Data Characteristics
Rehabilitation equipment pharmacovigilance data primarily originates from post-market surveillance reports, clinical trial reports, user feedback, regulatory warnings, and academic research papers. Updates vary: manufacturer reports might be quarterly or annual, while user feedback is irregular and fragmented. Document structures typically include event descriptions, device information, patient information, medical professional assessments, actions taken, and outcomes. Fields and units are highly specialized. Examples include deviceModel, serialNumber, faultCode, adverseEventType, patient age (years/months), weight (kg), and rehabilitation metrics (e.g., ROM/°, MMT/grade). Some documents may contain rehabilitation exercise diagrams, device interface screenshots, or photos of faulty parts.
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
Specialized fields in rehabilitation equipment documents require parsers to accurately identify and extract key information. This includes formatted recognition of deviceModel and handling the variability of faultCode. Image content, such as rehabilitation diagrams or device fault photos, requires image recognition for contextual understanding. For example, identifying rehabilitation actions or abnormal device parts shown in images is crucial for RAG retrieval accuracy. Multi-column tables, common in clinical trial data or device parameter lists, require the parser to correctly identify table structures and associate row and column semantics to prevent data misalignment. Unstructured, colloquial text from user feedback and formal, standardized language from regulatory reports challenge chunking strategies, requiring semantic integrity across different text styles. Inconsistent update frequencies mean the system needs to support incremental parsing, avoid reprocessing already parsed content, and efficiently integrate new data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances the completeness of event descriptions in rehabilitation equipment reports with the model's context handling capacity. |
Chunk Overlap Length (Chunk Overlap Length) | 100 characters (characters) | Ensures semantic connection across chunks, especially for long event descriptions. |
Enable Image Recognition | Yes | Rehabilitation equipment documents often contain diagrams or photos; recognizing image content aids semantic understanding. |
Image Parsing Strategy | OCR + Image Description | Combines text recognition and visual description to comprehensively extract image information. |
Table Parsing Mode (Table Parsing Mode) | Smart Row/Column Header Recognition | Multi-column table structures in rehabilitation equipment documents are complex and require accurate data association. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accounts for the parsing time of large clinical reports or documents with numerous images/tables. |
Three Common Mistakes
faultCodefield is empty or incorrectly formatted in parsing results: This happens because fault codes have diverse representations in documents, and regular expressions or entity recognition rules are not sufficiently configured.- Image content is not recognized or described inaccurately: This occurs if
Enable Image Recognitionis not enabled, or the model's recognition capability for rehabilitation equipment-related images is insufficient. - Data misalignment after parsing multi-column tables, leading to inaccurate associated information during RAG retrieval: This is due to
Table Parsing Mode(Table Parsing Mode) failing to correctly identify table row and column relationships.
How to Verify Configuration
- Upload a document containing typical fault codes, rehabilitation training diagrams, and multi-column tables. Check if the key
faultCodefield is accurately extracted and correctly formatted in the parsing results. - Verify that image content in the parsed document chunks generates meaningful and highly relevant descriptions.
- For documents with complex multi-column tables, confirm that the row and column correspondence of table data is correct in the parsing results, ensuring data integrity.
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.