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Combining structured and unstructured data for predictive models: a deep learning approach.

Dongdong Zhang^1,2, Changchang Yin³, Jucheng Zeng^1,2

¹Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH, 43210, USA.

BMC Medical Informatics and Decision Making

|October 30, 2020

Summary

This summary is machine-generated.

This study introduces novel deep learning models that integrate electronic health records (EHRs) structured and unstructured data. Combining these data types improves patient representation and prediction accuracy for clinical tasks.

Keywords:

Data fusion Deep learning Electronic health records Time series forecasting

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Area of Science:

Medical Informatics
Machine Learning in Healthcare
Deep Learning for Clinical Prediction

Background:

Electronic health records (EHRs) offer vast potential for medical research.
Machine learning and deep learning are increasingly used in medical informatics.
Existing predictive models often overlook valuable unstructured clinical notes.

Purpose of the Study:

To develop advanced neural network architectures for patient representation learning.
To integrate sequential unstructured clinical notes with structured EHR data.
To enhance the performance of clinical prediction models.

Main Methods:

Proposed two general-purpose multi-modal neural network architectures.
Utilized document embeddings for long clinical notes.
Employed CNNs or LSTMs for sequential data and one-hot encoding for static information.

Main Results:

Evaluated models on mortality, readmission, and length of stay prediction.
The proposed fusion models outperformed models using only structured or unstructured data.
Demonstrated improved patient representation by combining heterogeneous EHR data.

Conclusions:

Fusion models effectively combine structured and unstructured data for better patient representation.
Integrating diverse EHR data types enhances prediction model performance.
This approach can lead to reduced clinical errors and improved patient outcomes.