Data Characteristics for This Category
Data for intelligent triage in clinical trial prescreening primarily originates from internal electronic health record (EHR) systems, recruitment criteria published by clinical trial centers, and various medical literature and guidelines. Data update frequencies vary. EHR data updates in real-time. Clinical trial recruitment criteria typically release in batches, updating monthly or quarterly. Document structures are mainly semi-structured or unstructured. Examples include patient medical history records, examination reports, genetic test results, and trial protocol PDF documents. Fields and units are highly specialized. They involve disease diagnostic codes (e.g., ICD-10), laboratory indicators (e.g., mg/dL, mmol/L), and descriptive text in imaging reports.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The specialized and diverse nature of biomedical data places high demands on knowledge base retrieval and recall. The mix of semi-structured and unstructured documents makes traditional keyword matching ineffective. This requires stronger semantic understanding capabilities. Frequently updated clinical trial recruitment information requires the knowledge base to quickly ingest and index new data. This ensures the timeliness of recall results. Medical data sensitivity and accuracy require recall results to be highly relevant and traceable to their sources. This avoids misjudgment. The presence of specific fields like disease diagnostic codes and laboratory indicators means efficient extraction and matching of this structured information is necessary to support prescreening logic.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Clinical trial protocols or medical records often contain long descriptive paragraphs. This length helps preserve contextual integrity. |
Chunk Overlap Length (Segment Overlap Length) | 100–150 characters (characters) | Ensures semantic continuity between paragraphs, preventing critical information from being cut at paragraph boundaries. |
Recall count (Number of Retrieved Items) | 8–12 entries (items) | Provides sufficient candidate information for the large model to make comprehensive judgments, while ensuring recall relevance. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjust based on the semantic similarity distribution of the actual dataset to ensure highly relevant recall. |
Rerank result count (Number of Reranked Items) | 3–5 entries (items) | Further refines recall results, presenting the most relevant few pieces of information to the large model, reducing processing load. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds (seconds) | Processing large PDF clinical trial protocols or medical literature requires longer file parsing times. |
Three Common Mistakes
- Symptom: Outdated clinical trial information appears in intelligent triage results. Reason: The knowledge base data did not synchronize with the latest clinical trial recruitment batches in time, leading to the recall of trials that have closed or are full.
- Symptom: Key laboratory indicators in patient medical records are not correctly identified or matched. Reason: The knowledge base's preprocessing pipeline did not effectively parse and standardize units, abbreviations, and numerical ranges specific to the biomedical field.
- Symptom: The knowledge base answer does not display the specific original content cited. Reason: The
Reference Content Displayconfiguration item is not enabled, or the knowledge base failed to correctly associate original paragraphs with vectors during indexing.
How to Verify Correct Configuration
- Select a batch of patient medical records known to meet or not meet specific clinical trial criteria. Prescreen them using the intelligent triage system. Check if the recalled trial protocols are correct.
- Randomly select different types of medical documents (e.g., genetic test reports, imaging reports). Upload them to the knowledge base and perform a search. Verify that document content is correctly segmented and indexed.
- For queries involving common disease diagnoses and laboratory indicators, check if the recall results include relevant medical terms and values. Verify the accuracy of their source documents.
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.