Data Characteristics
Data sources for mental health clinical trials primarily include study protocols, informed consent forms, case report forms (CRFs), medical imaging reports, scale assessment results, patient medical records, and follow-up records. These documents are often in PDF, Word, or structured table formats. Data update frequency is high, especially in multi-center clinical trials, with continuous influx of patient recruitment, follow-up data, and adverse event reports. Document structures vary: study protocols typically contain numerous nested headings, lists, and charts, while CRFs mainly consist of tables and fixed fields. Specificity of fields and units is notable; for example, psychiatric scales (e.g., HAM-D, PANSS) have specific scoring systems and interpretation standards. Medical imaging reports often include descriptive text and quantitative indicators of brain structural or functional abnormalities, and pharmacokinetic reports contain drug concentration and metabolite units.
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
The complexity of mental health clinical trial data presents unique requirements for document parsing and chunking. Complex structures and numerous charts in study protocols require parsers to accurately identify heading hierarchies and chart content, preventing information loss or incorrect segmentation. Semi-structured text in scale assessment results and medical imaging reports necessitates that chunking identifies and retains key scoring items, descriptive conclusions, and quantitative indicators, ensuring semantic completeness. High-frequency data updates require support for incremental parsing and effective management of duplicate or revised versions to avoid knowledge base redundancy or data conflicts. Furthermore, specialized terminology and abbreviations unique to the mental health domain (e.g., MMSE, ADL) require parsers to have domain-specific vocabulary recognition capabilities to ensure semantic accuracy after chunking and prevent poor recall due to term splitting.
Configuration Strategy
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800-1200 characters | Mental health documents (e.g., study protocols) often contain long paragraphs, requiring context retention; scale descriptions and diagnostic criteria also demand high contextual completeness. |
Chunk Overlap Length (Overlap Length) | 100-200 characters | Ensures continuity of specialized terms, diagnostic criteria, or treatment plan descriptions across chunks, reducing semantic discontinuity. |
Parsing Strategy | Table Recognition | CRFs and scale data are often in tabular format; accurate table structure recognition is crucial for extracting key indicators. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large study protocols or PDFs with many images and tables can be time-consuming; this prevents parsing failures due to timeouts. |
maxContext | 4000 characters | Clinical descriptions and diagnostic criteria in mental health often require a longer text window for judgment, ensuring the model receives sufficient information. |
Max Upload Size | 500 MB | Medical imaging reports and large PDF documents can be substantial in size; support for uploading them is necessary. |
Common Pitfalls
- After uploading a large PDF file, the system displays "request failed" or "parsing timeout." This might be due to the
PARSE_FILE_TIMEOUT_SECONDSparameter being set too low, failing to accommodate the parsing time for complex documents. - After importing tabular datasets into the knowledge base, some column data is missing or incompletely parsed. This usually occurs when the table parsing strategy fails to correctly identify complex table structures or merged cells.
- When a PDF document containing images or embedded charts is uploaded, relevant visual information is not parsed or converted to text. This indicates that the parsing configuration has not enabled or correctly configured an image parsing model.
Validation Steps
- Upload a study protocol PDF containing complex hierarchical headings, tables, and images. Verify that the parsed chunks fully retain heading hierarchies, table data, and image descriptions.
- Upload a psychiatric scale report, such as HAM-D or PANSS. Verify that the parsed chunks accurately extract all scoring items, scores, and corresponding interpretation text, and check field correctness.
- Perform a small-scale simulated clinical trial pre-screening query. Verify that the recall results include key information from different document types, such as patient inclusion/exclusion criteria, scale assessment results, and adverse event records. Evaluate the relevance threshold.
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.