Data Characteristics
Public bidding clinical trial data originates from public resource trading platforms, medical institution websites, and third-party bidding information aggregators. Data updates frequently, typically daily or weekly. Document structures are primarily structured and semi-structured, often PDF files such as bidding announcements, procurement documents, and winning bid notices. These documents contain key fields like project name, sponsor, clinical trial institution, drug name, indication, inclusion criteria, exclusion criteria, study phase, and study type. Units include milligrams (mg) and grams (g) for dosage, days, weeks, and months for duration, and number of participants for enrollment.
Constraints on Model Integration and Configuration
High-frequency updates require the model to have efficient data ingestion and indexing capabilities to ensure timely pre-screening results. The complex structure of PDF documents, especially mixed tables and images, challenges document parsing accuracy, necessitating a more robust preprocessing module. Key fields like inclusion/exclusion criteria are often described in natural language, with synonyms, abbreviations, and domain-specific terminology, directly impacting the accuracy of entity recognition and relationship extraction. Inconsistent data formats and field naming across different sources require the model to generalize or use mapping rules for standardization. Unit normalization is also critical to prevent misinterpretations due to inconsistent units.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Balances semantic completeness with indexing efficiency, suitable for long descriptions of inclusion/exclusion criteria. |
Overlap Length | 100 characters | Ensures contextual continuity, reducing the risk of critical information being split. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Filters for clinical trial projects highly relevant to the user query, balancing recall and precision. |
Recall count (Recall Count) | 20–30 items | Covers a sufficient number of potentially relevant projects, providing a candidate set for subsequent re-ranking. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the time required to parse large PDF files, preventing file processing failures due to timeouts. |
embedding_model | text-embedding-ada-002 or m3e | Considers generality and domain adaptability; M3E performs well in Chinese contexts and requires local deployment or API integration. |
Common Pitfalls
- Observation: Some inclusion/exclusion criteria in bidding announcements are not correctly identified or are extracted as empty. Reason: Complex PDF document structures, including image-based text or non-standard tables, prevent OCR or text parsers from accurate recognition.
- Observation: The clinical trial projects returned by the model clearly do not match the query conditions, e.g., mismatched indications. Reason: Inaccurate key entity recognition, such as confusing drug trade names with generic names, or failing to correctly understand medical terminology.
- Observation: After data updates, pre-screening results do not reflect the latest information in a timely manner. Reason: Data synchronization frequency is set too low, or the document parsing queue is backlogged, causing new data to not be indexed in time.
Verification of Configuration
- Select 5 typical recently published bidding announcements. Manually extract key fields (e.g., indications, inclusion criteria) and compare them with the model's parsing results to verify field extraction accuracy.
- Simulate more than 10 user queries of varying complexity. Check the top 5 clinical trial projects recalled by the model, evaluate their relevance to the query, and compare with a manually determined relevance threshold.
- Continuously monitor the document processing queue length and indexing update latency during high-frequency data update periods (e.g., daily mornings) to ensure new data is indexed and included in pre-screening within a reasonable timeframe.
Note: The values provided are common starting points. Measure performance against specific samples to determine optimal settings for your use case.
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.