scholarly journals Development of an Institution-Specific Readmission Risk Prediction Model for Real-time Prediction and Patient-Centered Interventions

2021 ◽  
Author(s):  
Ann-Marcia C. Tukpah ◽  
Eric Cawi ◽  
Laurie Wolf ◽  
Arye Nehorai ◽  
Lenise Cummings-Vaughn
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Patient Centered ◽  
Time Prediction ◽  
Readmission Risk

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events

PEDIATRICS ◽  
2021 ◽  
pp. e2020042325
Author(s):  
Shannon C. Walker ◽  
C. Buddy Creech ◽  
Henry J. Domenico ◽  
Benjamin French ◽  
Daniel W. Byrne ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Thromboembolic Events ◽  
Venous Thromboembolic Events ◽  
Venous Thromboembolic

scholarly journals Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development Using Loop Detector Data

2015 ◽  
Vol 16 (8) ◽  
pp. 786-791 ◽  
Author(s):  
Ali Pirdavani ◽  
Ellen De Pauw ◽  
Tom Brijs ◽  
Stijn Daniels ◽  
Maarten Magis ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Model Development ◽  
Risk Prediction Model ◽  
Crash Risk ◽  
Rule Based ◽  
Loop Detector ◽  
Rule Based Approach

scholarly journals Predicting Real-Time Crash Risk for Urban Expressways in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Miaomiao Liu ◽  
Yongsheng Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Fuzzy Inference ◽  
Risk Prediction Model ◽  
Training Data ◽  
Sensor Data ◽  
Crash Risk ◽  
Inference System

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

scholarly journals Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

2019 ◽  
Author(s):  
Sameh N. Saleh ◽  
Anil N. Makam ◽  
Ethan A. Halm ◽  
Oanh Kieu Nguyen
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Risk Prediction ◽  
Hospital Readmission ◽  
Model Performance ◽  
Risk Prediction Model ◽  
Day Hospital ◽  
Baseline Characteristics ◽  
Substantial Change ◽  
Readmission Risk

AbstractDespite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day readmission prediction model predicts 7-day readmissions. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We compared model performance and compared differences in strength of model factors between the 7-day model to the 30-day model. While there was no substantial change in model performance between the original 30-day and the re-derived 7-day model, there was significant change in strength of predictors. Characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to the day of discharge.

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

Acute Myocardial Infarction Readmission Risk Prediction Model in Admit and Discharge patients

2021 ◽  
Author(s):  
Rupanjali Chaudhuri ◽  
Vadim Khotilovich ◽  
Monica Gaur ◽  
KV Chetan ◽  
Will Zimmerman
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Readmission Risk
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