Predicting dementia with routine care EMR data

Topic: Risk, prevention and care in population studies

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.

Key findings

• The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data.

Why it matters

This study developed a machine learning model using routine electronic medical record data that predicted dementia within one year of onset with nearly 80% accuracy across multiple institutions. It is a pre-screening and prediction tool, not a treatment, cure or prevention.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1016/j.artmed.2019.101771
• PubMed: 31980108
• Original source →

May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.

@article{benmiled2020-31980108,
  author  = {Ben Miled, Z. and Haas, K. and Black, C. M. and Khandker, R. K. and Chandrasekaran, V. and Lipton, R. and Boustani, M. A.},
  title   = {{Predicting dementia with routine care EMR data}},
  journal = {Artificial intelligence in medicine},
  year    = {2020},
  doi     = {10.1016/j.artmed.2019.101771},
  pmid    = {31980108},
  url     = {https://doi.org/10.1016/j.artmed.2019.101771}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests BMC research notes · 2011-08-17 · usefulness score 47/100 · Digital health tools
• Machine-learning techniques for building a diagnostic model for very mild dementia NeuroImage · 2010-04-09 · usefulness score 44/100 · Digital health tools
• Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse Cortex; a journal devoted to the study of the nervous system and behavior · 2013-06-14 · usefulness score 44/100 · Digital health tools

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