Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

08/05/2021
by   Sayantan Kumar, et al.
0

Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 4

page 5

12/03/2018

Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data

Alzheimer's disease (AD) is a degenerative brain disease impairing a per...
08/15/2019

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Of the 2652 articles considered, 106 met the inclusion criteria. Review ...
08/05/2020

Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression

In this thesis the aim is to work on optimizing the modern machine learn...
06/24/2021

Disease Progression Modeling Workbench 360

In this work we introduce Disease Progression Modeling workbench 360 (DP...
11/19/2019

Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches

Background: Overweight and obesity are an increasing phenomenon worldwid...
07/21/2021

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Objective: Temporal electronic health records (EHRs) can be a wealth of ...
10/12/2020

Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review

Language is a valuable source of clinical information in Alzheimer's Dis...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.