Adaptive mining prediction model for content recommendation to coronary heart disease patients

2013 ◽  
Vol 17 (3) ◽  
pp. 881-891 ◽  
Author(s):  
Jae-Kwon Kim ◽  
Jong-Sik Lee ◽  
Dong-Kyun Park ◽  
Yong-Soo Lim ◽  
Young-Ho Lee ◽  
...  
Author(s):  
Guizhou Hu ◽  
Martin M. Root

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.


Author(s):  
Mitti Blakoe ◽  
Anne Vinggaard Christensen ◽  
Pernille Palm ◽  
Ida Elisabeth Højskov ◽  
Lars Thrysoee ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Guan ◽  
Guo-Hua Dai ◽  
Wu-Lin Gao ◽  
Xue Zhao ◽  
Zhen-Hao Cai ◽  
...  

Objective. This study aimed to construct a 5-year survival prediction model of coronary heart disease (CHD) induced chronic heart failure (CHF), which is supported by the traditional Chinese medicine (TCM) factor, and to verify the model. Methods. Inpatients from January 1, 2012, to December 31, 2017, in seven hospitals in Shandong Province were studied. The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). In the training set, the least absolute shrinkage selection operator regression was first used to screen the independent variables. Logistic regression was then applied to construct a survival prediction model. The following nomogram visualizes the prediction model results. Finally, C-indices, calibration curves, and decision curves were used to discriminate and calibrate the established model and evaluate its practicability in the clinic. Bootstrap resampling and the verification set were used for internal and external verification, respectively. Results. A total of 424 eligible patients were included in the model construction and verification. In this 5-year survival prediction model of patients with CHF induced by CHD, eight independent predictors were included. The series of C-indices for the training set, bootstrap resamples, and verification set was 0.885, 0.867, and 0.835, respectively, demonstrating the credibility of our model. Additionally, the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis of the training and verification sets showed that this 5-year survival prediction model was good in discrimination, calibration, and clinical practicability. Conclusion. This work highlights eight independent factors affecting 5-year mortality in patients with CHF induced by CHD after discharge and further helps reallocate medical resources rationally by precisely identifying high-risk groups. The constructed prediction model not only plays a credible role in prediction but also demonstrates TCM intervention as a protective factor for the 5-year death of patients with CHF induced by CHD, thereby advancing the use of TCM in CHF.


2010 ◽  
Vol 88 (3) ◽  
pp. 314-321 ◽  
Author(s):  
Janice C. Zgibor ◽  
Kristine Ruppert ◽  
Trevor J. Orchard ◽  
Sabita S. Soedamah-Muthu ◽  
John Fuller ◽  
...  

BMJ Open ◽  
2014 ◽  
Vol 4 (5) ◽  
pp. e005025 ◽  
Author(s):  
Sun Ha Jee ◽  
Yangsoo Jang ◽  
Dong Joo Oh ◽  
Byung-Hee Oh ◽  
Sang Hoon Lee ◽  
...  

1986 ◽  
Vol 6 (10) ◽  
pp. 433
Author(s):  
S. Jackson Andrew ◽  
J. Gorten Ralph ◽  
F. Beard Earl ◽  
G. Squires William ◽  
W. Boettcher Sheila

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12259
Author(s):  
Qian Wang ◽  
Wenxing Li ◽  
Yongbin Wang ◽  
Huijun Li ◽  
Desheng Zhai ◽  
...  

Background Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis. Methods In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors. Results Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models. Conclusion The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.


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