Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records

• Published in: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Vol. 2019; pp. 799 - 806
• Main Authors: Fouladvand, Sajjad, Mielke, Michelle M., Vassilaki, Maria, St. Sauver, Jennifer, Petersen, Ronald C., Sohn, Sunghwan
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2019
• Subjects: Alzheimer's disease; deep learning; dementia; machine learning; mild cognitive impairment; recurrent neural networks; deep learning; dementia; mild cognitive impairment; Alzheimer’s disease; recurrent neural networks; machine learning
• ISSN: 2156-1125
• DOI: 10.1109/BIBM47256.2019.8982955

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.