{"id":13871,"date":"2025-05-22T10:32:46","date_gmt":"2025-05-22T08:32:46","guid":{"rendered":"https:\/\/www.convegnonazionaleaiic.it\/predictcare-an-ai-based-platform-for-real-time-vital-signs-monitoring-and-clinical-risk-prediction\/"},"modified":"2025-06-04T14:23:41","modified_gmt":"2025-06-04T12:23:41","slug":"predictcare-an-ai-based-platform-for-real-time-vital-signs-monitoring-and-clinical-risk-prediction","status":"publish","type":"post","link":"https:\/\/www.convegnonazionaleaiic.it\/predictcare-an-ai-based-platform-for-real-time-vital-signs-monitoring-and-clinical-risk-prediction\/","title":{"rendered":"PREDICTCARE: AN AI-BASED PLATFORM FOR REAL-TIME VITAL SIGNS MONITORING AND CLINICAL RISK PREDICTION"},"content":{"rendered":"
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AFFILIAZIONE<\/span><\/strong><\/p>\n

new delta<\/p>\n


\nAUTORE PRINCIPALE<\/span><\/strong><\/p>\n

Dott.ssa Bruno Martina<\/p>\n

VALUTA IL CHALLENGE
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Registrazione obbligatoria. Una valutazione consentita<\/em><\/div>\n
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GRUPPO DI LAVORO<\/span><\/strong><\/p>\n

Dott.ssa Bruno Martina<\/strong> new delta
\nDott.ssa Moncado Francesca<\/strong> universit\u00e0 degli studi di palermo
\nDott.ssa Ferrito Maria Federica<\/strong> universit\u00e0 degli studi di palermo
\nPhD La Rosa Martina<\/strong>
\nPhD Sicari Mirea<\/strong> universit\u00e0 degli studi di messina
\nPhD Calanni Pileri Michela<\/strong> humanitas university<\/p><\/div>\n

AREA TEMATICA<\/span><\/strong><\/p>\n

Applicazioni di intelligenza artificiale in sanit\u00e0<\/div>\n

ABSTRACT<\/span><\/strong><\/p>\n

Continuous monitoring of vital signs combined with early risk prediction models has shown significant potential in improving patient outcomes and optimizing healthcare resources (Zhao et al., 2020; Johnson et al., 2018). PredictCare is a web-based platform designed to simulate an advanced multiparametric monitoring system, integrating real-time data acquisition and AI-driven risk stratification. The primary objective is to develop a software-as-a-medical-device (SaMD) prototype capable of real-time vital signs tracking, automated clinical risk assessment, and enhanced patient-provider communication via an AI-powered interface. PredictCare simulates the acquisition of core clinical parameters (systolic and diastolic blood pressure, heart rate, SpO\u2082, body temperature, BMI, and stress indices) using synthetic patient data.
\nA two-stage XGBoost classification model was developed, achieving a recall of 98% for critical patients (Stage 3), in line with best practices for high-sensitivity medical triage systems (Rajkomar et al., 2019).
\nThe platform also incorporates an interactive dashboard, automated PDF report generation, and HealthGuard AI \u2014 a conversational agent based on OpenAI\u2019s GPT-4 architecture \u2014 to facilitate patient interaction and symptom documentation
\nPredictCare demonstrated robust performance: Overall model accuracy of 95%; Precision (macro average): 88%; Recall (macro average): 89%; F1-Score (macro average): 88%.
\nThe architecture prioritizes critical patient detection while minimizing false negatives, supporting early clinical intervention. The system allows dynamic tracking of clinical variables, personalized alerts, and health trend visualization, meeting the need for scalable, AI-enhanced remote patient monitoring solutions.
\nPredictCare represents an innovative step towards data-driven, AI-supported healthcare, offering a modular framework for future integration with wearable devices and electronic health records (EHRs). Further development aims to pursue regulatory compliance and clinical validation.
\nAlthough currently not certified as a medical device, PredictCare highlights the transformative role of AI in precision medicine and proactive patient care.<\/p>\n

Zhao, M. et al. 2020. DOI: 10.3389\/fmed.2020.637434
\nJohnson et al., 2018. DOI: 10.1109\/JPROC.2015.2501978
\nRajkomar et al., 2019. DOI: 10.1056\/NEJMra1814259<\/p><\/div>\n

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