Medical Proble​​m Oriented Plan (MPOP)

The Medical Problem-Oriented Plan (MPOP) research group adopts a structured, problem-centred research framework that prioritises clinically relevant questions as the starting point for scientific investigation. Rather than pursuing discipline-driven inquiries, MPOP systematically defines medical problems, dissects their underlying biological complexity, and develops integrated research strategies to address them. The group operates at the interface of basic and clinical sciences, emphasizing iterative feedback between clinical observations and experimental validation. This approach ensures that research outputs are not only mechanistically insightful but also directly translatable into improved diagnostic, prognostic, and therapeutic applications.​

The MPOP research group focuses on structured, problem-oriented biomedical research, where medical problems are systematically identified, assessed, and linked to optimized intervention strategies. This approach is inspired by the validated MPOP framework, which emphasizes the integration of problem classification, treatment assessment, and actionable care planning to improve patient outcomes.

Key research interests include:

• Medical Problem Identification and Classification

Developing structured frameworks to identify and categorize medical and treatment-related issues (e.g., indication, effectiveness, safety, and patient-related factors), forming the foundation for targeted research and intervention design. 

• Treatment Assessment and Optimization

Systematically evaluating therapeutic strategies in relation to defined problems, focusing on effectiveness, safety, and appropriateness of interventions within real clinical contexts. 

• AI-Driven Problem Detection and Decision Support

Applying artificial intelligence and machine learning to automatically detect medical problems, predict risks, and support clinical decision-making through data-driven models. 

• Smart Care Plan Design and Intervention Mapping

Linking identified problems to evidence-based interventions using computational approaches, including AI-assisted recommendation systems that optimize personalized care plans. 

• Multi-Omics and Clinical Data Integration

Combining clinical data with genomics, transcriptomics, and other omics layers to enhance problem characterization and enable precision, problem-oriented medicine. 

• Outcome Evaluation and Impact Assessment

Quantifying the effectiveness of interventions and care plans using real-world data, with emphasis on improving patient outcomes and healthcare efficiency. 

• Digital and Translational Clinical Frameworks

Developing scalable, technology-enabled systems that translate structured problem-oriented research into clinical practice, education, and healthcare delivery.