Can I read Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis on EtoBox?

Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis by Pang, Huadong; Zhou, Li; Dong, Yiping; Chen, Peiyuan; Gu, Dian; Lyu, Tianyi; Zhang, Hansong is a scholarly article available to read on EtoBox.

What is Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis about?

In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in the field of diabetes, where the deep analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The first phase of our approach involves employing BiLSTM-CRF to delve into the temporal characteristics and latent patterns present in EHR data. This method effectively uncovers the progression trends of diabetes, which are often hidden in the complex data structures of medical records. The second phase leverages the combined strength of XGBoost and Logistic Regression to classify these extracted features and evaluate associated risks. This dual approach facilitates a more nuanced and precise predictio

Author

Pang, Huadong; Zhou, Li; Dong, Yiping; Chen, Peiyuan; Gu, Dian; Lyu, Tianyi; Zhang, Hansong

Published

2024

Language

EN