@inbook{e0942080ab7a48048ae03be47dd8043a,
title = "Predicting Alzheimer{\textquoteright}s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort",
abstract = "The rise of Alzheimer{\textquoteright}s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer{\textquoteright}s Disease risk.",
keywords = "ADNI, Clinical Prediction Modelling, Survival Machine Learning",
author = "Henry Musto and Daniel Stamate and Ida Pu and Daniel Stahl",
note = "Funding Information: Acknowledgements. This work is part of the DHFPT/2023/01 project funded by the FPT University, Hanoi, Vietnam. Funding Information: Supported by NSERC (Natural Sciences and Engineering Research Council of Canada). Funding Information: Funding. This research was performed and financed by the Ministry of Science and Higher Education of the Republic of Kazakhstan within the framework of the AP 19577833 scientific project. Funding Information: Acknowledgments. The presented study was supported by the grant WZ/WI-IIT/3/2023 from the Bialystok University of Technology and funded from the resources for research by the Polish Ministry of Science and Higher Education. Funding Information: Acknowledgement. This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number DS2021-26-03. Funding Information: PNRR MUR program funded by the EU - NGEU, iNEST-Interconnected NordEst Innovation Ecosystem funded by PNRR (Mission 4.2, Investment 49 1.5) NextGen-eration EU - Project ID: ECS 00000043, Research project on Formal Method Based Security Evaluation funded by M/s Keysight Technologies, USA and Research project on Connected Smart Health Services for Rural India under the cluster IoT Research funded by DST, Government of India. Funding Information: Daniel Stahl was part funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London. This study represents independent research and views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Funding Information: Supported by Tunisian Ministry of Higher Education and Scientific Research under the grant agreement number LR11ES48. Funding Information: Acknowledgment. Work partially supported by SERICS (PE00000014) under the Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Computational Collective Intelligence, ICCCI 2023 ; Conference date: 27-09-2023 Through 29-09-2023",
year = "2023",
month = aug,
day = "15",
doi = "10.1007/978-3-031-41456-5_53",
language = "English",
isbn = "9783031414558",
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 = "700--712",
editor = "Nguyen, {Ngoc Thanh} and Adrianna Kozierkiewicz and J{\'a}nos Botzheim and L{\'a}szl{\'o} Guly{\'a}s and Manuel N{\'u}{\~n}ez and Jan Treur and Gottfried Vossen",
booktitle = "Computational Collective Intelligence - 15th International Conference, ICCCI 2023, Proceedings",
address = "Germany",
}