Model Integration and Configuration for Pharmacovigilance in

Data Characteristics

Raw data in pharmacovigilance for laboratory services originates from clinical trials, post-market surveillance, and real-world studies. This data mixes structured and unstructured formats. Structured data includes laboratory results, patient demographics, and medication records from Case Report Forms (CRFs). This data typically resides in databases and updates frequently, possibly daily or weekly. Unstructured data encompasses medical reports, imaging reports, pathology reports, handwritten doctor's notes, and patient interview records. These documents are often PDFs, DOCX files, or plain text. They vary in format and length, containing extensive medical terminology, abbreviations, and specific units like mg/dL, IU/L, ng/mL. Data fields are complex, potentially involving medical coding systems like SNOMED CT or LOINC.

Constraints Imposed by Data Characteristics on Model Integration and Configuration

The mixed structure of laboratory service data imposes specific requirements on model integration. Frequently updated structured data needs real-time or near real-time synchronization mechanisms. This ensures model analysis uses the latest information. The diversity of unstructured documents, especially text with extensive medical terminology and specialized units, requires robust text parsing and entity recognition capabilities during preprocessing. This extracts key information accurately, such as drug names, adverse events, dosages, and temporal relationships. Document length and complexity dictate chunking strategy selection. Overly long text can lead to context loss. Overly short chunks can break semantic integrity. Additionally, the heterogeneity of different data sources makes data cleaning and standardization critical before model integration. This eliminates ambiguity and improves model understanding accuracy.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`32000 token`	Balances long document context understanding with inference efficiency.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Adapts to the paragraph structure of medical reports, preventing semantic fragmentation.
`Chunk overlap` (Chunk Overlap)	`100 characters` (characters)	Ensures contextual continuity at chunk boundaries, improving recall.
`Embedding Model`	`text-embedding-ada-002` or `bge-large-zh`	Performs well in the biomedical domain and supports specialized vocabulary.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall precision with generalization ability, reducing false positives.
`Recall count` (Number of Retrieved Items)	`Top 5–8 entries` (top 5–8 items)	Covers key information, reducing irrelevant noise's impact on model inference.

Common Pitfalls

Missing or incorrectly identified key medical entities (e.g., drug names, adverse events) in model results. This occurs due to insufficient embedding model understanding of specific medical terms or improper entity recognition rule configuration during document preprocessing.
PARSE_FILE_TIMEOUT_SECONDS timeout errors when processing large volumes of unstructured reports. This may be due to inefficient file parser handling of complex PDF or DOCX documents, or insufficient server resources.
Model inability to link medication information and laboratory results for the same patient across different reports. This happens when the unique identifier patient_id is not correctly mapped during import, leading to data silos during knowledge base construction.

Validation Steps

Upload a batch of test documents containing common adverse event reports and laboratory results. Check if the model accurately extracts and links key information such as drugs, adverse events, dosages, and time units.
For typical medical queries, such as "adverse reactions of a certain drug in patients with abnormal liver function," verify if the model's results include relevant laboratory indicators (e.g., ALT, AST) and corresponding value ranges.
Monitor oneapi interface response times and concurrent connections. Ensure stable model service operation under high load, without HTTP 500 or HTTP 502 errors.
Randomly select documents from the knowledge base. Use the GET /api/v1/document/{docId}/chunks interface to check if the chunking strategy works as expected, avoiding overly long or short text blocks.

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.