Data Characteristics
Cardiovascular pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE), post-market surveillance reports, global adverse event databases (e.g., WHO VigiBase, FDA FAERS), and professional medical literature. Data update frequencies vary. Clinical trial data typically releases after study completion. RWE and post-market surveillance data accumulate continuously, with annual or periodic reports. Literature publishes continuously. Document structures are diverse, including structured Case Report Forms (CRFs), semi-structured adverse event report forms, and unstructured free-text reports, medical paper abstracts, and full texts. Fields and units are highly specialized. Dosage units often include milligrams (mg), micrograms (µg), and milliliters (mL). Time units include hours (h), days (d), and months (m). Specific medical terminology, such as ICD-10 codes and MedDRA codes, are common.
Constraints on Source Citation and Traceability
The diverse data sources for cardiovascular pharmacovigilance require a citation and traceability mechanism that handles structured, semi-structured, and unstructured data simultaneously. Unstructured text, such as medical literature and free-text reports, needs advanced Natural Language Processing (NLP) to identify and extract key information for accurate citation. Varying data update frequencies mean the traceability mechanism needs flexible version management. It must trace data snapshots to specific points in time, addressing information changes due to updates. The use of specialized medical coding (e.g., MedDRA) requires the system to correctly parse and link to standard terminology during citation, preventing semantic confusion. During traceability, it must display the original code and its description. For example, a citation about "myocardial infarction" must trace back to its MedDRA code and specific description in the original report.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 1500 tokens | Balances model understanding of long texts with processing efficiency, accommodating lengthy medical reports. |
Chunk size | 800 characters | Ensures each segment contains sufficient context while preventing individual segments from becoming too long and diluting key information. |
Recall count | Top 10 entries | Increases the chance of recalling relevant information from vast medical literature, ensuring coverage. |
Similarity threshold | 0.75 | Filters out low-similarity non-core content while maintaining relevance. |
Rerank result count | Top 5 entries | Selects the most relevant citation snippets, reducing redundant information and improving traceability efficiency. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large clinical trial reports or multi-page PDF documents. |
Common Pitfalls
- Citation results contain numerous irrelevant or duplicate medical terms. This happens due to an unreasonable segmentation strategy or a similarity threshold set too low, failing to filter noise effectively.
- Uploading large PDF documents or archives results in upload failure or timeout. This occurs when
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSparameters are set too low, preventing the processing of large files or complex parsing tasks. - Tool calls cannot cite file content uploaded in a custom workflow. This happens when the tool call parameter design does not account for dynamic file content referencing, instead expecting direct link input, preventing file content from being passed as a variable.
Verification Steps
- Upload multiple cardiovascular adverse drug reaction reports or literature. Check if the system correctly identifies and extracts key adverse events, drugs, and patient characteristics.
- Query for specific adverse events or drug names. Verify if the returned citation snippets accurately point to relevant descriptions in the original document and if traceability links are valid.
- Simulate different data update scenarios. Verify if the system can trace to specific data versions at particular times and display corresponding data differences.
- Check if MedDRA codes and corresponding medical terms in the citation results are consistent, preventing code-description mismatches.
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.