@inbook{c51b58daca8a4f76a8201bfd8a868eab,
title = "Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling",
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{\textquoteright}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.",
keywords = "Alzheimer{\textquoteright}s, Cox Proportional Hazard, Metabolomics, Risk prediction, Survival machine learning, Transformers, XGBoost",
author = "Henry Musto and Daniel Stamate and Doina Logofatu and Daniel Stahl",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 33rd International Conference on Artificial Neural Networks, ICANN 2024 ; Conference date: 17-09-2024 Through 20-09-2024",
year = "2024",
month = sep,
day = "17",
doi = "10.1007/978-3-031-72353-7_26",
language = "English",
isbn = "9783031723520",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "359--372",
editor = "Michael Wand and J{\"u}rgen Schmidhuber and Michael Wand and Krist{\'i}na Malinovsk{\'a} and J{\"u}rgen Schmidhuber and Tetko, {Igor V.} and Tetko, {Igor V.}",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Proceedings",
address = "Germany",
}