Cloud-Based Predictive Healthcare System Leveraging Machine Learning for Early Disease Detection and Patient Monitoring

Abstract

The present invention is a cloud-based predictive healthcare system that utilizes machine learning (ML) for early disease detection and continuous patient monitoring. Designed to analyze large, diverse datasets from electronic health records, wearable devices, and IoT sensors, this system offers real-time insights into patient health, enabling proactive healthcare interventions. The system includes a Data Collection Module that aggregates patient data, a Preprocessing Module for data cleaning and feature extraction, and a Machine Learning Predictive Model for identifying disease risk and predicting onset based on historical and real-time data. Additionally, a Patient Monitoring and Alert Module continuously evaluates patient health metrics, sending alerts to healthcare providers in response to abnormal patterns. The cloud-based infrastructure ensures scalable storage and access across healthcare facilities, facilitating secure data handling and collaboration among providers. By enabling early detection and continuous monitoring, this system enhances patient outcomes and improves healthcare efficiency.

Specification

Description:The present invention is a cloud-based predictive healthcare system that utilizes machine learning (ML) for early disease detection and continuous patient monitoring. Designed to analyze large, diverse datasets from electronic health records, wearable devices, and IoT sensors, this system offers real-time insights into patient health, enabling proactive healthcare interventions. The system includes a Data Collection Module that aggregates patient data, a Preprocessing Module for data cleaning and feature extraction, and a Machine Learning Predictive Model for identifying disease risk and predicting onset based on historical and real-time data. Additionally, a Patient Monitoring and Alert Module continuously evaluates patient health metrics, sending alerts to healthcare providers in response to abnormal patterns. The cloud-based infrastructure ensures scalable storage and access across healthcare facilities, facilitating secure data handling and collaboration among providers. By enabling early detection and continuous monitoring, this system enhances patient outcomes and improves healthcare efficiency. , C , C , Claims:1. A cloud-based predictive healthcare system comprising:
o A data collection module to aggregate patient data from various sources, including EHR, wearable devices, and IoT sensors.
o A preprocessing module for data cleaning, feature extraction, and transformation.
o A machine learning predictive model system that uses real-time patient data to assess disease risk and predict onset.
o A patient monitoring module that continuously monitors health metrics, computes risk scores, and generates alerts.
o A cloud-based storage and processing system for secure data handling and cross-provider access.
2. The system of claim 1, wherein the machine learning predictive models adapt to new patient data and outcomes, thereby improving prediction accuracy over time.
3. The system of claim 2, wherein the patient monitoring module sends real-time alerts for high-risk health patterns via mobile and email notifications.
4. The system of claim 3, wherein the data collection module integrates with wearable IoT sensors, providing continuous health data for real-time analysis.
5. The system of claim 4, further comprising a patient dashboard for healthcare providers, offering visualized health trends and predictive risk assessments.

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