Machine Learning Models for Early Disease Prediction in Healthcare Using Electronic Health Records
Keywords:
Healthcare Analytics, Electronic Health Records, Machine Learning, Predictive Modeling, Early Diagnosis, Clinical Decision SupportAbstract
The increasing availability of electronic health records (EHRs) provides unprecedented opportunities to leverage machine learning (ML) models for early disease prediction. By integrating structured data (demographics, labs, vitals) with unstructured data (clinical notes, imaging reports), predictive models can enable proactive interventions, reduce costs, and improve patient outcomes. This paper reviews current literature, presents a conceptual framework, compares ML approaches, and highlights key challenges in clinical integration.
References
Choi, E., Bahadori, M. T., Sun, J., Kulas, J., Schuetz, A., & Stewart, W. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Machine Learning for Healthcare Conference (MLHC).
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Dean, J. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 18.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. Journal of Biomedical Informatics, 83, 168–185.
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094.
Lee, J., Yoon, J., & Van der Schaar, M. (2021). Personalized risk prediction in healthcare with federated learning. IEEE Transactions on Artificial Intelligence, 2(4), 343–356.
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 119.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. W. H., Feng, M., Ghassemi, M., ... & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
Luo, Y., Xin, Y., Joshi, R., Celi, L. A., & Szolovits, P. (2017). Predicting ICU mortality risk by grouping temporal trends from a multivariate panel of physiologic measurements. AAAI Conference on Artificial Intelligence, 38–45.
Esteban, C., Staeck, O., Yang, Y., Tresp, V. (2016). Predicting clinical events by combining static and dynamic information using recurrent neural networks. IEEE International Conference on Healthcare Informatics (ICHI).
Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics & Proteomics, 15(1), 41–51.
Xu, Y., Biswal, S., Jiang, X., Wang, Y., & Wang, F. (2019). Incorporating temporal EHR data in predictive models: A review. Journal of Biomedical Informatics, 95, 103145.
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318.
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? Review in Methods of Information in Medicine, 56(6), e1–e8
