# Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis

arXiv: https://arxiv.org/abs/2412.03961 · HTML: https://mlsanity.com/paper/2412.03961/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 71 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review](https://mlsanity.com/paper/1908.05780/) (2019)
- [Automatic News Generation and Fact-Checking System Based on Language Processing](https://mlsanity.com/paper/2405.10492/) (2024)
- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)

## Built on by (most cited first, 5 of 5)

- [Intelligent logistics management robot path planning algorithm integrating transformer and GCN network](https://mlsanity.com/paper/2501.02749/) (2025)
- [CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning](https://mlsanity.com/paper/2503.10045/) (2025)
- [MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic](https://mlsanity.com/paper/2507.00304/) (2025)
- [Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning](https://mlsanity.com/paper/2509.12269/) (2025)
- [DT-NeRF: A Diffusion and Transformer-Based Optimization Approach for Neural Radiance Fields in 3D Reconstruction](https://mlsanity.com/paper/2509.17232/) (2025)

## Abstract (arXiv)

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 prediction of diabetes, outperforming traditional models, particularly in handling multifaceted and nonlinear medical datasets. Our research demonstrates a notable advancement in diabetes prediction over traditional methods, showcasing the effectiveness of our combined BiLSTM-CRF, XGBoost, and Logistic Regression model. This study highlights the value of data-driven strategies in clinical decision-making, equipping healthcare professionals with precise tools for early detection and intervention. By enabling personalized treatment and timely care, our approach signifies progress in incorporating advanced analytics in healthcare, potentially improving outcomes for diabetes and other chronic conditions.