Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

ABSTRACT:

Background

Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients' Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely.

Methods

The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as

SUBMITTER: Vyas A 

PROVIDER: S-EPMC9578246 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature