Model Integration and Configuration for Pharmacovigilance in

Data Characteristics in This Domain

Data for pharmacovigilance in real-world studies comes from various sources. These include Electronic Health Records (EHRs), medical claims databases, registries, wearable devices, and patient reports. Data typically exists as unstructured text (e.g., clinical notes, medical record summaries), semi-structured data (e.g., diagnostic codes, medication records), and structured data (e.g., laboratory results, demographic information). Update frequencies vary; EHR data might update in real-time, while registry data could update quarterly or annually in batches. Document structures are diverse. For example, free-text sections in EHRs contain extensive medical terminology, abbreviations, and colloquialisms, lacking a unified format. In contrast, claims data have clear field definitions, such as ICD-10 diagnostic codes, NDC drug codes, administration routes, and dosage units.

Constraints on Model Integration and Configuration

The wide range and heterogeneity of data sources require robust data preprocessing capabilities at the model integration layer. This ensures consistent formatting and encoding. For instance, specialized entity recognition and standardization for medical terminology are necessary. Varying update frequencies mean knowledge base indexing strategies need flexible adjustments. For high real-time data sources, configure more frequent incremental update mechanisms. The complexity of document structures, especially unstructured text, challenges segmentation strategies. Both overly long and overly short segments can affect recall quality. Additionally, diverse fields and units, such as drug dosage unit conversions, require standardization during the data cleaning phase. This prevents ambiguity in model understanding and inference. For example, different databases might use mg and milligrams for the same unit, requiring unified processing.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances context completeness with model processing capacity, suitable for texts with longer clinical descriptions.
`Overlap Length`	`100–200 characters` (characters)	Ensures contextual continuity at segment boundaries, reducing information loss.
`Recall count` (Recall Count)	`Top 5–8 entries` (top 5–8 items)	Balances accuracy with model input token limits, covering potentially relevant information.
`Similarity threshold` (Similarity Threshold)	`0.75`	Filters highly relevant knowledge snippets, addressing the semantic precision requirements of medical texts.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Accommodates parsing time for large medical record files or complex reports, preventing timeout interruptions.
`embeddingModel`	`text-embedding-ada-002` or `bge-large-zh`	Selects an embedding model that performs well in the medical domain to accurately capture semantic information.

Common Pitfalls

Model list incomplete or unable to select the latest model: This usually results from an incorrect ONEAPI_BASE_URL configuration or insufficient key permissions. The system cannot correctly retrieve or authenticate the model list provided by the model service provider.
Knowledge base file upload fails to parse and restarts indefinitely: This often occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low. Large or complex documents fail to complete parsing within the allotted time, triggering a container restart.
Query results fail to effectively utilize image or chart information: The knowledge base lacks specialized image content extraction and description capabilities. This causes image links to be treated as plain text, failing to convert into model-understandable descriptive content.

How to Verify Configuration

Upload typical medical record summaries and drug inserts. Check if knowledge base segmentation is reasonable, without obvious semantic truncation.
Use query statements containing specific medical terms or abbreviations. Verify if recall results include highly relevant knowledge snippets and check the similarity score.
Test real-world study reports of varying lengths and complexities. Observe if the file parsing process is smooth, without timeout errors, and check the knowledge base document status.
For the model support list, confirm that all models provided by the configured model service provider can be correctly selected and invoked, such as glm-4 or spark-4.0.

Note: 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.