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Predicting dementia with routine care EMR data.

Zina Ben Miled¹, Kyle Haas², Christopher M Black³

¹Department of Electrical and Computer Engineering, School of Engineering and Technology, Indiana University Purdue University at Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202, USA; Regenstrief Institute, Inc., 1101 W. 10th Street, Indianapolis, IN 46202, USA.

Artificial Intelligence in Medicine

|January 26, 2020

Summary

This summary is machine-generated.

A new machine learning (ML) model predicts dementia up to three years in advance using routine electronic medical record (EMR) data. This cost-effective, non-invasive tool achieves nearly 80% accuracy in pre-screening at-risk patients.

Keywords:

Dementia EMR Machine learning Prediction Random forest

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Area of Science:

Neurology
Medical Informatics
Artificial Intelligence

Background:

Dementia diagnosis often occurs late, limiting timely intervention.
Predicting dementia risk early is crucial for patient management.
Routine healthcare data offers a valuable, underutilized resource for predictive modeling.

Purpose of the Study:

To develop a machine learning (ML) model for predicting dementia onset.
To enable early, non-invasive, and cost-effective pre-screening of at-risk individuals.
To utilize existing Electronic Medical Record (EMR) data for dementia risk prediction.

Main Methods:

Training ML models on structured and unstructured data from EMRs (diagnoses, prescriptions, medical notes).
Developing individual models for each data type and a combined model.
Employing human-interpretable ML techniques for clinical adoption.

Main Results:

The combined ML model demonstrated generalizability across multiple healthcare institutions.
The model achieved nearly 80% accuracy in predicting dementia onset within one year.
Identified key predictors of dementia, including known factors and novel insights from medical notes.

Conclusions:

Routine EMR data can be effectively used to build accurate, generalizable dementia prediction models.
The developed ML model offers a scalable solution for early dementia pre-screening.
Further clinical investigation of novel predictors from unstructured data is warranted.