Linked data and online classifications to organise mined patterns in patient data

Area of Science:

• Medical Informatics
• Data Science
• Bioinformatics

Background:

• Interpreting complex patient care trajectories requires extensive background knowledge and analysis of large datasets.
• Medical classifications are crucial for understanding diagnoses and treatments but can be challenging to integrate.

Purpose of the Study:

• To investigate the use of Linked Data resources for interpreting patterns in patient care trajectories.
• To enhance the analysis of sequential patterns derived from patient data.

Main Methods:

• Utilized Linked Data principles, particularly through the BioPortal system.
• Applied sequential pattern mining to patient care trajectories.
• Developed a navigation structure using linked medical classifications.

Main Results:

• Demonstrated a flexible approach to exploring patient data.
• Facilitated the interpretation of patterns in diagnoses and treatments.
• Showcased the utility of web data resources in medical data analysis.

Conclusions:

• Linked Data and BioPortal offer a valuable framework for navigating and interpreting complex medical data.
• This approach improves the understanding of patient care trajectories by integrating diverse medical classifications.