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

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

doi:10.62762/TIS.2025.367320

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ICCK Transactions on Intelligent Systematics, Volume 2, Issue 1, 2025: 1-13

Free to Read | Research Article | 22 December 2024

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

Huadong Pang 1,† *

Li Zhou 2,†

Yiping Dong 3

Peiyuan Chen 4

Dian Gu 5

Tianyi Lyu 6

Hansong Zhang 7

1 Georgia Institute of Technology, Atlanta, GA 30332, United States

2 Faculty of Management, McGill University, Montreal, QC H3B0C7, Canada

3 Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States

4 School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97333, United States

5 University of Pennsylvania, Philadelphia, PA 19104, United States

6 College of Engineering, Northeastern University, Boston, MA 02115, United States

7 Department of Electrical and Computer Engineering, University of California, San Diego, CA 92037, United States

† Huadong Pang and Li Zhou contributed equally to this work

* Corresponding Author: Huadong Pang, [email protected]

DOI: 10.62762/TIS.2025.367320

ARK: ark:/57805/tis.2025.367320

Received: 17 October 2024, Accepted: 05 December 2024, Published: 22 December 2024

Cited by: 3 (Source: Web of Science), 9 (Source: ), 29 (Source: Google Scholar)

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Abstract

In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in diabetes research, where in-depth 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.

Graphical Abstract

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

Keywords

deep learning

electronic health records

BiLSTM-CRF

XGBoost

healthcare analytics

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

This study is a retrospective analysis based solely on fully anonymized historical physical examination records. According to the regulations of the National Health Commission of China and the institutional policy of the data-providing health check center, retrospective studies using completely de-identified routine clinical data are exempt from formal ethics approval and individual informed consent. All data processing strictly followed national regulations on personal information protection and patient privacy.

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APA Style

Pang, H., Zhou, L., Dong, Y., Chen, P., Gu, D., Lyu, T.& Zhang, H. (2024). Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis. ICCK Transactions on Intelligent Systematics, 2(1), 1–13. https://doi.org/10.62762/TIS.2025.367320

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TY  - JOUR
AU  - Pang, Huadong
AU  - Zhou, Li
AU  - Dong, Yiping
AU  - Chen, Peiyuan
AU  - Gu, Dian
AU  - Lyu, Tianyi
AU  - Zhang, Hansong
PY  - 2024
DA  - 2024/12/22
TI  - Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 2
IS  - 1
SP  - 1
EP  - 13
DO  - 10.62762/TIS.2025.367320
UR  - https://www.icck.org/article/abs/TIS.2025.367320
KW  - deep learning
KW  - electronic health records
KW  - BiLSTM-CRF
KW  - XGBoost
KW  - healthcare analytics
AB  - In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in diabetes research, where in-depth 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.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Pang2024Electronic,
  author = {Huadong Pang and Li Zhou and Yiping Dong and Peiyuan Chen and Dian Gu and Tianyi Lyu and Hansong Zhang},
  title = {Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {2},
  number = {1},
  pages = {1-13},
  doi = {10.62762/TIS.2025.367320},
  url = {https://www.icck.org/article/abs/TIS.2025.367320},
  abstract = {In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in diabetes research, where in-depth 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.},
  keywords = {deep learning, electronic health records, BiLSTM-CRF, XGBoost, healthcare analytics},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}

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