scholarly journals Establishment and Evaluation of a Risk-prediction Model for HBP in Elderly Patients With NAFLD From a Health Management Perspective

2022 ◽  
Author(s):  
Xin Luo ◽  
An Zhang ◽  
Hong Pan ◽  
Xinxin Shen ◽  
Baocheng Liu ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Prediction Model ◽  
Risk Prediction ◽  
Health Management ◽  
Factor Model ◽  
External Validation ◽  
Predictive Ability ◽  
Risk Prediction Model ◽  
Biochemical Tests ◽  
Nonalcoholic Fatty Liver

Abstract Objective: Elderly patients with nonalcoholic fatty liver disease (NAFLD) are at a higher risk of developing high blood pressure (HBP) and having a low quality of life. This study established an effective, individualised, early HBP risk-prediction model and proposed health management advice for the ³60 patients with NAFLD in Shanghai, China.Methods: Questionnaire surveys, physical examinations, and biochemical tests were conducted on 7,319 cases of sample data. Risk factors were screened using the least absolute shrinkage and selection operator (Lasso) model and random forest (RF) model. A risk-prediction model was established using logistic regression analysis and dynamic nomogram was drawn. The model was evaluated for discrimination, calibration, and clinical applicability using receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), net reclassification index (NRI), and external validation.Results: The results suggested the model showed moderate predictive ability. The area under curve (AUC) of internal validation was 0.707 (95% CI: 0.688-0.727), the external validation AUC was 0.688 (95% CI: 0.672-0.705). The calibration plots showed good calibration, the risk threshold of the decision curve was 30-56%, and the NRI value was 0.109.Conclusion: This HBP risk factor model may be used in clinical practice to predict the HBP risk in NAFLD patients.

scholarly journals Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

2017 ◽  
Vol 128 (5) ◽  
pp. 1140-1145 ◽  
Author(s):  
Japke F. Petersen ◽  
Martijn M. Stuiver ◽  
Adriana J. Timmermans ◽  
Amy Chen ◽  
Hongzhen Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model ◽  
Larynx Cancer

scholarly journals The M6 risk prediction model and two-step strategy to characterize pregnancies of unknown location: a multicentre external validation study

Authorea ◽  
2020 ◽  
Author(s):  
Evangelia Christodoulou ◽  
Shabnam Bobdiwala ◽  
Christopher Kyriacou ◽  
Jessica Farren ◽  
Nicola Mitchell Jones ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Validation Study ◽  
External Validation ◽  
Risk Prediction Model ◽  
External Validation Study

scholarly journals Evaluation of P-POSSUM as a Risk Prediction Model in Laparoscopic Gastrectomy of Elderly Patients with Gastric Cancer

2016 ◽  
Vol 19 (3) ◽  
pp. 97-101
Author(s):  
Hyo Jung Ko ◽  
Ki Hyun Kim ◽  
Si Hak Lee ◽  
Cheol Woong Choi ◽  
Su Jin Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Elderly Patients ◽  
Prediction Model ◽  
Risk Prediction ◽  
Laparoscopic Gastrectomy ◽  
Risk Prediction Model

scholarly journals External Validation Of The Risk-Prediction Model For Hepatocellular Carcinoma [Hcc] From The Reveal Hcv Study

2013 ◽  
Vol 16 (3) ◽  
pp. A12
Author(s):  
T. Matsuda ◽  
I. Tonnu-Mihara ◽  
Y. Yuan ◽  
P. Hines ◽  
S.L. Saab ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model

scholarly journals Foodborne Disease Risk Prediction using Multi-Graph Structural LSTM: Algorithm Design and Validation Study (Preprint)

2021 ◽  
Author(s):  
Yi Du ◽  
Hanxue Wang ◽  
Wenjuan Cui ◽  
Hengshu Zhu ◽  
Yunchang Guo ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Health Management ◽  
Disease Risk ◽  
Algorithm Design ◽  
Risk Prediction Model ◽  
Foodborne Diseases ◽  
Foodborne Disease ◽  
Spatial Correlations ◽  
Temporal Dimensions

BACKGROUND Foodborne disease is one of the common threats to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction of future foodborne disease risk is very urgent and of great significance for public health management. OBJECTIVE We aimed to design a spatial temporal risk prediction model suitable for foodborne diseases to predict the future foodborne disease risks in various regions, so as to provide guidance for the prevention and control of foodborne diseases. METHODS We design a novel end-to-end framework to predict the foodborne disease risk by using a multi-graph structural LSTM neural network, which can utilize the encoder-decoder structure to achieve multi-step prediction. In particular, to capture the multiple spatial correlations, we divide regions by administrative area and construct the adjacent graph with different metrics, including the proximity of regions, the similarity of historical data, the similarity of regional function and the similarity of foodborne disease exposure food. Furthermore, we also integrate the attention mechanism in both spatial and temporal dimensions and external factors to refine the prediction accuracy. RESULTS We validate our model with extensive experiments on a long-term real-world foodborne disease dataset, ranging from 2015 to 2019 in multiple provinces of China, where the experimental results clearly demonstrate that our approach can outperform other state-of-the-art baselines with a significant margin. CONCLUSIONS Our proposed spatial temporal risk prediction model of foodborne diseases can take into account the spatial temporal characteristics of foodborne disease data and provide a certain degree of precision for future disease spatial temporal risk prediction, thereby providing support for the prevention and risk assessment of foodborne disease.

scholarly journals Derivation and External Validation of an Early Readmission Risk Prediction Model

2016 ◽  
Vol 19 (7) ◽  
pp. A809
Author(s):  
SR Dorajoo ◽  
V See ◽  
CT Chan ◽  
ZY Tan ◽  
N Koomanan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model ◽  
Early Readmission ◽  
Readmission Risk
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