Data Characteristics in This Category
Data for clinical trial pre-screening in laboratory services primarily comes from various test reports, pathological analyses, gene sequencing results, biomarker detection data, and patient biological sample information. This data typically exists in a mixed format of structured (e.g., CSV/Excel files exported from LIS systems) and unstructured (e.g., scanned handwritten doctor's reports, image-format pathology slides). Data update frequency depends on the testing cycle and project progress, usually ranging from several days to several weeks. Document structures vary, including standardized report templates, custom experimental records, and raw data in image or PDF formats. Fields and units are highly specialized, for example, "gene mutation site" (e.g., EGFR L858R), "expression level" (e.g., Ct value, copies/mL), "concentration" (e.g., ng/mL, μg/dL), and complex medical terminology and abbreviations.
Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts
The diversity and specialization of laboratory service data impose specific constraints on multi-turn conversation and prompt design. Unstructured data, especially image and PDF format test reports, requires efficient OCR and information extraction capabilities. This ensures the AI accurately understands report content, preventing conversation interruptions or inaccurate responses due to missing information. Highly specialized fields and units require prompt design to fully consider the coverage and accuracy of domain-specific vocabulary. This avoids ambiguity and ensures the AI correctly interprets patient biological indicators. For example, with complex variation descriptions in gene sequencing reports, if prompts do not effectively guide the AI to focus on key information, the AI might fail to identify mutation sites relevant to clinical trial enrollment criteria. Data update frequency dictates the strategy for knowledge base synchronization mechanisms. This ensures the AI references the latest data in multi-turn conversations, preventing pre-screening result deviations due to outdated data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Balances report completeness and retrieval efficiency. Avoids overly long paragraphs diluting key information and overly short paragraphs losing context. |
Recall count | Top 10 entries | Ensures coverage of potentially dispersed key information across multimodal reports, improving relevance recall rate. |
Similarity threshold | 0.78–0.85 | Suitable for clinical data with many specialized terms and similar semantics. Balances recall accuracy and relevance. |
maxContext | 8000 tokens | Accommodates accumulated professional background information and report details in multi-turn conversations, maintaining conversational coherence. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time required to parse test report files containing large tables, images, or complex layouts. |
Rerank result count | Top 5 entries | Further focuses on the most relevant experimental data and indicators through a re-ranking mechanism among a large number of recalled results. |
Common Pitfalls
- AI replies "No answer found" during conversation: This usually occurs when OCR recognition of images or PDF reports in the knowledge base fails, resulting in empty text content or unextracted key information.
- In multi-turn conversations, the AI provides inaccurate responses for specific patient biological indicators: This might stem from prompts not adequately guiding the AI to focus on specific values and units in reports, or from outdated data in the knowledge base.
- When sending multiple questions, subsequent questions experience long waiting times: This might relate to FastGPT API's concurrent processing capability or excessive time spent on file parsing and knowledge retrieval in the backend workflow.
Verification of Configuration
- Select test reports of varying complexity. Simulate multi-turn conversations and verify if the AI accurately identifies and references key biomarkers, gene mutation sites, and their values from the reports.
- After a knowledge base update, immediately conduct conversations. Verify if the AI can instantly retrieve and apply the latest experimental data, and check for differences in responses before and after the update.
- During peak periods or under concurrent requests, observe API response times. Ensure the fluidity of multi-turn conversations and check for timeouts or connection errors to evaluate system processing capability.
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.