scholarly journals Design of Scoring Models for Trustworthy Risk Prediction in Critical Patients

10.5772/18163 ◽  
2011 ◽  
Author(s):  
Paolo Barbini ◽  
Gabriele Cevenini
Keyword(s):  
Risk Prediction ◽  
Critical Patients

Newer Markers Don't Improve Risk Prediction

2012 ◽  
Vol 45 (12) ◽  
pp. 10-11
Author(s):  
MARY ANN MOON
Keyword(s):  
Risk Prediction

Diabetoporosity – Risk prediction in prediabetic patients based on biochemical and anthropometric biomarkers

2020 ◽  
Author(s):  
C Haudum ◽  
E Kolesnik ◽  
N Tripolt ◽  
Thomas R. Pieber ◽  
B Obermayer-Pietsch
Keyword(s):  
Risk Prediction

Prognostication Stereotype of Patients Morbidity and Mortality by Extraction of E-Health Records

2018 ◽  
Vol 6 (6) ◽  
pp. 215
Author(s):  
Sunitha .T ◽  
Shyamala .J ◽  
Annie Jesus Suganthi Rani.A
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Health Risks ◽  
Preventive Intervention ◽  
Classification Problem ◽  
Health Examination ◽  
Disease Area ◽  
Health Records ◽  
Mortality And Morbidity ◽  
Main Motive

Data mining suggest an innovative way of prognostication stereotype of Patients health risks. Large amount of Electronic Health Records (EHRs) collected over the years have provided a rich base for risk analysis and prediction. An EHR contains digitally stored healthcare information about an individual, such as observations, laboratory tests, diagnostic reports, medications, procedures, patient identifying information and allergies. A special type of EHR is the Health Examination Records (HER) from annual general health check-ups. Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. By “risk”, we mean unwanted outcomes such as mortality and morbidity. This approach is limited due to the classification problem and consequently it is not informative about the specific disease area in which a personal is at risk. Limited amount of data extracted from the health record is not feasible for providing the accurate risk prediction. The main motive of this project is for risk prediction to classify progressively developing situation with the majority of the data unlabeled.

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