Semantic-based Approach for Solving the Heterogeneity of Clinical Data

Clinical records contain massive heterogeneity number of data types, generally written in free-note without a linguistic standard. Other forms of medical data include medical images with/without metadata (e.g., CT, MRI, radiology, etc.), audios (e.g., transcriptions, ultrasound), videos (e.g., surgery recording), and structured data (e.g., laboratory test
results, age, year, weight, billing, etc.). Consequently, to retrieve the knowledge from these data is not trivial task.
Handling the heterogeneity besides largeness and complexity of these data is a challenge. The main purpose of this paper
is proposing a framework with two-fold. Firstly, it achieves a semantic-based integration approach, which resolves the
heterogeneity issue during the integration process of healthcare data from various data sources. Secondly, it achieves a
semantic-based medical retrieval approach with enhanced precision. Our experimental study on medical datasets
demonstrates the significant accuracy and speedup of the proposed framework over existing approaches.