Intelligent health monitoring system with predictive risk assessment and automatic generation of clinical alerts

Abstract

An intelligent health monitoring system with predictive risk assessment and automatic generation of clinical alerts, the system comprising: a patient data acquisition and sensor integration module (101) configured to continuously acquire real-time physiological parameters from at least one of the following: wearable sensors, bedside monitoring devices, and IoT-enabled medical devices; an EHR synchronization and data aggregation engine (102) configured to retrieve and consolidate historical clinical patient data from electronic health record repositories; a data preprocessing and normalization unit (103) configured to clean, filter, normalize, and standardize the acquired real-time physiological parameters and historical clinical data to create a unified patient dataset;an AI-based predictive risk assessment module (104) that is functionally coupled with the data preprocessing and normalization unit (103) and configured to analyze the unified patient dataset and calculate dynamic risk scores that indicate the probability of at least one adverse medical event; a clinical pattern and anomaly detection engine (105) configured to detect abnormal multi-parameter physiological trends based on a correlation analysis of the unified patient dataset; a clinical alert automation and prioritization module (106) configured to generate clinical alerts based on the calculated dynamic risk scores and the detected abnormal physiological trends, and further configured to assign a priority level to each clinical alert;an interface for secure communication and notification delivery (107) configured to transmit prioritized clinical alerts via one or more secure communication channels to at least one device of authorized medical personnel; and an audit logging, feedback learning, and clinical decision support module (108) configured to store monitoring events, generated alerts, confirmations, and clinical outcomes, and further configured to update the predictive model performance based on feedback from medical personnel, the system enabling continuous patient monitoring, early prediction of clinical deterioration, and automated delivery of actionable alerts to facilitate timely medical intervention.

Description

The present invention relates to an intelligent health monitoring system with predictive risk assessment and automatic generation of clinical alerts, configured to continuously collect real-time physiological patient data from wearable sensors, hospital devices, and electronic health records. The system applies AI / ML-based predictive analytics to identify risks of health deterioration at an early stage, generate personalized risk assessments, and detect abnormal patterns indicative of critical medical events. Upon detection of a risk, the system automatically triggers prioritized clinical alerts to medical personnel via secure communication channels, enabling timely intervention, improved patient safety, and a reduction in emergency complications. In modern healthcare settings, continuous patient monitoring is becoming increasingly important due to the rising prevalence of chronic diseases, the aging population, and high patient occupancy rates in hospitals. However, most existing monitoring systems rely heavily on routine manual observations or simple, threshold-based alarms, which often fail to detect early warning signs of deteriorating patient condition. As a result, healthcare professionals may not receive an alert until a patient's condition has already worsened, leading to delayed clinical decisions and an increased risk of complications. Another major challenge is the overwhelming amount of patient data generated by wearables, bed monitors, diagnostic devices, and electronic health records. Clinicians are expected to interpret this vast amount of data in real time, which is virtually impossible in the hectic daily routine of a hospital. Furthermore, conventional alarm systems frequently trigger false alarms, leading to alarm fatigue among nurses and physicians, causing critical alerts to be ignored or overlooked due to repeated non-critical notifications. Therefore, there is a great need for an intelligent healthcare monitoring system that not only continuously records patients' vital signs but can also predict potential health risks in advance using automated analysis. Such a system should offer predictive risk assessment, early detection of deterioration, and the automated generation of clinically meaningful, prioritized alerts, thereby reducing false alarms and providing healthcare professionals with timely, actionable insights for decision-making. One objective of this disclosure is to provide an intelligent health monitoring system for the continuous, real-time acquisition of patients' vital signs and clinical parameters. The system minimizes reliance on manual observation and supports early-stage health monitoring. Another objective of this disclosure is to enable AI-based predictive risk assessment to detect potential deterioration in health status in advance. This supports proactive clinical decision-making and timely interventions. Another objective of this disclosure is to reduce false alarms and prevent alarm fatigue by applying anomaly detection with context analysis. This ensures that only meaningful and relevant clinical alerts are generated. Another objective of this disclosure is to automatically generate clinical alerts and prioritize them according to severity and urgency. This improves the efficiency of emergency response and ensures faster care for critically ill patients. Another objective of this disclosure is to securely transmit alerts and notifications to authorized medical personnel via multiple communication platforms. The system ensures data protection through encryption and access control mechanisms. Another objective of this disclosure is to integrate real-time monitoring data with information from electronic health records to create a unified health profile of the patient. This improves the accuracy of risk prediction and the outcomes of clinical decision support. Another objective of this disclosure is to maintain audit logs of patient monitoring events, generated alerts, and confirmations by medical personnel for compliance purposes. This improves traceability and medical-legal accountability in healthcare. Another objective of this disclosure is to support the continuous improvement of predictive models through feedback from medical staff and machine learning-based optimization. This improves the long-term accuracy and adaptability of the system to different patient conditions. The present invention relates to an intelligent health monitoring system with predictive risk assessment and automatic generation of clinical alerts, configured to continuously monitor patients' vital parameters in real time. The system acquires physiological data via a patient data acquisition and sensor integration module (101) and synchronizes the medical history via an EHR synchronization and data aggregation engine (102). Another embodiment of the present invention is the data preprocessing and normalization unit (103), which is configured to clean, filter, and standardize real-time and historical patient data. The processed data is then analyzed by an AI-based predictive risk assessment module (104) to generate dynamic health risk scores. Another embodiment of the present invention is the clinical pattern and anomaly detection engine (105) configured to identify abnormal multi-parameter trends that may indicate potential deterioration. This enables the early detection of medical emergencies before critical thresholds are reached. Another embodiment of the present invention is the module for the automated generation and prioritization of clinical alerts (106), which is configured to generate severity-based alerts with recommended clinical actions. The alerts are categorized into priority levels to ensure timely response and escalation. Another embodiment of the present invention is the interface for secure communication and notification transmission (107), which is configured to transmit alerts via dashboards, mobile applications, and platforms integrated into EMR systems. The interface ensures encrypted communication and role-based access control for secure transmission. Another embodiment of the present invention is the module for audit logging, feedback learning, and clinical decision support (108), which is configured to record monitoring activities and responses of medical staff for compliance purposes. The module further supports continuous learning to improve predictive accuracy over time. Another embodiment of the present invention consists in the system operating as a continuous monitoring loop that integrates real-time vital parameters, historical records, and predictive analytics. This ensures proactive health management, reduced emergency risks, and improved patient safety outcomes. Another embodiment of the present invention consists in the integrated modules (101) to (108) jointly providing an automated end-to-end framework for patient monitoring and alerting. The system improves clinical efficiency by reducing manual workload and providing actionable information for faster interventions. The present invention relates to an intelligent health monitoring system with predictive risk assessment and automatic generation of clinical alerts, configured to continuously monitor patients' health status in real time. The system comprises a patient data acquisition and sensor integration module (101), an electronic health record synchronization and data aggregation engine (102), and a data preprocessing and normalization unit (103) for collecting and preparing patient data. Furthermore, an AI-based predictive risk assessment module (104) and a clinical pattern and anomaly detection engine (105) analyze the data to predict risks of deterioration and detect abnormal clinical patterns.Based on this analysis, a module for the automated generation and prioritization of clinical alerts (106) generates actionable alerts that are transmitted via an interface for secure communication and notification delivery (107). Additionally, a module for audit logging, feedback learning, and clinical decision support (108) records events, supports compliance, and improves predictive accuracy through continuous learning. The system components are as follows: Module for patient data acquisition and sensor integration (101) The system includes a patient data acquisition and sensor integration module (101) configured to continuously acquire physiological and clinical parameters in real time from various sources, including wearable sensors, bedside monitors, IoT-enabled medical instruments, and diagnostic devices in hospitals. The module (101) is designed to capture vital signs such as heart rate, blood pressure, oxygen saturation (SpO2), respiratory rate, temperature, ECG readings, blood glucose levels, and activity patterns. Furthermore, the module supports wired and wireless communication protocols, including Bluetooth, Wi-Fi, ZigBee, and HL7 / FHIR interoperability standards, to ensure seamless integration with heterogeneous healthcare monitoring devices. Engine for synchronizing electronic health records (EHR) and data aggregation (102) The system further includes an EHR synchronization and data aggregation engine (102) configured to securely extract and consolidate historical patient records from electronic health record databases and hospital information systems. The engine (102) aggregates structured and unstructured data such as diagnostic history, medication prescriptions, laboratory reports, radiological findings, clinical notes, allergy information, and details of previous hospital admissions. The module (102) ensures bidirectional, real-time synchronization with EHR systems using secure APIs and maintains patient-specific, unified health profiles for downstream analysis and predictive modeling. Unit for data preprocessing and normalization (103) The invention comprises a data preprocessing and normalization unit (103) configured to clean, filter, and normalize collected physiological and clinical data prior to risk assessment. The unit (103) removes noise, detects missing values, handles outliers, and performs signal smoothing to ensure reliable, high-quality inputs for predictive models. Furthermore, the unit (103) performs timestamp matching, unit conversion, feature scaling, and context tagging based on patient conditions such as age, comorbidities, and medication regimens. The preprocessing module (103) ensures that both real-time streaming data and historical EHR records are standardized into a uniform format to enable accurate predictive calculations. AI-based module for predictive risk assessment (104) The system includes an AI-based predictive risk assessment module (104) configured to apply machine learning and deep learning algorithms to predict potential risks of health deterioration in monitored patients. The module (104) evaluates physiological time-series data, laboratory value fluctuations, and medical history to calculate dynamic risk scores associated with events such as sepsis, cardiac arrest, respiratory failure, stroke, diabetic shock, or the likelihood of intensive care unit admission. The module (104) can employ predictive models such as LSTM networks, random forest classifiers, gradient-boosting models, or transformer-based health predictors to detect early-stage anomalies before critical thresholds are exceeded.The results of the risk assessment are continuously updated to provide a risk forecast in near real time. Engine for clinical pattern recognition and anomaly detection (105) The invention further comprises a clinical pattern and anomaly detection engine (105) configured to detect deviations from normal physiological behavior and identify abnormal patterns that may indicate impending medical complications. The engine (105) performs a multiparametric correlation analysis to detect subtle warning signs, such as decreasing oxygen saturation in conjunction with an increasing heart rate or irregular ECG patterns in the presence of abnormal blood pressure fluctuations. The anomaly detection engine (105) can utilize unsupervised learning techniques such as clustering, autoencoders, or statistical deviation models to detect irregularities even in previously unknown patient scenarios. This module (105) increases system sensitivity and reduces reliance on fixed-rule threshold alarms. Module for automated generation and prioritization of clinical alerts (106) The system includes a module for the automated generation and prioritization of clinical alerts (106), configured to generate real-time alerts based on predicted risk scores, anomaly detection results, and clinical context rules. The module (106) assigns priority levels such as low, medium, high, and critical based on severity and predicted urgency. The alert module (106) is configured to generate actionable clinical alerts, including an explanation of the risk, probable cause, recommended intervention steps, and escalation suggestions. Furthermore, the module (106) reduces false alarms by incorporating contextual filtering, such as the patient's baseline health status, recent medication administration, and clinician-defined alert thresholds. Secure communication and notification interface (107) The system includes a secure communication and notification interface (107) configured to transmit generated alerts to healthcare professionals via various communication channels, such as mobile applications, nurse station dashboards, SMS, email, hospital paging systems, and alerts integrated into the electronic health record (EHR). The interface (107) ensures secure transmission using encryption protocols, authentication mechanisms, and role-based access controls to comply with healthcare data protection regulations. Furthermore, the interface (107) supports multi-stage escalation, where alerts not acknowledged within a defined timeframe are automatically forwarded to senior physicians, emergency teams, or intensive care unit specialists for immediate action. Module for audit logging, feedback learning and clinical decision support (108) The invention further comprises a module for audit logging, feedback learning, and clinical decision support (108) configured to maintain a complete record of all monitoring events, risk assessments, generated alerts, acknowledgments, and responses from medical staff. The module (108) stores event logs in a tamper-proof manner to ensure compliance, for medical-legal documentation, and to meet the requirements of hospital audits. Furthermore, the module (108) collects feedback from clinical staff regarding the accuracy, relevance, and effectiveness of the alerts in order to continuously retrain and improve predictive models over time.In addition, the module (108) provides decision support dashboards that display patient risk trends, historical warning patterns and recommended clinical treatment pathways to assist healthcare providers in making timely and evidence-based medical decisions. The invention is explained again below with reference to the figures. Figure 1 shows a flowchart illustrating the sequential workflow of real-time data acquisition, predictive risk assessment, anomaly detection, alarm generation, secure notification transmission, and logging of test processes. Figure 1 shows a flowchart illustrating the entire workflow of the intelligent health monitoring system during real-time patient monitoring. The figure demonstrates the sequential processing steps, beginning with the continuous collection of patient data, followed by predictive risk assessment and anomaly detection. Furthermore, Figure 1 shows the automated generation of prioritized clinical alerts, their secure transmission to medical personnel, and continuous logging with feedback-based system improvement. The intelligent health monitoring system with predictive risk assessment and automated clinical alert generation continuously collects physiological patient parameters in real time via the patient data acquisition and sensor integration module (101). Simultaneously, the system retrieves and synchronizes the patient's historical health records, laboratory reports, and medication data using the EHR synchronization and data aggregation engine (102). The collected real-time and historical data are then processed by the data preprocessing and normalization unit (103), which filters noise, corrects inconsistencies, and standardizes inputs to ensure accurate downstream analysis. Once the patient data is cleaned and structured, the AI-based predictive risk assessment module (104) evaluates the patient's health status and calculates dynamic risk scores indicating the likelihood of critical medical events such as cardiac arrest, sepsis, respiratory distress, or sudden deterioration. In parallel, the clinical pattern and anomaly detection engine (105) continuously analyzes multiparameter trends and correlations to identify subtle abnormal patterns that might not trigger conventional threshold alarms. This combined predictive and anomaly-driven approach enables the system to detect early warning signs long before the patient develops a severe condition. When a high-risk condition or abnormal pattern is detected, the Automated Clinical Alert Creation and Prioritization module (106) automatically generates clinically meaningful alerts and assigns severity levels based on urgency and patient context. These alerts are securely delivered to healthcare professionals via hospital dashboards, mobile devices, or integrated EMR platforms through the Secure Communication and Notification Interface (107). Furthermore, the Audit Logging, Feedback Learning, and Clinical Decision Support module (108) records all events and actions taken by healthcare professionals, enabling continuous system improvement through feedback-driven model learning. This is achieved while simultaneously supporting compliance and decision-making by visualizing risk trends and tracking alert history.

Claims

An intelligent health monitoring system with predictive risk assessment and automatic generation of clinical alerts, the system comprising: a patient data acquisition and sensor integration module (101) configured to continuously acquire real-time physiological parameters from at least one of the following: wearable sensors, bedside monitoring devices, and IoT-enabled medical devices; an EHR synchronization and data aggregation engine (102) configured to retrieve and consolidate historical clinical patient data from electronic health record repositories; a data preprocessing and normalization unit (103) configured to clean, filter, normalize, and standardize the acquired real-time physiological parameters and historical clinical data to create a unified patient dataset;an AI-based predictive risk assessment module (104) that is functionally coupled with the data preprocessing and normalization unit (103) and configured to analyze the unified patient dataset and calculate dynamic risk scores that indicate the probability of at least one adverse medical event; a clinical pattern and anomaly detection engine (105) configured to detect abnormal multi-parameter physiological trends based on a correlation analysis of the unified patient dataset; a clinical alert automation and prioritization module (106) configured to generate clinical alerts based on the calculated dynamic risk scores and the detected abnormal physiological trends, and further configured to assign a priority level to each clinical alert;an interface for secure communication and notification delivery (107) configured to transmit prioritized clinical alerts via one or more secure communication channels to at least one device of authorized medical personnel; and an audit logging, feedback learning, and clinical decision support module (108) configured to store monitoring events, generated alerts, confirmations, and clinical outcomes, and further configured to update the performance of the predictive model based on feedback from medical personnel, the system enabling continuous patient monitoring, early prediction of clinical deterioration, and automated delivery of actionable alerts to facilitate timely medical intervention. System according to claim 1, wherein the patient data acquisition and sensor integration module (101) is configured to acquire physiological parameters including heart rate, blood pressure, oxygen saturation, respiratory rate, temperature, ECG signals, glucose levels and activity data. System according to claim 1, wherein the EHR synchronization and data aggregation engine (102) is configured to extract structured and unstructured patient records that include diagnostic history, medication prescriptions, laboratory reports, radiological findings, allergy information and physician notes. System according to claim 1, wherein the data preprocessing and normalization unit (103) is configured to perform missing value imputation, outlier detection, noise filtering, timestamp alignment and feature scaling to enable accurate predictive analysis. System according to claim 1, wherein the AI-based predictive risk assessment module (104) is configured to use at least one machine learning model selected from a group consisting of Random Forest, Gradient Boosting, Long Short-Term Memory networks and Transformer-based models to predict adverse medical events. System according to claim 1, wherein the clinical pattern detection and anomaly detection engine (105) is configured to detect deterioration patterns based on a correlation of several vital parameters, including at least a decrease in oxygen saturation in combination with an increased heart rate and abnormal fluctuations in respiratory rate. System according to claim 1, wherein the module for automated generation and prioritization of clinical alerts (106) is configured to divide alerts into severity levels comprising “low”, “medium”, “high” and “critical”, and generates recommended intervention measures according to each severity level. System according to claim 1, wherein the interface for secure communication and notification delivery (107) is configured to implement encrypted transmission and role-based access control, and is further configured to automatically escalate alerts if no acknowledgment of the alert is received within a predefined time limit.

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