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Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.

Xueli Xiao¹, Guanhao Wei², Li Zhou²

¹Computer Science Department, Georgia State University, Atlanta, GA 30303, USA.

Journal of Biomedical Informatics

|June 17, 2021

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This study introduces a new method to analyze patient health records by mapping relationships between medical data points. This approach improves the precision of predicting disease progression and treatment needs.

Area of Science:

Biomedical Informatics
Machine Learning in Healthcare
Health Data Analytics

Background:

Electronic health records (EHRs) are rich sources of patient data for health analytics.
Current methods often overlook the relationships between EHR entities, treating them as isolated features.
This limitation hinders accurate disease detection, progression prediction, and patient profiling.

Purpose of the Study:

To develop a novel approach for analyzing EHR data that incorporates relationships between features.
To improve the predictive performance of machine learning models for clinical tasks.

Main Methods:

Proposed a method to map relationships between EHR features into Procedures, Prescriptions, and Diagnoses (PPD) tensor data.
Formatted PPD tensor data as images for analysis by deep convolutional networks (CNNs).

Keywords:

Convolutional Neural Networks Electronic Health Records Image Mapping Treatment Initiation Prediction

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Integrated this relationship-learning module as a boosting component for classical machine learning models.

Main Results:

The proposed method demonstrated superior real-world modeling performance compared to baseline models.
Achieved higher prediction precision in experiments on a Chronic Lymphocytic Leukemia dataset for treatment initiation prediction.

Conclusions:

Incorporating feature relationships into EHR analysis significantly enhances predictive modeling.
The PPD tensor imaging approach combined with deep learning offers a powerful tool for clinical decision support.