Data Characteristics
Medical affairs clinical trial pre-screening data primarily originates from internal research reports, clinical trial protocols, subject screening logs, adverse event reports, regulatory documents, and external academic publications. This data updates frequently, especially with protocol revisions, regulatory changes, and new research findings. Document structures often combine standardized templates with free-text descriptions. For example, clinical trial protocols typically follow ICH-GCP guidelines, including sections like background, objectives, inclusion/exclusion criteria, and trial design. Fields and units are highly specialized, such as drug dosage (mg/kg), patient vital signs (mmHg, ℃), and laboratory indicators (mmol/L, U/L), and frequently involve medical acronyms.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
High update frequency necessitates an efficient incremental update mechanism for the knowledge base. This ensures real-time accuracy of retrieval results and prevents decisions based on outdated information. Complex document structures, particularly the mix of free text and standardized templates, challenge chunking strategies. These strategies must balance semantic completeness with recall efficiency. Specialized fields, units, and extensive medical terminology require embedding models that accurately understand domain-specific vocabulary context. This reduces recall bias caused by synonyms or near-synonyms. The strictness and frequent changes of regulatory documents make precise retrieval of specific clauses critical. Recall results must accurately pinpoint the original source.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness of clinical trial documents with recall efficiency, preventing loss of key information due to splitting. |
Recall count (Recall Count) | 8–12 items | Increases recall coverage, capturing more potentially relevant document segments to meet complex query demands. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires balancing precision and recall to avoid excessive irrelevant information or missing critical information. |
Rerank result count (Reranked Return Count) | Top 5 items | Reduces large model processing burden while ensuring relevance, focusing on the most core information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing requirements for large clinical trial protocols or regulatory documents, preventing parsing timeouts. |
embedding_model_name | Domain-specific model | Improves understanding of medical terminology and specialized content, enhancing recall accuracy. |
Common Pitfalls
- Query results contain a large amount of irrelevant information: This occurs when the
Similarity threshold(Similarity Threshold) is set too low, leading to overly broad recall of document segments. - After a knowledge base update, retrieval results do not reflect the latest content promptly: This happens when the knowledge base's incremental update mechanism is not effectively triggered, or there is a delay in the document parsing and embedding process.
- Recall results are inaccurate for specific medical terms or acronyms: This indicates that the selected embedding model inadequately understands specialized vocabulary in the biomedical domain, failing to capture its precise semantics.
Verification of Configuration
- Select a representative set of clinical trial pre-screening queries. Check if recall results include all expected relevant document segments and evaluate their relevance ranking.
- Regularly upload updated clinical trial protocols or regulatory documents to the knowledge base. Immediately perform relevant queries to confirm that updated content is accurately recalled.
- Extract queries containing complex medical terminology. Verify that the contextual understanding of these terms in the recall results is correct by comparing them with the original documents.
- Monitor the
PARSE_FILE_TIMEOUT_SECONDSparameter to ensure all uploaded documents are parsed and embedded within the specified time.
The values provided are common starting points and should be measured against the reader's 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.