RT Journal Article
T1 Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach
A1 Mallo López, Sabela Carme
A1 Valladares Rodríguez, Sonia
A1 Facal Mayo, David
A1 Lojo Seoane, Cristina
A1 Fernández Iglesias, Manuel José
A1 Pereiro Rozas, Arturo X.
K1 Neuropsychiatric symptoms
K1 Mild cognitive impairment
K1 Dementia
K1 Behavioral and psychological symptoms of dementia
K1 Diagnosis and classifications
AB Objectives:To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.Design:Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen’s kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm.Setting:Primary care health centers.Participants:128 participants: 78 cognitively unimpaired and 50 with MCI.Measurements:Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score.Results:30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen’s kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score.Conclusions:ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI
PB Cambridge University Press
SN 1041-6102
YR 2020
FD 2020
LK http://hdl.handle.net/10347/32234
UL http://hdl.handle.net/10347/32234
LA eng
NO Mallo SC, Valladares-Rodriguez S, Facal D, Lojo-Seoane C, Fernández-Iglesias MJ, Pereiro AX. Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach. International Psychogeriatrics. 2020;32(3):381-392
NO This is an Accepted Manuscript of an article published by Edinburgh University Press in International Journal of Humanities and Arts Computing. The Version of Record is available online at: https://doi.org/10.1017/S1041610219001030
NO This work was financially supported through FEDER founds by the Spanish Directorate General of Scientific and Technical Research (Project Ref. PSI2014-55316-C3-1-R), the National Research Agency (Spanish Ministry of Science, Innovation and Universities) (Project Ref. PSI2017-89389-C2-1-R), the Galician Government (Consellería de Cultura, Educación e Ordenación Universitaria; axudas para a consolidación e estruturación de unidades de investigación competitivas do sistema universitario de Galicia; GI-1807-USC: Ref. ED431-2017/27), and the Galician Dementia Research Network (GAIN, Xunta de Galicia) (grant IN607C-2017/02). The first author is funded by a fellowship from the Spanish Ministry of Economy and Competitiveness (ref. BES-2015-071253).
DS Minerva
RD 27 abr 2026