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DLKN-MLC: A Disease Prediction Model via Multi-Label Learning.

Bocheng Li¹, Yunqiu Zhang¹, Xusheng Wu²

¹Department of Medical Informatics, School of Public Health, Jilin University, Changchun 130021, China.

International Journal of Environmental Research and Public Health

|August 12, 2022

Summary

This study introduces DLKN-MLC, a novel deep learning model for predicting multiple diseases from electronic health records (EHR). The model improves multi-disease prediction accuracy by integrating a disease knowledge network and considering symptom importance.

Keywords:

deep learning disease prediction disease prevention multi label learning

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Computational Biology

Background:

Electronic health records (EHR) offer vast potential for disease prediction.
Current models primarily focus on single-disease prediction, neglecting the common occurrence of multiple co-existing diseases.
Multi-disease prediction is crucial for effective patient intervention and treatment but presents significant data extraction and classification challenges.

Purpose of the Study:

To propose a novel deep learning model, DLKN-MLC, for accurate multi-disease prediction using EHR data.
To enhance disease prediction by integrating a disease knowledge network and quantifying disease correlations.
To differentiate the importance of various diagnostic signals, including symptoms and examination results.

Main Methods:

Developed the DLKN-MLC model combining deep learning with a disease knowledge network for EHR information extraction.
Utilized NodeRank algorithm to quantify correlations between diseases.
Incorporated a feature weighting mechanism to distinguish the importance of common symptoms, occasional symptoms, and auxiliary examination results.

Main Results:

The DLKN-MLC model achieved superior performance in empirical and comparative experiments on real EHR datasets.
Achieved a Hamming loss of 0.2624, one-error rate of 0.2136, ranking loss of 0.2190, average precision of 88.21%, and micro-F1 score of 87.86%.
Demonstrated state-of-the-art performance compared to existing methods.

Conclusions:

The proposed DLKN-MLC model effectively addresses the complexities of multi-disease prediction from EHR.
The integration of deep learning, knowledge networks, and differential symptom weighting significantly improves prediction accuracy.
DLKN-MLC represents a significant advancement in computational approaches for diagnosing multiple concurrent diseases.