Data Characteristics
Smart triage data originates from unstructured text within medical institutions. This includes clinical guidelines, disease treatment pathways, drug instructions, medical literature, and historical medical records. Document updates are relatively stable, typically occurring quarterly or annually. Document structures vary, encompassing lengthy guidelines, tabular drug dosage instructions, mixed text and image anatomical data, and medical records with specialized terminology and abbreviations. Fields and units are highly specialized, such as dosage units (mg, g, ml), time units (h, min, d), and normal ranges for various physiological indicators.
Constraints on Knowledge Base Retrieval and Recall
The characteristics of smart triage R&D documents impose several requirements on knowledge base retrieval and recall. The document update frequency necessitates support for incremental update mechanisms to ensure information timeliness. Diverse document structures require robust text parsing capabilities to extract key information from various formats and effectively index text within tables and images. Specialized terminology and abbreviations demand medical domain semantic understanding from the model to avoid recall bias due to lexical ambiguity. Furthermore, numerical information with units, such as dosage and time, requires support for range queries or unit conversion during retrieval to meet the precision requirements of triage logic.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Balances semantic completeness with recall efficiency, avoiding excessive fragmentation. |
Overlap Size | 100–200 characters | Ensures semantic continuity between paragraphs and minimizes information loss. |
Recall count (Number of Retrieved Chunks) | Top 8–15 | Covers potentially relevant knowledge, balancing recall rate and computational cost. |
Similarity threshold (Similarity Threshold) | Calibrate by measurement | Ensures relevance of retrieved results and reduces noise interference. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large medical literature or complex structured documents. |
maxContext | 8000 Tokens | Allows sufficient contextual information for large model inference. |
Common Pitfalls
- Retrieval results contain numerous irrelevant medical terms or concepts. This occurs due to insufficient semantic understanding of specialized medical vocabulary, leading to inaccurate similarity calculations.
- Critical treatment plans or drug dosage information are not recalled. This manifests as a lack of necessary details in responses. The reason may be incomplete document structured parsing, where information in tables or image captions is not effectively indexed.
- Smart triage results still reference old information after a knowledge base update. This happens when the incremental update mechanism for the knowledge base is not correctly configured, preventing new documents from taking effect promptly.
Validation Steps
- Select a batch of typical triage scenarios. Simulate user queries and verify if the recalled results contain all relevant and accurate diagnostic and treatment information. Compare these results against a human-judged baseline.
- For documents with complex structures, such as tables and mixed text/images, examine their chunking and indexing within the knowledge base. Confirm that key data points and specialized terminology are correctly identified and stored.
- After the release of new clinical guidelines, perform a knowledge base update. Verify that the smart triage system correctly references the new content, ensuring information timeliness meets business requirements.
The values provided are common starting points. Measure them 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.