A Comprehensive Review of Methods Used for Health Prediction and Monitoring Utilizing an Electronic Medical Records (EMR) System


  • Pamoda Jayasekera Department of Computer Science, Faculty of Computing Sir John Kotelawala Defence University, Sri Lanka
  • LP Kalansooriya Department of Computer Science, Faculty of Computing Sir John Kotelawala Defence University, Sri Lanka


Healthcare Deep learning Electronic Medical Records ; Rule-based method, Disease diagnosis, Machine learning


In the rapidly evolving field of healthcare, Artificial Intelligence (AI) and pattern recognition play a key role in enhancing disease diagnosis and prediction. As the patient population increases, the digitalization of medical records has become essential, therefore electronic medical records were developed. This stored Electronic Medical Records (EMR) data can be used to predict possible diseases based on the symptoms stored in the system. This study delves into the integration of AI methodologies within EMR systems, providing a comprehensive review of current techniques that have been used in health prediction and monitoring using EMR data. In this paper, different AI-driven approaches were examined and compared, including Deep Learning (DL), Machine Learning (ML), and Rule-Based Methods. This paper reveals the potential of these techniques in accurately diagnosing diseases, additionally, it discusses challenges and future directions, emphasizing the need for innovative solutions to optimize EMR systems in the context of AI and pattern recognition. Several instances where AI models, such as the application of Support Vector Machine (SVM) models, achieved predictive accuracies of 86.2% and 97.33% in different cancer types, and ML models diagnosing Diabetic Retinopathy with a 92% accuracy rate were observed. Variations in the effectiveness of these technologies across different diseases were also observed, such that a technique that has high accuracy in one disease may have lower accuracy in a different disease. This paper aims to contribute to the growing body of knowledge in AI applications in healthcare, offering insights into the development of more efficient, accurate, and predictive healthcare models.


J. Wu, J. Roy, and W. F. Stewart, “Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches,” Med. Care, vol. 48, no. 6, pp. S106–S113, 2010.

S. Ford, “Patient-centered Medicine, Transforming the Clinical Method,” Transforming the Clinical Method, vol. 7, pp. 181–182, 2004.

M. A. Alkureishi, W. W. Lee, S. Webb, and V. Arora, “Integrating patient-centered electronic health record communication training into resident onboarding: Curriculum development and post- implementation survey among house staff,” JMIR Med. Educ, vol. 4, no. 1, 2018.

J. Stausberg, D. Koch, J. Ingenerf, and M. Betzler, “‘Comparing paperbased with electronic patient records: Lessons learned during a study on diagnosis and procedure codes, ,’” J. Amer. Med. Inform. Assoc, vol. 10, no. 5, pp. 470–477, 2003.

G. Makoul, R. H. Curry, and P. C. Tang, “‘The use of electronic medical records: Communication patterns in outpatient encounters,” J. Amer. Med. Inform. Assoc, vol. 8, no. 6, pp. 610–615, 2001.

W. R. Hersh, “‘The electronic medical record: Promises and problems,” J. Amer. Soc. for Inf. Sci, vol. 46, no. 10, pp. 772–776, 1995.

C.-S. Yu, Y.-J. Lin, C.-H. Lin, S.-Y. Lin, J. L. Wu, and S.-S. Chang, “‘Development of an online health care assessment for preventive medicine: A machine learning approach, ,’” J. Med. Internet Res, vol. 22, no. 6, 2020.

Y. Si, “Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review,” J. Biomed. Inform, vol. 115, no. 103671, 2021.

X. Zhang, J. Xiao, and F. Gu, “‘Applying support vector machine to electronic health records for cancer classification,” in Proc. Spring Simul. Conf. (SpringSim), 2019, pp. 1–9.

Z. Zeng, “‘Contralateral breast cancer event detection using nature language processing,” in Proc. AMIA Annu. Symp, 2017.

M. Jamaluddin and A. D. Wibawa, “Patient diagnosis classification based on electronic medical record using text mining and support vector machine,” in 2021 International Seminar on Application for Technology of Information and Communication, pp. 243–248.

R. J. Carroll, A. E. Eyler, and J. C. Denny, “Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis,” AMIA Annu. Symp. Proc., vol. 2011, 2011.

Y. Shen, “CBN: Constructing a clinical Bayesian network based on data from the electronic medical record,” J. Biomed. Inform, vol. 88, pp. 1–10, 2018.

Q. T. Zeng, S. Goryachev, S. Weiss, M. Sordo, S. N. Murphy, and R. Lazarus, “‘Extracting principal diagnosis, co-morbidity and smoking status for asthma research: Evaluation of a natural language processing system, ’’ BMC Med,” BMC Med. Informat. Decis. Making, vol. 6, no. 1, 2006.

S. Sakai, K. Kobayashi, J. Nakamura, S. Toyabe, and K. Akazawa, “‘Accuracy in the diagnostic prediction of acute appendicitis based on the Bayesian network model, ’’ Methods Inf,” Methods Inf. Med, vol. 46, no. 06, pp. 723–726, 2007.

K. M. Al-Aidaroo, A. A. Bakar, and Z. Othman, “Medical data classification with naive Bayes approach,” Inf. Technol. J., vol. 11, no. 9, pp. 1166–1174, 2012.

J. Kazmierska and J. Malicki, “‘Application of the Naïve Bayesian Classifier to optimize treatment decisions,” Radiotherapy Oncol, vol. 86, no. 2, pp. 211–216, 2008.

Y. Sun and D. Zhang, “‘Diagnosis and analysis of diabetic retinopathy based on electronic health records,” IEEE Access, vol. 7, pp. 86115–86120, 2019.

A. Lungu, A. J. Swift, D. Capener, D. Kiely, R. Hose, and J. M. Wild, “‘Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis,” Pulmonary Circulat, vol. 6, no. 2, pp. 181–190, 2016.

A. Tyagi and P. Singh, “‘Asthma diagnosis and level of control using decision tree and fuzzy system,” Int. J. Biomed. Eng. Technol, vol. 16, no. 2, pp. 169–181, 2014.

R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, “‘Deep patient: An unsupervised representation to predict the future of patients from the electronic health records, ,’” Sci. Rep, vol. 6, no. 1, 2016.

H. Xu, “Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases,” AMIA Annu. Symp. Proc, vol. 2011, pp. 1564–1572, 2011.

C. Breischneider, S. Zillner, M. Hammon, P. Gass, and D. Sonntag, “Automatic extraction of breast cancer information from clinical reports,’’ in Proc,” IEEE 30th Int. Symp. Comput.-Based Med. Syst. (CBMS), pp. 213–218, 2017.

A. Jorge, “‘Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms,” Seminars in Arthritis and Rheumatism, 2019.

S. Mehrabi, “‘Temporal pattern and association discovery of diagnosis codes using deep learning,” in Proc. Int. Conf. Healthcare Informat, 2015, pp. 408–416.

X. Shi, “‘Multiple disease risk assessment with uniform model based on medical clinical notes,” IEEE Access, vol. 4, pp. 7074–7083, 2016.

E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, and J. Sun, Doctor AI: Predicting clinical events via recurrent neural networks. 2015.

S. Wu, “‘Modeling asynchronous event sequences with RNNs, ,’” J. Biomed. Informat, vol. 83, pp. 167–177, 2018.

R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, “Deep patient: An unsupervised representation to predict the future of patients from the electronic health records,” Sci. Rep., vol. 6, no. 1, pp. 1–10, 2016.

L. Ali, C. Zhu, Z. Zhang, and Y. Liu, “Automated detection of Parkinson’s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network,” IEEE J. Transl. Eng. Health Med., vol. 7, pp. 1–10, 2019.

S. Farzi, S. Kianian, and I. Rastkhadive, “Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach,” in 2017 5th International Symposium on Computational and Business Intelligence (ISCBI), 2017.

K. Adem, S. Kilicarslan, and O. Cömert, “‘Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder,” Expert Syst. Appl, vol. 115, pp. 557–564, 2019.

U. Hwang, S. Choi, H.-B. Lee, and S. Yoon, Adversarial training for disease prediction from electronic health records with missing data. 2017.

J. Latif, C. Xiao, S. Tu, S. U. Rehman, A. Imran, and A. Bilal, “Implementation and use of disease diagnosis systems for electronic medical records based on machine learning: A complete review,” IEEE Access, vol. 8, pp. 150489–150513, 2020.



How to Cite

Jayasekera, P., & Kalansooriya, P. (2024). A Comprehensive Review of Methods Used for Health Prediction and Monitoring Utilizing an Electronic Medical Records (EMR) System. International Journal of Research in Computing, 2(2), 51–62. Retrieved from http://ijrcom.org/index.php/ijrc/article/view/124