The TADPOLE challenge

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Table of contents

1 Predicting the Progression of Mild Cognitive Impairment Using Machine Learning : A Systematic and Quantitative Review 
1.1 Introduction
1.2 Materials and Method
1.2.1 Selection process
1.2.2 Reading process
1.2.3 Quality check
1.2.4 Statistical analysis
1.3 Descriptive analysis
1.3.1 A recent trend
1.3.2 Features
1.3.3 Algorithm
1.3.4 Validation method
1.4 Performance analyses
1.4.1 Features
1.4.2 Cognition
1.4.3 Medical imaging and biomarkers
1.4.4 Combination of different imaging modalities
1.4.5 Longitudinal data
1.4.6 Algorithms
1.5 Design of the decision support system and methodological issues .
1.5.1 Identified issues
1.5.1.1 Lack or misuse of test data
1.5.1.2 Performance as a function of data set size
1.5.1.3 Use of features of test subjects
1.5.1.4 Use of the diagnosis date
1.5.1.5 Choice of time-to-prediction
1.5.1.6 Problem formulation and data set choice
1.5.2 Proposed guidelines
1.6 Conclusion
2 Prediction of future cognitive scores and dementia onset in Mild Cognitive Impairment patients 
2.1 Introduction
2.2 Materials and methods
2.2.1 Cross-sectional framework
2.2.1.1 Description
2.2.1.2 Inclusion of additional features
2.2.2 Longitudinal frameworks
2.2.2.1 Averaging approach
2.2.2.2 Temporal regression and stacking
2.2.2.3 Rate of change approach
2.2.3 Experimental setup
2.2.3.1 Data set
2.2.3.2 Validation procedure
2.2.4 TADPOLE challenge
2.3 Results
2.3.1 Cross-sectional framework
2.3.1.1 Proposed approach
2.3.1.2 Additional features
2.3.1.3 Building regression groups
2.3.2 Longitudinal frameworks
2.3.2.1 Averaging approach
2.3.2.2 Stacking approach
2.3.2.3 Rate of change approach
2.3.3 Prediction at different temporal horizons
2.3.4 TADPOLE challenge
2.4 Discussion
2.4.1 Cross-sectional experiments
2.4.2 Longitudinal frameworks
2.4.3 Interpretability
2.4.4 TADPOLE challenge
2.5 Conclusion
3 Reduction of Recruitment Costs in Preclinical AD Trials : Validation of Automatic Pre-Screening Algorithm for Brain Amyloidosis 
3.1 Abstract
3.2 Introduction
3.2.1 Background
3.2.2 Related works
3.2.3 Contributions
3.3 Materials and Methods
3.3.1 Cohorts
3.3.2 Input Features
3.3.3 Algorithms
3.3.4 Performance Measures
3.4 Results
3.4.1 Algorithm and feature choice
3.4.1.1 Algorithm choice
3.4.1.2 Feature selection for cognitive variables
3.4.1.3 Use of MRI
3.4.2 Use of longitudinal measurements
3.4.3 Proposed method performance
3.4.3.1 Cost reduction
3.4.3.2 Age difference between groups
3.4.3.3 Training on a cohort and testing on a different one
3.4.3.4 Representativity of the selected population
3.4.4 Building larger cohorts
3.4.4.1 Pooling data sets
3.4.4.2 Effect of sample size
3.5 Discussion
3.5.1 Results of the experiments
3.5.1.1 Algorithm and feature choice
3.5.1.2 Method performance
3.5.1.3 Data set size
3.5.2 Comparison with existing methods
3.5.2.1 Univariate approaches
3.5.2.2 Other multivariate approaches
3.6 Conclusion
4 Use of psychotropic drugs throughout the course of Alzheimer’s disease : a large-scale study of French medical records 
4.1 Introduction
4.2 Materials and methods
4.2.1 Cohort description
4.2.1.1 Description
4.2.1.2 Group definition
4.2.1.3 Patient overview
4.2.2 Studied treatments
4.2.3 Descriptive and predictive analysis of treatment history
4.2.3.1 Statistical analysis
4.2.4 Predictive model
4.3 Results
4.4 Discussion
4.4.1 Risk factors
4.4.2 Prediction
4.4.3 Management practices
4.4.4 Strengths and weaknesses of the study
4.5 Conclusion
Conclusion & Perspectives 
A Supplementary materials for the systematic and quantitative review 
A.1 Query
A.2 Selection process diagram
A.3 Reported items
A.4 Journals and conference proceedings
A.5 Information table
B Supplementary materials for amyloidosis prediction 
B.1 Computing R and S from the PPV and NPR
B.2 Difference of age in the 3 cohorts
B.3 Algorithm pseudo-code
C Supplementary materials for the study of treatment prescriptions 
C.1 Statistical analysis
C.1.1 Model description
C.1.2 Coefficient interpretation
C.1.3 Intercept of the non-AD group
C.1.4 Slope of the non-AD group
C.1.5 Intercept change for the AD group
C.1.6 Slope change for the AD group
C.1.7 Impact of diagnosis on the intercept
C.1.8 Impact of diagnosis on the slope
C.2 Predictive model
C.2.1 Performance measures
C.2.2 Results optimized for screening