Data Characteristics
Hematological oncology pharmacovigilance data originates from national drug adverse event monitoring centers, spontaneous reports from healthcare institutions, clinical trial data, academic literature, and public databases from global drug regulatory agencies. Data update frequencies vary. Post-market surveillance data typically updates quarterly or annually. Clinical trial data may update in real-time with research progress. Document structures are primarily semi-structured and unstructured. These include adverse drug reaction report forms (MedWatch, CIOMS I forms), medical records, medical imaging reports, and laboratory test results. Fields and units are highly specialized. For example, adverse event descriptions often contain medical terminology (e.g., "thrombocytopenia," "neutropenia"). Dosage units are precise, down to milligrams (mg) or international units (IU). Time units involve hours, days, and weeks. Risk assessment often requires combining these with biological indicators like patient age, weight, and liver/kidney function.
Constraints on Deployment and Upgrade
The multi-source and specialized nature of hematological oncology pharmacovigilance data imposes specific deployment requirements for FastGPT. Data update frequencies are irregular and may have delays. Deployment requires flexible data synchronization mechanisms to adapt to varying data source update cycles. The presence of semi-structured and unstructured documents necessitates a focus on medical terminology standardization and entity recognition during data preprocessing. This directly influences tokenization strategies and vectorization model selection. The precision of fields like dosage and time means knowledge base construction requires detailed field mapping and data type definitions to ensure query accuracy. The specialized nature of hematological oncology requires the model to understand complex medical concepts. This may involve introducing domain-specific word vectors or knowledge graphs during model fine-tuning, increasing resource demands for deployment and model compatibility considerations during upgrades.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Accommodates long sentences and complex descriptions in medical literature, ensuring context completeness. |
Chunk size (Segment Length) | 400–600 characters | Balances semantic completeness of text with recall efficiency, avoiding excessive truncation of key information. |
Recall count (Recall Count) | 10–15 items | Accounts for the complex associations of adverse event incidents, increasing recall to improve coverage. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures high matching accuracy between query results and professional medical terminology, reducing false positives. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time when processing large medical records or report files. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large files, including medical images and PDF reports. |
Common Pitfalls
- Query results lack critical associated information for adverse drug reaction events. This occurs because the tokenization strategy fails to effectively identify compound medical terms, leading to vectorization bias.
- The system frequently encounters parsing timeout errors when processing specific formats of clinical trial reports. This happens due to complex document structures containing numerous tables or embedded objects, where the default parser's performance is insufficient.
- After an upgrade, field values in some historical adverse reaction reports appear empty or inconsistent. This is because the new version's data model has changed field mappings from the old version, and compatibility handling was not performed.
Verification
- Submit a series of test queries containing complex medical terminology and dosage information. Verify that the returned results accurately link to relevant adverse event reports. Check that key fields (e.g., adverse event name, drug dosage) are complete.
- Upload multiple hematological oncology pharmacovigilance documents from different sources and formats (e.g., MedWatch forms, PDF medical records, JSON-formatted laboratory reports). Observe their parsing success rate and the completeness of content extraction.
- Simulate high-concurrency knowledge base query scenarios. Monitor system response time and resource utilization. Ensure stable performance when handling a large volume of specialized queries.
- Compare recall results and similarity scores for the same queries before and after an upgrade. Verify whether the upgrade's impact on knowledge base retrieval performance meets expectations.
The values provided are common starting points and should be measured against the reader's 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.