Data Characteristics
Data for surgical robot pharmacovigilance comes from post-market surveillance reports, adverse event databases, clinical trial reports, and medical literature. This data updates frequently. Post-market surveillance reports may update weekly or monthly, and medical literature is continuously published. Document structures vary, including structured database records, semi-structured report templates, and unstructured free-text descriptions. Fields and units are highly specific. Examples include surgical site, device model, batch number, fault code, adverse event codes (e.g., MedDRA codes), and patient vital signs (e.g., blood pressure in mmHg, heart rate in bpm). Some fields contain complex medical terminology and abbreviations.
Constraints on Knowledge Base Retrieval and Recall
Frequent updates require the knowledge base to have efficient incremental update and indexing mechanisms to ensure retrieval timeliness. Diverse document structures and complex fields mean simple keyword matching often misses critical information. This requires advanced semantic understanding and entity recognition. Examples include fuzzy matching for device models or MedDRA codes, and recognizing synonyms for medical terms. Furthermore, the large amount of non-standardized descriptions in free text demands higher quality text preprocessing and vectorization models. These models require training or fine-tuning with specialized biomedical corpora. Specific units and numerical ranges necessitate support for numerical range queries or unit conversion during retrieval to precisely match user intent.
Configuration Guidelines
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances contextual completeness with vector representation accuracy, accommodating longer adverse event descriptions in reports. |
Recall count (Recall Count) | Top 10-15 | Increases recall rate, covering more potentially relevant surveillance reports and literature snippets. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, typically 0.75-0.85 | Balances precision and recall, avoiding false positives and negatives, ensuring high relevance of recalled results to the query. |
Rerank result count (Rerank Return Count) | 5 | Further refines results, presenting the most relevant document snippets to the user. |
embeddingModel | text-embedding-ada-002 or higher version | Improves semantic understanding of complex medical terms, especially for specialized content like MedDRA codes. |
maxTokens | 4096 tokens | Allows for longer context input to process detailed surgical records and adverse event reports. |
Common Pitfalls
- When retrieving adverse event reports, results may lack critical device model or batch information. This occurs because associated fields were not chunked together with core descriptions during knowledge base segmentation, leading to incomplete context during recall.
- When users query specific adverse reactions, the AI response may omit subsequent treatment steps. This happens when knowledge base document splitting granularity is too coarse, causing a single recalled snippet to contain multiple logical steps that are not fully extracted during reranking or summarization.
- After a knowledge base update, users may query a newly uploaded surgical robot device model, but the system fails to recall relevant information. This indicates that the knowledge base index was not updated promptly, or the incremental update mechanism has a delay, preventing new data from being included in the retrieval scope.
Validation Steps
- For typical adverse event queries, verify that recalled document snippets include key device models, fault codes, and patient symptom descriptions. Check if the
similaritymetric meets expectations. - Test with queries containing specific medical terms and abbreviations. Verify that recalled results correctly identify and match these specialized terms. An example is querying for adverse reactions with MedDRA code
10020000. - Upload a new surgical robot surveillance report. After a few minutes, query for unique information from the report. Confirm that the knowledge base's incremental update and indexing are effective, and that newly added content is accurately recalled.
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.