Improving Early Prognosis of Dementia Using Machine Learning Methods

被引：2

作者：

Katsimpras G. ^{[1
]}

Aisopos F. ^{[1
]}

Garrard P. ^{[2
]}

Vidal M.-E. ^{[3
]}

Paliouras G. ^{[1
]}

机构：

[1] NCSR Demokritos, Athens

[2] St George's, University of London, London

[3] L3S Research Center, Leibniz Universität Hannover, Hannover

来源：

ACM Transactions on Computing for Healthcare | 2022年 / 3卷 / 03期

基金：

欧盟地平线“2020”;

关键词：

Dementia; machine learning; prognosis;

D O I：

10.1145/3502433

中图分类号：

学科分类号：

摘要：

Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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