Data Characteristics
CRO pharmacovigilance data originates from clinical trial reports, real-world data (RWD), post-market surveillance reports, and medical literature. Data updates frequently, especially during new drug launches and clinical trials. Document structures vary, including structured Adverse Drug Event (ADE) report forms, unstructured patient medical records, handwritten doctor's notes, and semi-structured regulatory documents. Fields cover patient demographics, medication history, adverse event descriptions, severity, outcomes, and causality assessments. Adverse event descriptions often contain extensive medical terminology and free text. Units include dosage (e.g., mg, g), frequency (e.g., times/day), and time (e.g., days, weeks).
Deployment and Upgrade Constraints
High update frequency of CRO pharmacovigilance data requires efficient data ingestion and indexing mechanisms to ensure timely information. Diverse document structures, particularly large amounts of unstructured text, challenge model comprehension. This necessitates powerful text embedding models and parsers. Specialized medical terminology requires the knowledge base to accurately identify and link entities, impacting recall strategies and similarity calculations. Complex logic, such as causality assessment, may require multi-turn dialogues or more intricate reasoning chains. Standardizing field units requires meticulous cleaning and normalization during data preprocessing to avoid misinterpretations due to inconsistent units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | CRO report files, especially those with images or detailed medical record attachments, can be large. |
maxContext | 3000 tokens | Pharmacovigilance reports often contain detailed clinical descriptions, requiring a large context window for comprehension. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Processing large or complex PDF and DOCX files can be time-consuming. |
Chunk size (Segment Length) | 800 characters | Ensures semantic integrity of key information like adverse event descriptions and patient histories during segmentation. |
Recall count (Recall Count) | Top 8 | Increases coverage when retrieving relevant adverse events or regulatory clauses from a large knowledge base. |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement | Balances detection rate and false positive rate. Adjust for precise matching of medical terminology and semantic similarity. |
Common Mistakes
- Low GPU utilization, high single-core CPU usage after deployment: This typically indicates incorrect model inference scheduling, where large models run on the CPU instead of fully utilizing GPU resources.
- Login interface masked and unresponsive after version update: This can be caused by front-end cache conflicts or issues loading new version front-end resources. Clear browser cache or check static resource deployment.
- Knowledge base Q&A lacks precise matching of medical terms: This often results from a tokenizer, embedding model, or recall strategy not optimized for the medical domain, leading to ineffective recognition and matching of specialized terminology.
Verification of Configuration
- Upload a report file containing complex medical terminology and detailed patient medical records. Verify that the system correctly parses it and generates valid knowledge blocks.
- Query the Q&A system with a known adverse event case. Check if the returned results include relevant regulations, similar cases, and potential causality analyses. Evaluate accuracy.
- Simulate high-concurrency data ingestion. Monitor system resources (CPU, GPU, memory) to ensure stable performance under expected load and that GPU utilization meets expectations.
- Retrieve adverse reaction information for a specific drug from the knowledge base. Verify the completeness and relevance of recall results. Compare with manual retrieval results to determine if the similarity threshold and recall count settings are appropriate.
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.