Cookies?
Library Header Image
LSE Research Online LSE Library Services

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Zhou, Jiayi, Liu, Wenlong, Zhou, Huiquan, Lau, Kui Kai, Wong, Gloria H.Y., Chan, Wai Chi, Zhang, Qingpeng, Knapp, Martin ORCID: 0000-0003-1427-0215, Wong, Ian C.K. and Luo, Hao (2024) Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study. The Lancet Regional Health - Western Pacific, 46. ISSN 2666-6065

[img] Text (Knapp_identifying-dementia-from-cognitive-footprints--published) - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (835kB)

Identification Number: 10.1016/j.lanwpc.2024.101060

Abstract

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519.

Item Type: Article
Official URL: https://www.thelancet.com/journals/lanwpc/home
Additional Information: © 2024 The Author(s)
Divisions: Personal Social Services Research Unit
Health Policy
Subjects: R Medicine > RA Public aspects of medicine
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 22 Apr 2024 08:45
Last Modified: 20 Jun 2024 03:15
URI: http://eprints.lse.ac.uk/id/eprint/122701

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics