Data Characteristics
Data for rational drug use in clinical trial pre-screening primarily comes from clinical trial protocols, subject screening logs, medical history records, examination and test reports, and medication records. These documents are typically PDFs, DOCXs, or scanned images. They contain large amounts of unstructured text and semi-structured tabular data. Data updates frequently, especially during ongoing clinical trials, as subject status and medication adjustments are generated in real-time. Document structures are complex; for example, clinical trial protocols often include multi-level headings, charts, and appendices. Fields involve drug names, dosages, administration routes, frequencies, treatment cycles, and adverse reactions. Some fields use medical abbreviations and specialized terminology, and units are expressed in various ways (e.g., mg/kg, IU, TID), requiring precise identification and standardization.
Constraints on Document Parsing and Chunking
The complexity of clinical trial pre-screening data for rational drug use imposes multiple constraints on document parsing and chunking. First, documents contain specialized medical terminology, abbreviations, and diverse unit expressions. The parser requires high-precision entity recognition to avoid losing or incorrectly chunking critical information due to misidentification. Second, hierarchical structures and tabular data in long documents like clinical trial protocols require the parser to effectively identify and preserve contextual relationships, preventing the separation of highly related paragraphs. Third, frequent data updates mean the knowledge base needs to support incremental updates and efficient re-indexing to ensure pre-screening logic is based on the latest data. Finally, critical information such as drug dosage and frequency requires chunking to preserve the integrity of these values and units. For example, 200mg BID should not be split into 200mg and BID as separate chunks, as this would affect subsequent rational drug use judgments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Clinical trial protocols and medical records may contain many charts and scanned images, resulting in large file sizes. Support for large file uploads is necessary. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Ensures each chunk contains sufficient context while avoiding excessive length, which can lead to information redundancy and reduced recall efficiency, especially for information-dense texts like medication records. |
Overlap Length | 150–200 characters (characters) | Ensures appropriate contextual overlap between chunks, maintaining logical coherence, particularly in areas dense with medical terminology and abbreviations. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing large PDF or DOCX files can be time-consuming. Increasing the timeout prevents parsing interruptions. |
Splitting Rule | Paragraphs, Table Rows | Prioritizes splitting by semantic completeness of paragraphs. For tables, splitting by row helps maintain the independence of each medication record. |
Entity Recognition | Enable, and configure medical terminology dictionary | Accurately identifies key entities such as drugs, dosages, and frequencies, improving parsing quality and subsequent recall accuracy. |
Common Mistakes
- Uploading a large clinical trial protocol results in the system being unresponsive for an extended period or displaying a parsing failure. This occurs because
PARSE_FILE_TIMEOUT_SECONDSis set too short, preventing the parser from completing processing within the allotted time. - After importing an Excel file containing medication records, automatic splitting merges multiple records into a single knowledge chunk, affecting subsequent query accuracy. This happens when the
Splitting Ruleis not configured to process table rows independently. - A dosage query for a specific drug fails to recall relevant knowledge chunks, even if explicit dosage information exists in the knowledge base. This can happen if
Entity Recognitionwas not enabled during document parsing or if the corresponding medical terminology dictionary was not configured, leading to incorrect splitting or identification of dosage values and units.
How to Verify Configuration
- Upload a typical clinical trial protocol. Check the number of parsed knowledge chunks and their content completeness, ensuring key sections and tabular data are effectively extracted.
- Upload an Excel or DOCX document containing complex medication records. Cross-reference 10 randomly selected medication records to confirm each record exists as an independent and complete knowledge chunk in the knowledge base.
- Perform precise queries for core entities such as drug names, dosages, and administration frequencies already in the knowledge base. Verify that recall results are accurate and include the expected contextual information. Compare these results with the original documents to confirm that the recalled knowledge chunks support rational drug use judgments.
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.