A machine learning-based real-time patient monitoring and health risk prediction system

Abstract

A machine learning-based real-time patient monitoring and health risk prediction system (100), comprising: (a) a data acquisition module configured to receive real-time physiological data from one or more health monitoring devices; (b) a data preprocessing and normalisation module configured to clean, format and standardise the collected data; (c) a patient profile and context information module for maintaining dynamic, personalized health records, including medical history and lifestyle data; (d) a machine learning engine configured to analyse the pre-processed data in conjunction with contextual information to generate predictive health risk scores; (e) an alert and notification module configured to provide real-time alerts upon detection of abnormal health patterns; (f) a dashboard interface module for visualising patient vital signs, risk predictions and historical trends; g) a feedback and continuous learning module that refines the machine learning models using clinical results and feedback; h) and a security, compliance and audit module that ensures encrypted data processing and compliance with healthcare regulations.

Description

[0001] The present invention relates to the field of healthcare informatics and real-time patient monitoring. More specifically, it concerns a machine learning-based system for continuously collecting and processing health data and predicting risk. The invention enables the early detection of critical health events and supports timely clinical decision-making.

[0002] In modern healthcare, continuous patient monitoring is critical for the early detection of deteriorating health conditions, especially in high-risk patients such as intensive care unit patients, the elderly, or patients with chronic diseases. Traditional patient monitoring systems rely heavily on manual data entry, regular assessments, and rule-based alerts, which can lead to delays in response time and the overlooking of signs of clinical deterioration. Furthermore, these systems lack adaptability and often lack the ability to learn from historical and real-time data to improve patient outcomes.

[0003] The current challenge is that conventional systems are unable to effectively analyze vast streams of patient data collected from diverse sources such as wearable devices, bedside monitors, and electronic health records. This data is often unstructured and high-volume, making it difficult for physicians to gain meaningful insights in real time. Furthermore, there is a lack of intelligent systems that can proactively predict health risks or emergencies before they become clinically apparent, creating gaps in preventive care and early intervention.

[0004] To overcome these limitations, the present invention introduces a machine learning-based real-time patient monitoring and health risk prediction system. This system integrates data from various health sensors and medical records, processes it using advanced machine learning models, and provides real-time alerts for potential health risks. By continuously learning from new patient data and adapting its prediction algorithms, the invention improves the accuracy and timeliness of clinical decisions, thus increasing patient safety and reducing the burden on healthcare providers.

[0005] An objective of the present disclosure is to enable continuous real-time monitoring of patient vital signs.

[0006] Another objective of this disclosure is the early prediction of critical health risks through machine learning.

[0007] Another objective of this disclosure is to reduce the workload of clinicians through automated data analysis.

[0008] Another objective of this disclosure is to improve decision-making through patient-specific risk assessments.

[0009] Another objective of this disclosure is to minimize false alarms through intelligent, context-based alerting.

[0010] Another objective of this disclosure is to improve patient outcomes through timely intervention.

[0011] Another objective of this disclosure is to support remote and mobile access for healthcare professionals.

[0012] Another objective of this disclosure is to ensure data security and compliance with legal requirements through robust safeguards.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention generally relates to a system that collects continuous health data from wearable devices, bedside monitors, and mobile health apps. It supports various physiological parameters such as heart rate, blood pressure, and oxygen saturation.

[0015] In one embodiment of the present invention, the raw data is cleaned, filtered, and standardized to remove noise, fill in missing values, and standardize formats. This ensures accurate and reliable input for machine learning analysis.

[0016] Another embodiment of the invention involves each patient having a personalized digital profile containing information about their medical history, medication, and lifestyle. This profile increases the contextual accuracy of predictions and warnings.

[0017] Another embodiment of the invention involves advanced algorithms analyzing real-time and historical data to detect anomalies and predict health risks. The system supports models such as neural networks and time-series predictors.

[0018] Another embodiment of the invention is that the system delivers timely alerts for high-risk conditions via SMS, email, or integrated hospital systems. It reduces false positives through context-dependent filtering.

[0019] Another embodiment of the invention allows clinicians to access a visual dashboard that displays vital signs, risk scores, and health trends in real time. It is accessible on both desktop and mobile platforms, thus providing flexibility.

[0020] Another embodiment of the invention involves using clinical feedback to retrain and fine-tune the prognostic models to ensure continuous learning. This module improves the system's adaptability to new data and patient responses.

[0021] Another embodiment of the invention provides for data protection through encryption, role-based access, and compliance with regulatory requirements (e.g., HIPAA, GDPR). A complete audit trail ensures transparency and accountability.

[0022] The present invention relates to an integrated, modular, machine learning-based real-time patient monitoring and health risk prediction system. It is designed to continuously collect, process, analyze, and interpret patient health data to enable timely intervention and improve outcomes. The system is divided into several interdependent modules that work seamlessly together: Data acquisition module: This module serves as the primary interface for collecting real-time data from various sources, including wearable devices, bedside monitors, IoT-enabled sensors, mobile health applications, and hospital information systems. It supports multiple data types such as ECG, heart rate, blood pressure, oxygen saturation, temperature, glucose levels, and more. The data acquisition module ensures synchronization and timestamping of incoming data streams and is designed to process both continuous and event-driven inputs. Data preprocessing and normalization module: This module is used for data cleansing, normalization, and transformation. Raw data from different devices can be noisy, inconsistent, or incomplete. This module applies filtering techniques to remove artifacts, corrects missing values ​​using statistical or machine learning-based methods, and standardizes units and formats across datasets. Preprocessing ensures high-quality, consistent data, which is crucial for accurate analysis by subsequent modules. Module for patient profiles and contextual information: This module manages a dynamic digital profile for each patient, including demographics, medical history, medication records, lifestyle factors, genetic predispositions, and physician notes. This contextual information is used to personalize the interpretation of physiological data and adjust machine learning predictions to ensure relevance and patient-specific accuracy. Machine learning and risk prediction module: This core module consists of pre-trained and dynamically updated machine learning models designed to detect abnormal patterns and predict potential health risks. Depending on the clinical use case, algorithms such as random forest, gradient boosting, neural networks, or LSTM-based time series models can be used. The engine continuously learns from new patient data, improving its prediction accuracy over time. It provides probabilistic risk assessments for conditions such as cardiac arrest, sepsis, respiratory arrest, or diabetic shock. Real-time alert and notification module: Once the ML engine identifies a potential risk or anomaly, this module generates alerts and sends real-time notifications to healthcare providers, nursing staff, or emergency contacts. The alert system is customizable, prioritizes critical alerts, and reduces false positives through a context-dependent filter. It supports multimodal communication channels such as SMS, email, in-app notifications, and a direct interface to hospital alert systems. Visualization and dashboard interface module: This module provides intuitive, real-time dashboards for clinicians and authorized users. It visualizes patient trends, vital statistics, risk scores, and alarm histories using charts, graphs, and timeline views. Customizable widgets and drill-down functionality allow users to examine individual patient data or population-level insights. The dashboard supports both desktop and mobile platforms, ensuring accessibility. Module for feedback and continuous learning: After clinical validation or intervention, this module records feedback on the accuracy of the prediction or outcome. This real-world feedback loop allows the machine learning models to relearn at regular intervals or continuously in a supervised manner. Over time, this module helps the system evolve with changing patient demographics, new diseases, and new treatment protocols. Security, Compliance and Audit Module: Given the sensitive nature of healthcare data, this module ensures end-to-end encryption, secure user authentication, and strict access control. It complies with regulatory standards such as HIPAA, GDPR, and other healthcare data management laws. Furthermore, a secure audit trail is maintained for all activities for accountability, medico-legal validation, and quality control.

[0023] The invention is explained again below with reference to the figure. It shows: Fig. : a machine learning-based real-time patient monitoring and health risk prediction system.

[0024] Fig.shows a machine learning-driven real-time patient monitoring and health risk prediction system (100). The operation of the machine learning-based real-time patient monitoring and health risk prediction system begins with the data acquisition module, which continuously collects physiological signals and health parameters from various sources such as wearable devices, bedside monitors, and health apps. These raw inputs are passed to the data preprocessing and normalization module, where they are cleaned, normalized, and structured to ensure consistency and reliability across heterogeneous data formats. At the same time, patient-specific historical data, lifestyle habits, and comorbidities are managed in the patient profile and context information module, enabling the system to contextualize real-time data with personalized health insights.After processing, the structured data is fed into the machine learning and risk prediction module, which analyzes trends and patterns using advanced algorithms to calculate real-time risk scores for conditions such as cardiac arrhythmias, infections, or hypoglycemia. When critical thresholds are exceeded or abnormal patterns are detected, the real-time alerts and notifications module immediately sends alerts to the relevant medical providers via SMS, email, or through the hospital system. Patient status, vital charts, and risk predictions are displayed via the visualization and dashboard interface module, providing physicians with a clear, interactive, and comprehensive overview of the patient's health history.As physician results and feedback are fed back into the system, the Feedback and Continuous Learning module uses this information to retrain and optimize the ML models, ensuring adaptability and improved predictive accuracy over time. The Security, Compliance, and Audit module ensures that data privacy, user access, and audit trails are securely maintained in compliance with healthcare regulations. This seamless integration and interaction of the modules ensures that patient monitoring becomes intelligent, predictive, and proactive.

Claims

[1] A machine learning-based real-time patient monitoring and health risk prediction system (100) comprising: (a) a data acquisition module configured to receive real-time physiological data from one or more health monitoring devices; (b) a data preprocessing and normalisation module configured to clean, format and standardise the collected data; (c) a patient profile and context information module for maintaining dynamic, personalized health records, including medical history and lifestyle data; (d) a machine learning engine configured to analyse the pre-processed data in conjunction with contextual information to generate predictive health risk scores; (e) an alert and notification module configured to provide real-time alerts upon detection of abnormal health patterns; (f) a dashboard interface module for visualising patient vital signs, risk predictions and historical trends; g) a feedback and continuous learning module that refines the machine learning models using clinical results and feedback; h) and a security, compliance and audit module that ensures encrypted data processing and compliance with healthcare regulations. [2] The system (100) of claim 1, wherein the data acquisition module supports integration with wearable devices, bedside monitors, mobile health applications, and IoT-enabled sensors. [3] The system (100) of claim 1, wherein the preprocessing module uses noise filtering, missing value imputation, and unit standardization to ensure consistent data quality. [4] The system (100) of claim 1, wherein the patient profile module dynamically updates and uses medical records, genetic predispositions, and physician notes to personalize predictions. [5] The system (100) of claim 1, wherein the machine learning module uses time series models, including long short memory (LSTM) networks, for continuous risk prediction. [6] The system (100) of claim 1, wherein the alarm and notification module provides multi-channel alerts via SMS, email, push notifications, and integration with hospital alarm systems. [7] The system (100) of claim 1, wherein the feedback module enables supervised learning updates of the machine learning model based on verified clinical results. [8] The system (100) of claim 1, wherein the security module implements end-to-end encryption, user authentication, role-based access control, and audit trails in accordance with HIPAA and GDPR standards.

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