Knowledge Base Retrieval and Recall for Cardiovascular

Data Characteristics

Cardiovascular disease clinical trial pre-screening data comes from various sources. These include medical literature databases (e.g., PubMed, Embase), clinical trial registries (e.g., ClinicalTrials.gov, European Clinical Trials Database), pharmaceutical company internal research reports, and cardiovascular specialty guidelines. Data update frequencies vary. Literature and registries typically update monthly or quarterly. Guidelines may revise every 2-3 years. Document structures are complex, containing unstructured free text (e.g., study background, inclusion/exclusion criteria descriptions) and semi-structured tabular data (e.g., patient baseline characteristics, laboratory test results). Key fields include disease names (e.g., "heart failure," "coronary artery disease"), drug names (e.g., "sacubitril/valsartan," "aspirin"), biomarkers (e.g., "NT-proBNP," "troponin"), dosage units (e.g., "mg," "g"), time units (e.g., "days," "weeks," "years"), and specific medical terminology (e.g., "NYHA class," "LVEF").

Constraints on Knowledge Base Retrieval and Recall

The highly specialized and complex nature of cardiovascular clinical trial data imposes specific constraints on knowledge base retrieval and recall. Diverse, heterogeneous data sources lead to significant variations in document format and content. This requires refined preprocessing and chunking strategies. For example, numerical ranges and units in inclusion/exclusion criteria must be correctly identified and indexed. Failure to do so can confuse "200 mg" with "200 µg." Inconsistent update frequencies require the knowledge base to support incremental updates and version management to ensure timely retrieval results. Long text descriptions (e.g., study background) need longer chunk lengths to preserve contextual semantics. Tabular data may require structured extraction before indexing. Additionally, the cardiovascular field has numerous specialized terms and abbreviations. This demands strong synonym expansion and semantic understanding capabilities from recall algorithms. This avoids missing relevant information due to terminology mismatches.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800-1200 characters`	Cardiovascular clinical trial documents often contain lengthy descriptions. Longer chunks preserve context and reduce semantic fragmentation.
`Chunk Overlap`	`100-200 characters`	Ensures continuity of context at chunk boundaries. This improves the accuracy of cross-chunk information retrieval.
`Recall count` (Recall Count)	`Top 10-15 items`	Cardiovascular pre-screening requires comprehensive coverage of potential matches. Increasing recall count reduces the risk of missed information.
`Similarity threshold` (Similarity Threshold)	`Calibrate by actual measurement`	Test against specific cardiovascular terminology and query patterns. This balances recall and precision.
`Rerank result count` (Reranked Return Count)	`Top 5 items`	The reranking stage typically focuses on the most relevant few items. This highlights core information.
`SEARCH_TIMEOUT_SECONDS`	`30 seconds`	Complex queries may involve multiple indexing layers. This ensures sufficient time for retrieval completion.

Common Mistakes

Retrieval results contain many irrelevant cardiovascular disease types. This happens due to insufficient synonym expansion or a lack of fine-grained differentiation of disease subtypes.
Knowledge base answers contain unit errors for drug dosages or biomarker values. This occurs when the text parsing stage fails to correctly identify and standardize unit fields.
Team members cannot create new entries in the knowledge base. This happens when WRITE_PERMISSION is not correctly configured for the corresponding user group or role.

How to Verify Configuration

Perform simulated pre-screening queries for different cardiovascular diseases (e.g., myocardial infarction, heart failure) and drugs. Check if recall results include the expected key literature and trials.
Use queries containing specific dosages (e.g., "10 mg") or biomarkers (e.g., "LVEF 40%"). Verify the accuracy of values and units in the recalled snippets.
Check FastGPT backend logs. Confirm the SEARCH_TIMEOUT_SECONDS parameter does not trigger timeout errors during actual queries.
Create new cardiovascular-related documents. Verify that users with write permissions can successfully upload and index documents.

Note: The values provided are common starting points. Measure them against specific 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.