicine and State Key Laboratory of Liver Research, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.; Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China., Wong WCW; Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.; Department of Family Medicine and Primary Care, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China.
Jazyk:	angličtina
Zdroj:	Annals of medicine [Ann Med] 2024 Dec; Vol. 56 (1), pp. 2314237. Date of Electronic Publication: 2024 Feb 10.
DOI:	10.1080/07853890.2024.2314237
Abstrakt:	Background: The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. Methods: Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. Results: The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample ( n  = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). Conclusions: Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264).
Databáze:	MEDLINE
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