Data Characteristics
Core data sources for dermatology registration documents include clinical trial reports, non-clinical study reports, pharmaceutical research data, post-market safety reports, and guidelines and regulations from domestic and international regulatory bodies. Update frequency varies by document type. Clinical trial reports update after phase summaries or final reports. Regulatory documents update irregularly with policy changes. Most documents are structured PDF text, containing numerous charts, appendices, and references. Clinical study reports include pathological diagnoses, efficacy evaluation indicators (e.g., PASI score, IGA score), and adverse event classifications. Fields often contain medical terminology and standardized scales, with units like mm² (lesion area), % (improvement percentage), and mg (drug dosage).
Constraints on Knowledge Base Retrieval
Dermatology document characteristics impose specific requirements on knowledge base retrieval. First, varying update frequencies necessitate flexible knowledge base synchronization strategies. This ensures timely regulatory documents and accurate clinical data. Second, charts and appendices in PDF documents require robust file parsing to effectively index non-textual information. Structured text contains extensive medical terminology and standardized scales. This demands vector models that accurately understand domain-specific concepts to avoid irrelevant recalls. For example, a search for "eczema treatment" must differentiate treatment plans for various eczema types and identify relevant clinical indicators. Additionally, the rigor of registration documents requires highly precise and traceable retrieval results. Any inaccurate recall can impact the registration process, making similarity threshold settings particularly sensitive.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 500–800 characters | Dermatology documents have relatively regular paragraph structures. This length balances contextual semantic completeness and recall efficiency. |
Recall Count | 10–15 items | A single query may involve multiple clinical or regulatory details. Increasing recall count covers potentially relevant information. |
Similarity Threshold | 0.78–0.85 | Ensures precision of recalled content and avoids false positives, especially for regulatory clauses and clinical indications. |
Rerank Return Count | 5–8 items | Reranking further optimizes results, focusing on the most relevant items and reducing irrelevant information. |
File Preprocessing | Enable OCR recognition | Ensures scanned clinical reports and chart data are effectively indexed and retrieved. |
Vector Model | Use domain-pretrained model | Improves understanding of medical terms, disease classifications, and drug mechanisms, enhancing retrieval accuracy. |
Common Pitfalls
- The knowledge base search node directly outputs content in the workflow. This leads to duplicate or redundant final answers because subsequent AI nodes are not explicitly instructed to process or refine knowledge base recall results.
- Uploaded file search performance is poor. This occurs when original documents are not effectively cleaned and structured, containing significant noise or non-textual information that degrades vector embedding quality.
- An excessively high
Reference Limit(e.g.,2000 tokens) is set. This causes even short answers to exceed the model's context window due to too much reference content, leading to errors or truncation.
Configuration Validation
- Build representative test question sets for different dermatological conditions (e.g., psoriasis, atopic dermatitis) and registration stages (e.g., preclinical, clinical trials). Observe the relevance and completeness of recall results.
- Verify that professional terms, dosage units, and regulatory clauses in recall results highly match the query intent. Check the reasonableness of the
Similarity Threshold. - Confirm that the knowledge base correctly extracts and indexes text information from uploaded PDF documents containing charts or scanned images.
Note: The values provided are common starting points. Measure them against your own samples to find the best fit.
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.