Leveraging Machine Learning for Real-Time Big Data Analytics in Critical Care and Patient Monitoring Systems
Keywords:
Machine Learning, Big Data Analytics, Critical Care, Patient Monitoring, Real-Time Systems, Predictive AnalyticsAbstract
The advent of machine learning (ML) has revolutionized healthcare, particularly in critical care and patient monitoring systems. This study explores the integration of ML for real-time big data analytics to enhance patient outcomes in intensive care units (ICUs). It presents a synthesis of recent advancements, highlighting the benefits of predictive analytics, anomaly detection, and decision support systems. By analyzing data from published studies, we discuss challenges such as data heterogeneity, security concerns, and model interpretability. Practical recommendations for improving real-time analytics through advanced ML models are provided. This paper contributes to a growing body of literature underscoring the transformative potential of ML in critical care.
References
Johnson, A. E. W., et al. "Machine Learning and Sepsis Prediction in ICU Settings." Critical Care Medicine, vol. 47, no. 6, 2019, pp. 930-939.
Sharma, P., et al. "Logistic Regression for Early Detection of Sepsis." Journal of Biomedical Informatics, vol. 105, 2020, pp. 103422.
Sheta, S.V. (2023). The Role of Test-Driven Development in Enhancing Software Reliability and Maintainability. Journal of Software Engineering, 1(1), 13–21.
Wu, J., et al. "Unsupervised ML Framework for Arrhythmia Detection." Computers in Biology and Medicine, vol. 134, 2021, pp. 104446.
Smith, J., et al. "Big Data Analytics in Critical Care: A Review." Health Informatics Journal, vol. 26, no. 3, 2020, pp. 1849-1865.
Sheta, S.V. (2023). Developing Efficient Server Monitoring Systems Using AI for Real-Time Data Processing. International Journal of Engineering and Technology Research (IJETR), 8(1), 26–37.
Doe, A., et al. "LSTM for Predictive Analytics in ICUs." Journal of Artificial Intelligence in Medicine, vol. 15, no. 1, 2021, pp. 15-23.
Brown, M., et al. "The Role of ML in Healthcare Data." IEEE Transactions on Biomedical Engineering, vol. 68, no. 4, 2021, pp. 1025-1033.
Sheta, S.V. (2022). A study on blockchain interoperability protocols for multi-cloud ecosystems. International Journal of Information Technology and Electrical Engineering, 11(1), 1–11.
Zhang, H., et al. "Predicting Clinical Deterioration Using ML." Annals of Emergency Medicine, vol. 77, no. 2, 2021, pp. 190-197.
Green, T., et al. "Data Heterogeneity in ICU Monitoring Systems." International Journal of Medical Informatics, vol. 145, 2021, pp. 104268.
Patel, S., et al. "Anomaly Detection in Healthcare Systems Using ML." PLoS ONE, vol. 15, no. 8, 2020, pp. e0237395.
Sheta, S.V. (2022). A Comprehensive Analysis of Real-Time Data Processing Architectures for High-Throughput Applications. International Journal of Computer Engineering and Technology, 13(2), 175–184.
Kim, J., et al. "Advances in Explainable AI for Healthcare." Artificial Intelligence in Medicine, vol. 107, 2020, pp. 101889.
Clark, A., et al. "Standardizing Data Formats in Healthcare." Health Data Science, vol. 9, 2022, pp. 12-22.
Chen, T., et al. "XGBoost: A Scalable Tree Boosting System for High-Dimensional Data." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
Rajkomar, A., et al. "Scalable and Accurate Deep Learning for Electronic Health Records." npj Digital Medicine, vol. 1, 2018, pp. 18.
Sheta, S.V. (2021). Investigating Open-Source Contributions to Software Innovation and Collaboration. International Journal of Computer Science and Engineering Research and Development, 11(1), 39–45.
Ching, T., et al. "Opportunities and Obstacles for Deep Learning in Biology and Medicine." Journal of the Royal Society Interface, vol. 15, no. 141, 2018, pp. 20170387.
Luo, G., et al. "Real-Time Big Data Analytics for Predicting Clinical Deterioration in ICUs." Journal of Healthcare Informatics Research, vol. 4, no. 1, 2020, pp. 123-140.
Miotto, R., et al. "Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records." Scientific Reports, vol. 6, 2016, pp. 26094.
Sheta, S.V. (2021). Artificial Intelligence Applications in Behavioral Analysis for Advancing User Experience Design. International Journal of Artificial Intelligence, 2(1), 1–16.
Lee, J., et al. "Deep Learning in the Intensive Care Unit: Predicting Mortality and Acute Physiologic Events." AMIA Annual Symposium Proceedings, 2018, pp. 866-874.
Hinton, G., et al. "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups." IEEE Signal Processing Magazine, vol. 29, no. 6, 2012, pp. 82-97.
Ngiam, K. Y., et al. "Big Data and Machine Learning Algorithms for Health-Care Delivery." The Lancet Oncology, vol. 19, no. 5, 2018, pp. e262-e273.
Sheta, S.V. (2020). Enhancing Data Management in Financial Forecasting with Big Data Analytics. International Journal of Computer Engineering and Technology (IJCET), 11(3), 73–84.
Wang, F., et al. "Learning Interpretable Latent Autoencoder Representations with Applications to Healthcare." IEEE Transactions on Biomedical Engineering, vol. 68, no. 9, 2021, pp. 2686-2696.
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