Knowledge Base Retrieval and Recall for Molecular Diagnostic

Data Characteristics

Molecular diagnostic clinical trial pre-screening data originates primarily from gene sequencing reports, pathology reports, clinical genomics databases (e.g., ClinVar, dbSNP), academic papers, and clinical guidelines. This data updates frequently, especially gene mutation information and targeted drug indications. Document structures typically include structured fields like gene locus, mutation type, variant frequency, clinical significance, and recommended treatment plans. They also contain extensive unstructured descriptive text. Common units in the data include base pairs (bp), mutation frequency percentage (%), and sequencing depth (X). The data often involves specific gene nomenclature standards (e.g., HGVS) and disease classification codes (e.g., ICD-10). Some data may exist as images, such as fluorescence in situ hybridization (FISH) results.

Constraints Imposed by Data Characteristics on Knowledge Base Retrieval and Recall

Molecular diagnostic data characteristics impose multiple constraints on knowledge base retrieval and recall. First, high update frequency requires the knowledge base to support efficient incremental updates and version management. This ensures the timeliness and accuracy of recalled information. Second, the coexistence of structured and unstructured data formats necessitates a hybrid retrieval strategy. This strategy must support precise field matching and complex natural language queries. Specific gene nomenclature and disease codes require retrieval models to understand and correctly parse these specialized terms, preventing recall bias due to synonyms or abbreviations. Image data requires additional image recognition or annotation processes to convert it into retrievable text information. Furthermore, clinical trial pre-screening demands extremely high precision in recall. Any minor omission or error in gene variant information can impact patient enrollment decisions.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Length)	`512 characters` (characters)	Balances completeness of gene sequences and report descriptions with embedding model processing capabilities.
`Chunk Overlap Length` (Chunk Overlap Length)	`64 characters` (characters)	Maintains contextual continuity, preventing critical information from being truncated at chunk boundaries.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Covers potentially highly relevant gene variants and clinical data, improving recall rate.
`Similarity threshold` (Similarity Threshold)	`0.78`	Balances recall precision and recall rate, reducing irrelevant results. This value requires calibration against actual samples.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (top 3)	Focuses on the most relevant gene loci, mutation types, and clinical trial matching information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Accommodates the parsing time for large gene sequencing reports and complex pathology reports.

Common Pitfalls

Missing critical gene locus or mutation type information in query results: This usually occurs due to an improper knowledge base chunking strategy, where critical information is split across different chunks, or the embedding model fails to fully understand specialized terminology.
Slow retrieval and recall speed, especially when importing a large number of files: This may relate to PARSE_FILE_TIMEOUT_SECONDS being set too low, or UPLOAD_FILE_MAX_SIZE limits causing files to be repeatedly split and uploaded.
Recall results not aligning with actual clinical needs, with many irrelevant documents: The primary reason is a Similarity threshold (Similarity Threshold) set too loosely, or an excessive Recall count (Recall Count) without effective reranking.

How to Verify Configuration

Select typical clinical trial pre-screening queries. Verify that the recall results include all expected gene loci, mutation information, and relevant clinical guidelines.
Use FastGPT's retrieval logs to check if the query's Recall count (Recall Count) and Rerank result count (Rerank Return Count) meet expectations. Analyze the Similarity score distribution for each recall.
Simulate importing molecular diagnostic reports of varying sizes and formats. Confirm that file parsing and knowledge base update times are within an acceptable range.
For specific gene mutations or diseases, input queries with multiple phrasing variations. Ensure the system correctly handles synonyms and abbreviations, and recalls consistent and accurate results.

Note: The values provided are common starting points and should be measured 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.