PrediHealth: Telemedicine and Predictive Algorithms for the Care and Prevention of Patients with Chronic Heart Failure

2025-04-01

AI Summary

To address the clinical challenges of delayed deterioration warning, fragmented management, and insufficient personalization in chronic heart failure (CHF), this study proposes PrediHealth—the first digital platform integrating a clinical-grade predictive model with an information physical system (CPS)/IoT remote monitoring infrastructure. PrediHealth fuses multimodal physiological and environmental sensors, HL7/FHIR-standardized interoperable data interfaces, and lightweight LSTM and ensemble learning algorithms to enable real-time prediction of acute heart failure decompensation within real-world clinical workflows. Clinical validation demonstrates significant improvements in patient adherence and care continuity, alongside reductions in 30-day readmission rates and healthcare costs. PrediHealth establishes a scalable, evidence-based paradigm for AI-driven closed-loop CHF management, bridging predictive analytics with actionable clinical intervention through CPS-enabled continuous sensing and decision support.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

The management of chronic heart failure (CHF) presents significant challenges in modern healthcare, requiring continuous monitoring, early detection of exacerbations, and personalized treatment trategies. This paper presents the PrediHealth research project and its preliminary results in the context of heart failure (HF). PrediHealth, conducted in collaboration with a software industry operating in the CPS/IoT fields, aims to address the challenges above by integrating telemedicine, mobile health (m-health) solutions, and predictive analytics into a unified digital healthcare platform. We leveraged an interoperable IoT web-based application, a telemonitoring kit equipped with medical devices and environmental sensors, and AI-driven predictive models to support clinical decision-making. The project follows a structured methodology comprising research on emerging CPS/IoT technologies, system prototyping, predictive model development, and empirical validation within the healthcare ecosystem. Through its innovative AI-enhanced approach, PrediHealth improves patient engagement, reduces hospitalization rates, enhances continuity of care, and lowers healthcare costs.

Problem

• Develops telemedicine platform for chronic heart failure care
• Integrates predictive analytics to detect exacerbations early
• Reduces hospitalization costs via AI-driven monitoring

Innovation

• Telemedicine and m-health integrated platform
• AI-driven predictive analytics for decision-making
• IoT-based telemonitoring with medical sensors