Prognostic/Clinical Prediction Models: Development of Health Risk Appraisal Functions in the Presence of Multiple Indicators: The Framingham Study Nursing Home Institutionalization Model

2005 ◽  
pp. 209-222
Author(s):  
Ralph B. D'Agostino ◽  
Albert J. Belanger ◽  
Elizabeth W. Markson ◽  
Maggie Kelly-Hayes ◽  
Philip A. Wolf
Keyword(s):  
Nursing Home ◽  
Health Risk ◽  
Prediction Models ◽  
Framingham Study ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Risk Appraisal ◽  
Health Risk Appraisal ◽  
Multiple Indicators

Development of health risk appraisal functions in the presence of multiple indicators: The Framingham Study nursing home institutionalization model

1995 ◽  
Vol 14 (16) ◽  
pp. 1757-1770 ◽  
Author(s):  
Ralph B. D'Agostino ◽  
Albert J. Belanger ◽  
Elizabeth W. Markson ◽  
Maggie Kelly-Hayes ◽  
Philip A. Wolf
Keyword(s):  
Nursing Home ◽  
Health Risk ◽  
Framingham Study ◽  
Risk Appraisal ◽  
Health Risk Appraisal ◽  
Multiple Indicators

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

2021 ◽  
pp. postgradmedj-2020-139352
Author(s):  
Simon Allan ◽  
Raphael Olaiya ◽  
Rasan Burhan
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Prediction Models ◽  
Bone Fractures ◽  
Statin Treatment ◽  
Computer Algorithms ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Heart Attacks

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

scholarly journals Predictive Accuracy of a Health Risk Appraisal Program Using Mortality Risk Age in 116,927 Korean Men

2011 ◽  
Vol 26 (2) ◽  
pp. 159 ◽  
Author(s):  
Ju-Young Kim ◽  
Byung-Joo Park ◽  
Yoon Kim ◽  
Jin-Ho Park ◽  
Be-Long Cho
Keyword(s):  
Health Risk ◽  
Mortality Risk ◽  
Predictive Accuracy ◽  
Risk Appraisal ◽  
Health Risk Appraisal
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