Collection of Medical Original Data with Search Engine for Decision Support

Area of Science:

• Medical Informatics
• Computational Medicine

Background:

• The increasing complexity of medicine limits human comprehension of total medical knowledge.
• A need exists for advanced tools to access and analyze specific, quantitative medical data.

Purpose of the Study:

• To demonstrate a high-resolution search engine for quantitative medical data.
• To enable personalized patient care through data-driven insights and decision support.

Main Methods:

• Development of a search engine allowing conventional text search and precise quantitative data retrieval.
• Implementation of "Domain Spaces" (DSs) and "Domain Vectors" (DVs) for defining searchable metric spaces.
• Filtering patient groups based on specific diagnostic data to evaluate therapies and outcomes.

Main Results:

• The search engine facilitates the identification of patient cohorts with similar diagnostic profiles.
• It allows for the evaluation of variable therapies and their associated therapeutic results within specific patient groups.
• Anonymous statistical results provide decision support for clinicians.

Conclusions:

• The developed search engine supports personalized medicine by leveraging quantitative data analysis.
• It facilitates an iterative process of data collection, analysis, and therapeutic decision-making for continuous improvement.
• This approach enhances the precision of medical decision support and treatment planning.