Data Characteristics
Data for respiratory clinical trial pre-screening originates from electronic health record systems, lab and imaging reports, and historical clinical trial documents. Data updates frequently, especially during patient visits and follow-ups. Document structures vary, including unstructured free text, semi-structured tabular data, and structured field information. For example, imaging reports often describe lung CT and X-ray results, including key information like lesion size, location, and nature. Lab reports contain complete blood count, biochemical indicators, and pulmonary function test results, often with clear numerical values and units. Historical clinical trial reports may include study protocols, inclusion/exclusion criteria, and patient baseline characteristics, with greater length and complexity than single visit records. Common fields include patient ID, diagnosis, medication records, complications, and vital signs, with units such as mg/dL, kPa, and ml/s.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The diversity of respiratory data demands advanced document parsing capabilities. Unstructured free text, such as doctors' clinical notes, requires sophisticated named entity recognition and relation extraction to accurately identify disease names, medications, dosages, and symptoms. Semi-structured tabular data, like FEV1 and FVC values in pulmonary function reports, requires accurate table parsing to prevent data misalignment. Lengthy clinical trial reports, with their strong contextual dependencies, challenge chunking strategies. Chunks that are too short may lose critical information, while chunks that are too long may introduce noise. Identifying and standardizing numerical fields and units is another critical constraint. For example, the same indicator may have different unit representations, requiring conversion for subsequent numerical comparison and screening. The high frequency of data updates requires the document parsing process to have high throughput and low latency to adapt to rapidly changing clinical information.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Balances contextual completeness and retrieval efficiency. Avoids overly large chunks with excessive irrelevant information or overly small chunks that lose semantic meaning. |
chunk_overlap | 100–200 characters | Ensures semantic continuity at chunk boundaries, especially for clinical trial reports with complex logic and long sentences. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the upload of large PDF files, such as clinical trial reports containing extensive images and charts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient time to process complex PDF documents, preventing parsing timeouts. |
chunk_strategy | recursive_character | Suitable for handling mixed-structure documents, flexibly adapting to free text, tables, and structured data. |
ocr_enabled | true | Ensures content from scanned or image-based reports can be recognized and parsed. |
Common Pitfalls
- "Offset out of range" errors occur when uploading large PDF files because the file size exceeds system configuration or network transmission limits.
- Key pulmonary function indicators (e.g., FEV1) are not correctly extracted after document parsing because the parser is not optimized for specific medical terminology and numerical units.
- Files uploaded via API have inconsistent chunking compared to files uploaded directly through the platform because the API call did not specify or used a different chunking strategy than the platform's default.
How to Verify Configuration
- Upload a respiratory clinical trial report containing free text, tables, and images. Check if chunked content completely retains information from all sections.
- Upload multiple inspection and lab reports in different formats (e.g., PDF, images). Verify that key numerical fields and their units are accurately identified and standardized.
- Use the knowledge base Q&A function to ask about specific symptoms, diagnoses, or treatment plans. Confirm that relevant information can be effectively retrieved from the parsed documents.
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.