Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create...

Area of Science:

• Health Informatics
• Data Science
• Machine Learning

Background:

• Modern healthcare faces challenges integrating data from IoT devices and Electronic Health Records (EHRs).
• Advancements in data science and Machine Learning (ML) enable better processing of primary and secondary health data for real-time, personalized decisions.

Purpose of the Study:

• To introduce an innovative mechanism for processing and integrating health-related data.
• To highlight the potential of Holistic Health Records (HHRs) and advanced ML/Semantic Web techniques for improving data quality, reliability, and interoperability.

Main Methods:

• Development of a novel mechanism for health data processing and integration.
• Utilization of ML-based and Semantic Web techniques.
• Evaluation using heterogeneous healthcare datasets for pancreatic cancer risk identification and monitoring.

Main Results:

• Introduction of Holistic Health Records (HHRs) for harmonized capture of health determinants.
• Demonstration of a holistic data ingestion mechanism for advanced data processing and analysis.
• Validation and evaluation in a real-world scenario showing improved data quality and interoperability.

Conclusions:

• The proposed mechanism and HHRs offer a viable approach to integrate diverse health data.
• Advanced techniques improve the quality and reliability of health data, supporting personalized risk identification and monitoring.
• This holistic approach enhances decision-making in healthcare through improved data integration and analysis.