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Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling

Henry Musto, Daniel Stamate, Doina Logofatu, Daniel Stahl

TL;DR

A comprehensive Monte Carlo simulation procedure indicates that the survival machine learning models based on Transformer and XGBoost are superior to the conventional survival analysis Cox Proportional Hazards model and are more stable than the conventional statistical model.

Abstract

The paper proposes a novel approach of survival transformers and extreme gradient boosting models in predicting cognitive deterioration in individuals with mild cognitive impairment (MCI) using metabolomics data in the ADNI cohort. By leveraging advanced machine learning and transformer-based techniques applied in survival analysis, the proposed approach highlights the potential of these techniques for more accurate early detection and intervention in Alzheimer's dementia disease. This research also underscores the importance of non-invasive biomarkers and innovative modelling tools in enhancing the accuracy of dementia risk assessments, offering new avenues for clinical practice and patient care. A comprehensive Monte Carlo simulation procedure consisting of 100 repetitions of a nested cross-validation in which models were trained and evaluated, indicates that the survival machine learning models based on Transformer and XGBoost achieved the highest mean C-index performances, namely 0.85 and 0.8, respectively, and that they are superior to the conventional survival analysis Cox Proportional Hazards model which achieved a mean C-Index of 0.77. Moreover, based on the standard deviations of the C-Index performances obtained in the Monte Carlo simulation, we established that both survival machine learning models above are more stable than the conventional statistical model.

Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling

TL;DR

A comprehensive Monte Carlo simulation procedure indicates that the survival machine learning models based on Transformer and XGBoost are superior to the conventional survival analysis Cox Proportional Hazards model and are more stable than the conventional statistical model.

Abstract

The paper proposes a novel approach of survival transformers and extreme gradient boosting models in predicting cognitive deterioration in individuals with mild cognitive impairment (MCI) using metabolomics data in the ADNI cohort. By leveraging advanced machine learning and transformer-based techniques applied in survival analysis, the proposed approach highlights the potential of these techniques for more accurate early detection and intervention in Alzheimer's dementia disease. This research also underscores the importance of non-invasive biomarkers and innovative modelling tools in enhancing the accuracy of dementia risk assessments, offering new avenues for clinical practice and patient care. A comprehensive Monte Carlo simulation procedure consisting of 100 repetitions of a nested cross-validation in which models were trained and evaluated, indicates that the survival machine learning models based on Transformer and XGBoost achieved the highest mean C-index performances, namely 0.85 and 0.8, respectively, and that they are superior to the conventional survival analysis Cox Proportional Hazards model which achieved a mean C-Index of 0.77. Moreover, based on the standard deviations of the C-Index performances obtained in the Monte Carlo simulation, we established that both survival machine learning models above are more stable than the conventional statistical model.
Paper Structure (13 sections, 1 equation, 3 figures, 4 tables)

This paper contains 13 sections, 1 equation, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Alzheimer's Disease Neuroimaging Initiative
  5. Data Preprocessing
  6. ReliefF-Based Feature Selection
  7. Models
  8. Bayesian Optimisation
  9. Model Performance Indicator
  10. Cross-Validation with Bayesian Optimisation and Monte-Carlo Simulation
  11. Software and Hardware
  12. Results
  13. Discussion

Figures (3)

  • Figure 1: A diagram of the architecture of the survival transformer hu_transformer-based_nodate.
  • Figure 2: Illustration of the complete methodology for preprocessing, training, tuning, and evaluation for this study
  • Figure 3: Boxplots of C-Index performances for CoxPH, SXGB and STran models obtained in the Monte Carlo simulation of 100 iterations.