Mining Implicit Treatment Concepts for Neural Precision Medicine.

Area of Science:

• Biomedical Informatics
• Information Retrieval
• Computational Biology

Background:

• Precision medicine (PM) aims to retrieve relevant treatment information from biomedical literature for patient care.
• Current PM methods often rely on explicit keywords, potentially missing implicitly related concepts.
• Implicit medical concepts are crucial for distinguishing relevant from non-relevant documents in PM.

Purpose of the Study:

• To propose an enhanced neural information retrieval (NIR) framework for precision medicine.
• To incorporate implicitly related medical concepts into NIR models to improve treatment information retrieval.
• To evaluate the effectiveness of the proposed NIR^PM model.

Main Methods:

• Extended state-of-the-art neural IR models (K-NRM, DRMM) into a unified framework (NIR^PM).
• Developed a method to mine a global list of common medical concepts from relevant documents.
• Integrated these mined implicit concepts into the NIR framework.

Main Results:

• The proposed NIR^PM model demonstrated superior performance compared to existing methods.
• Incorporating implicit medical concepts significantly enhanced the effectiveness of PM.
• Experimental results were validated on the TREC PM track benchmark.

Conclusions:

• The NIR^PM model effectively leverages implicit medical concepts for improved precision medicine.
• This approach enhances the retrieval of critical treatment information for clinical decision-making.
• The findings suggest a promising direction for advancing AI in healthcare and biomedical research.