Studying how the prediction models will be built as part of platforms.

We are collaborating with companies that have integrated ML model solutions within their platforms to explore their applicability in CDV risk calculation. Our focus is on leveraging these models to enhance predictive accuracy and improve early risk detection. Unlike existing solutions, our approach integrates multiple data sources for a more comprehensive assessment, offering a scalable and adaptable framework for real-world clinical use.

Technical features of the exploitable result:

Machine learning model integration – Utilizes advanced ML models embedded in partner platforms for CDV risk assessment. Multi-source data fusion – Aggregates and analyzes heterogeneous health data (e.g., EHR, wearable device data, lab results) to enhance prediction accuracy. Scalability & adaptability – Designed to integrate seamlessly with existing healthcare systems, supporting different deployment environments (cloud/on-premise). Real-time risk stratification – Provides dynamic, personalized risk scores based on continuous data input, enabling proactive interventions. Interpretable AI – Employs explainable AI techniques to ensure transparency and trust in model predictions, aiding clinical decision-making.

Integration methods & requirements

APIs & interoperability – The solution offers RESTful APIs and FHIR-compliant endpoints for seamless integration with Electronic Health Records (EHR) and other healthcare systems. Cloud & on-premise deployment – Supports deployment on cloud platforms (Azure) or on-premise environments, depending on infrastructure needs. Data input formats – Accepts structured and unstructured data via HL7, JSON, and CSV formats to ensure compatibility with diverse healthcare data sources. Machine learning model hosting – Can be integrated into existing ML pipelines, or deployed as a containerized service via Docker/Kubernetes.

Constraints & considerations

Regulatory compliance – Requires adherence to GDPR, HIPAA, AI Act, and other regional healthcare data protection regulations. Computational resources – Optimal performance requires GPU/TPU acceleration for large-scale predictions and real-time processing. Data privacy & security – Encryption (TLS 1.2+) and role-based access control (RBAC) are mandatory for secure data exchange. System compatibility – Recommended for Linux-based environments (Ubuntu, CentOS) but adaptable for Windows-based healthcare infrastructures.

Our primary customers include healthcare providers, hospitals, and clinics seeking advanced predictive analytics for cardiovascular disease (CDV) risk assessment. Additionally, we target digital health companies looking to enhance their platforms with AI-driven risk prediction capabilities.

For business partnerships, we are looking for:

Healthcare IT companies with expertise in EHR integration and medical data interoperability. AI/ML solution providers specializing in predictive modeling and explainable AI for healthcare applications. Regulatory consultants to support compliance with GDPR, HIPAA, and medical device regulations in different markets.

Our solution is designed for European market, with potential expansion into other regions with strong digital health adoption.

Licensing Model – The exploitable result is available under a commercial license, with flexible options such as:

SaaS-based subscription for cloud deployment. Enterprise licensing for on-premise integration.

Intellectual Property (IP) protection – Proprietary algorithms and models are protected under applicable copyright and patent laws. Compliance Requirements – Users must ensure adherence to data protection regulations (GDPR, HIPAA) when integrating the solution.