scholarly journals Risk Prediction Models of Chronic Disease in Chinese Medicine: A Systematic Review

Author(s):  
Yi-tong Li ◽  
Yan Liu ◽  
Wei Yang ◽  
Xinlong Li ◽  
Deqiang Gao

Abstract Objective: To summarize the risk prediction models of chronic disease in Chinese medicine, describe their performance, and assess suitability of clinical or administrative use. Methods: The China National Knowledge Infrastructure and Wanfang Data were searched through February 2021, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies of prediction models of chronic disease in Chinese medicine. Results: From 399 citations reviewed, 17 studies were included in the analysis. Most of the studies were from single-centers (50%) or did not external validated (81.25%). The sample sizes were smaller and the models’ discrimination were larger compared with studies in fully western medicine. All the models used both laboratory findings and subjective judgements from doctors or patients. 9 models concentrated on diabetes mellitus or cardiovascular disease, and showed better performance and clinical application. Conclusions: The prediction models of chronic disease in Chinese medicine have unique advantages due to their considerations of doctors’ and patients’ subjective judgement. Diabetes mellitus and cardiovascular disease prediction models were in higher quality and clinical usability. Efforts to improve their quality are needed as use becomes more widespread.

2020 ◽  
Author(s):  
Maria Athanasiou ◽  
Konstantina Sfrintzeri ◽  
Konstantia Zarkogianni ◽  
Anastasia Thanopoulou ◽  
Konstantina S. Nikita

<div> <div> <div> <p>Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human understanding on the reasoning process, maximize transparency and embellish trust towards the models’ adoption in clinical practice. The aim of the present study is to develop and evaluate an explainable personalized risk prediction model for the fatal or non-fatal CVD incidence in T2DM individuals. An explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the Tree SHAP (SHapley Additive exPlanations) method is deployed for the calculation of the 5-year CVD risk and the generation of individual explanations on the model’s decisions. Data from the 5- year follow up of 560 patients with T2DM are used for development and evaluation purposes. The obtained results (AUC=71.13%) indicate the potential of the proposed approach to handle the unbalanced nature of the used dataset, while providing clinically meaningful insights about the ensemble model’s decision process. </p> </div> </div> </div>


2018 ◽  
Vol 279 ◽  
pp. 38-44 ◽  
Author(s):  
Takanori Honda ◽  
Daigo Yoshida ◽  
Jun Hata ◽  
Yoichiro Hirakawa ◽  
Yuki Ishida ◽  
...  

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