Real-Time Predictive Analytics for Continuous Monitoring Of Patient Electronic Health Records
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I5P107Keywords:
Predictive Analytics, Real-time Monitoring, Electronic Health Records (EHR), Machine Learning, Health Informatics, Anomaly Detection, Patient CareAbstract
Because of advancements regarding technology & the widespread implementation of Electronic Health Records (EHRs), health care information is growing very exponentially. This means that physicians need to have up-to-date predictive analytics tools to help them make quick decisions. The goal of this project is to build a system that will allow powerful statistical modeling techniques to continuously check client electronic health information. This technique combines machine learning procedures with current data streams from medical information infrastructures. This helps you see problems early, predict major health events & act before they happen. The system combines preparation of data techniques to handle different kinds of clinical data, feature extraction to discover appropriate trends & predictive techniques like recurrent neural networks & algorithms for finding anomalies to look at patterns throughout time. Datasets from available EHR sources were assessed to train & validate the model, demonstrating its accuracy in identifying early signs of patient deterioration, sepsis risk & abnormal vital sign fluctuations. Doctors set up a unified analytical workflow by using Apache Kafka for data streaming, TensorFlow for model training & visualization dashboards. The results show that response speed, diagnostic accuracy & patient safety have all improved a lot compared to traditional batch-based monitoring systems. The recommended approach makes it very less difficult for healthcare to function over actual time & sets the foundation for smart systems for clinical decision-making. Further enhancements might include adding wearable technology, using reinforcement education for adaptive supervision & using privacy-preserving analytics to make sure that healthcare data regulations like HIPAA are followed. This research shows how actual time predictive analytics can turn raw healthcare data into useful information, which may improve patient outcomes & make modern healthcare systems work better
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