scholarly journals Comparison of Biological Age Prediction Models Using Clinical Biomarkers Commonly Measured in Clinical Practice Settings: AI Techniques Vs. Traditional Statistical Methods

2021 ◽  
Vol 1 ◽  
Author(s):  
Chul-Young Bae ◽  
Yoori Im ◽  
Jonghoon Lee ◽  
Choong-Shik Park ◽  
Miyoung Kim ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Clinical Practice ◽  
Statistical Methods ◽  
Statistical Models ◽  
Predictive Power ◽  
Prediction Models ◽  
Correlation Coefficients ◽  
Biological Age ◽  
Clinical Biomarkers ◽  
Age Prediction

In this work, we used the health check-up data of more than 111,000 subjects for analysis, using only the data with all 35 variables entered. For the prediction of biological age, traditional statistical methods and four AI techniques (RF, XGB, SVR, and DNN), which are widely used recently, were simultaneously used to compare the predictive power. This study showed that AI models produced about 1.6 times stronger linear relationship on average than statistical models. In addition, the regression analysis on the predicted BA and CA revealed similar differences in terms of both the correlation coefficients (linear model: 0.831, polynomial model: 0.996, XGB model: 0.66, RF model: 0.927, SVR model: 0.787, DNN model: 0.998) and R2 values. Through this work, we confirmed that AI techniques such as the DNN model outperformed traditional statistical methods in predicting biological age.

Development and Application of Biological Age Prediction Models with Physical Fitness and Physiological Components in Korean Adults

Gerontology ◽  
2012 ◽  
Vol 58 (4) ◽  
pp. 344-353 ◽  
Author(s):  
Haemi Jee ◽  
Byeong Hwan Jeon ◽  
Young Hwan Kim ◽  
Hong-Kyu Kim ◽  
Jaeone Choe ◽  
...  
Keyword(s):  
Physical Fitness ◽  
Prediction Models ◽  
Biological Age ◽  
Korean Adults ◽  
Age Prediction

Models for estimating the biological age of five organs using clinical biomarkers that are commonly measured in clinical practice settings

Maturitas ◽  
2013 ◽  
Vol 75 (3) ◽  
pp. 253-260 ◽  
Author(s):  
Chul-Young Bae ◽  
Young Gon Kang ◽  
Mei-Hua Piao ◽  
BeLong Cho ◽  
Kyung Hee Cho ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Biological Age ◽  
Clinical Biomarkers

Toward Stable Predictions of Apprentices’ Training Success

2011 ◽  
Vol 10 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Lothar Schmidt-Atzert ◽  
Stefan Krumm ◽  
Dirk Lubbe
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Multiple Regression Analysis ◽  
Statistical Methods ◽  
Personnel Selection ◽  
Predictive Power ◽  
Cross Validation ◽  
Selection Algorithm ◽  
Training Success

Mechanical (statistical) predictions have proven to be useful in personnel selection. However, such predictions require the use of an algorithm to aggregate different predictor scores. The identification of such an algorithm requires analyzing predictor and criterion data obtained from previous applicants. The present manuscript compared predictions made by two different statistical methods: artificial neural networks (ANNs) and multiple regression analysis. Therefore, three consecutive cohorts of apprentices (n = 322, 217, and 118) were examined. Algorithms were derived from one cohort and applied to more recent cohorts. It was shown that ANNs outperformed linear predictions in a cross-validation within the cohorts. However, applying trained ANNs to other samples resulted in a predictive power which was worse than most of the linear predictions. Thus, we conclude that ANNs should only be used as selection algorithm if their validity in different cohorts has been confirmed.

scholarly journals Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Shijun Yang ◽  
Bin Wang ◽  
Xiong Han
Keyword(s):  
Antiepileptic Drugs ◽  
Statistical Models ◽  
Prediction Models ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Treatment Withdrawal ◽  
Patient Treatment ◽  
Recent Developments ◽  
Progression Of Disease ◽  
Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

DECISION MAKING BY VARIABLE CONTRIBUTION IN DISCRIMINANT, LOGIT, AND REGRESSION ANALYSES

2004 ◽  
Vol 03 (02) ◽  
pp. 265-279 ◽  
Author(s):  
STAN LIPOVETSKY ◽  
MICHAEL CONKLIN
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Models ◽  
Regression Analyses ◽  
Discriminant Functions ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Variable Contribution ◽  
Linear Aggregate

Comparative contribution of predictors in multivariate statistical models is widely used for decision making on the importance of the variables for the aims of analysis and prediction. However, the analysis can be made difficult because of the predictors' multicollinearity that distorts estimates for coefficients in the linear aggregate. To solve the problem of the robust evaluation of the predictors' contribution, we apply the Shapley Value regression analysis that provides consistent results in the presence of multicollinearity both for regression and discriminant functions. We also show how the linear discriminant function can be constructed as a multiple regression, and how the logistic regression can be approximated by linear regression that helps to obtain the variables contribution in the linear aggregate.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

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