Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

BMC Medical Informatics and Decision Making

Research Article

Dina Silva¹  Ana Rodrigues²  Isabel Santana³  Sara C. Madeira⁴  Telma Pereira⁵  Luís Lemos⁵  Manuela Guerreiro⁶  Sandra Cardoso⁶  Alexandre de Mendonça⁶ 

[1] Cognitive Neuroscience Research Group, Department of Psychology and Educational Sciences and Centre for Biomedical Research (CBMR), University of Algarve, Faro, Portugal;Faculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal;Faculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal;Departamento de Neurologia, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal;INESC-ID, Lisbon, Portugal;LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal;Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal;INESC-ID, Lisbon, Portugal;Laboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal;

关键词: Neurodegenerative diseases;    Mild cognitive impairment;    Prognostic prediction;    Time windows;    Supervised learning;    Neuropsychological data;   

DOI  :  10.1186/s12911-017-0497-2

 received in 2017-02-19, accepted in 2017-06-28,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPredicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion.MethodsIn the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”.ResultsThe proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set.ConclusionsPrognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.

【 授权许可】

CC BY   
© The Author(s). 2017