Data Characteristics
Cardiovascular intervention clinical trial data primarily originates from the National Medical Products Administration (NMPA) Clinical Trial Registration and Information Disclosure Platform, international clinical trial registries (e.g., ClinicalTrials.gov), medical journal articles, conference abstracts, and technical documentation from device manufacturers. Data updates are relatively stable, with new trial registrations and results typically updated quarterly or annually. Document structures vary, including structured trial registration forms, semi-structured research protocol summaries, and unstructured full-text papers and device manuals. Core fields include trial ID, device name, indications, inclusion/exclusion criteria, primary endpoints, secondary endpoints, research center information, study phase, and sample size. Units involve millimeters (mm), milligrams (mg), percentages (%), days (d), and months (m), often accompanied by ranges or thresholds.
Constraints on Knowledge Base Retrieval and Recall
The complexity of cardiovascular intervention devices means clinical trial inclusion/exclusion criteria are often highly detailed. They contain multi-layered logical relationships and medical terminology. This requires the knowledge base to semantically understand complex sentences, not relying solely on keyword matching. Document formats vary significantly across different data sources, from structured tables to unstructured text. A unified pre-processing pipeline is necessary to ensure complete information extraction. Trial data update frequency is not high but critical. New devices or trial results can rapidly change clinical practice. Therefore, the knowledge base needs to support incremental updates and version management to ensure recall timeliness. Additionally, units and range values for different fields are crucial for retrieval accuracy. Recall results must accurately reflect these numerical constraints to avoid misleading information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Balances the completeness of cardiovascular intervention trial inclusion/exclusion criteria with vector model processing efficiency. |
Recall count | Top 8–12 entries | Covers multi-source information, ensuring initial capture of multiple key aspects of relevant trials. |
Similarity threshold | 0.75–0.85 | Ensures recall results are highly relevant to the query intent, reducing noise. |
Rerank result count | Top 3–5 entries | Focuses on a small number of most relevant documents, reducing the processing burden on the subsequent language model. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Time required to process large PDF-format clinical trial reports and device manuals. |
maxContext | 6000 characters | Accommodates longer descriptions and complex logic in cardiovascular intervention trial protocols. |
Common Mistakes
- Knowledge base search results take too long to return, sometimes tens of seconds. This typically results from a lack of index optimization for the vector database or an excessively large number of recall items, leading to inefficient queries.
- Recall results contain many general medical terminology documents irrelevant to the query. This happens when the chunking strategy is too coarse, failing to effectively retain specialized cardiovascular intervention context, leading to inaccurate vector embeddings.
- Queries for trials on specific device models result in missing or inaccurate information. This may occur if key fields like device model and version number are not correctly identified and extracted during document parsing, or if these fields are not included in the vectorization scope.
How to Verify Configuration
- For core devices and indications, construct queries including specific models and inclusion/exclusion criteria. Check if recall results include at least one relevant clinical trial report or device manual. Verify if key information in the report matches the query conditions.
- Simulate queries from different data sources (NMPA registration information, journal articles, device manuals). Observe the distribution of sources in the recalled documents. Ensure information from each source is effectively retrieved and that the recalled document types match the source data types.
- Test complex queries involving medical terminology and numerical ranges. Check if relevant documents in the recall results accurately reflect these limiting conditions. Evaluate the similarity score distribution of recalled documents to determine a reasonable range for the similarity threshold.
The values provided are common starting points. Measure them against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.