Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records

About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinely-collected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:2019

Enthalten in:

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine - 2019(2019) vom: 06. Nov., Seite 799-806

Sprache:

Englisch

Beteiligte Personen:

Fouladvand, Sajjad [VerfasserIn]
Mielke, Michelle M [VerfasserIn]
Vassilaki, Maria [VerfasserIn]
Sauver, Jennifer St [VerfasserIn]
Petersen, Ronald C [VerfasserIn]
Sohn, Sunghwan [VerfasserIn]

Links:

Volltext

Themen:

Alzheimer’s disease
Deep learning
Dementia
Journal Article
Machine learning
Mild cognitive impairment
Recurrent neural networks

Anmerkungen:

Date Revised 04.04.2023

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1109/bibm47256.2019.8982955

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM317617028