1
School of Medicine University of Electronic Science and Technology of China Chengdu 610072 Sichuan China
2
Institute for
Emergency and Disaster Medicine Sichuan Provincial People's Hospital University of Electronic Science and Technology of China
Chengdu 610072 Sichuan China
3
Sichuan Clinical Research Center for Emergency and Critical Care Sichuan Provincial People's
Hospital University of Electronic Science and Technology of China Chengdu 610072 Sichuan China
Abstract：The construction of clinical prediction models based on biochemical indicators has become an important direction in
medical research. This paper systematically reviews recent advances in the development of clinical prediction models based on
biochemical indicators and machine learning techniques with a focus on the key aspects of model construction highlighting the current
limitations of such models and proposing future directions for improvement. We enrolled 28 studies that are retrieved from PubMed
Embase Web of Science and China Knowledge Network CNKI databases summarizing and analyzing the modeling objectives
variable selection modeling methods and model evaluations of these studies. We found that most of the included studies were
small-sample single-center designs with a median sample size of 466 n = 54-58 616 . The study populations primarily consisted of
cancer patients and the purpose of the studies was mainly prognosis/ risk prediction of disease. The model performance varied widely
with AUC values ranging from 0. 691 to 0. 992 and the qualities of enrolled studies varied. Further analysis revealed several
limitations including unclear inclusion / exclusion criteria lack of reliable preprocessing methods absence of feature engineering and
insufficient cross-validation and external validation. Therefore although a few studies attempted to establish prediction models using
biochemical indicators the overall quality of the research still needs improvement. Future research should focus on optimizing models
using multivariate variable selection and advanced machine learning / deep learning algorithms adopting standardized evaluation
methods for model validation to ensure the clinical applicability of the models and incorporating time-series data to enhance model
quality and fully realize the clinical value of biochemical indicators.
