Data Characteristics
Regulatory clinical trial pre-screening data primarily originates from official regulatory websites, CRO (Contract Research Organization) trial protocols, investigator brochures, informed consent forms, ethics approvals, and clinical trial reports. These documents typically have a low update frequency, with changes mainly occurring during protocol revisions, safety report submissions, and final report releases. Documents are mostly unstructured text, often containing numerous tables and figures. These tables and figures display subject screening criteria, exclusion criteria, dosage information, adverse event reports, and statistical analysis results. Common fields include "inclusion criteria," "exclusion criteria," "primary endpoint," "secondary endpoint," "drug name," "dosage unit" (e.g., mg/kg, IU), and "dosing frequency" (e.g., QD, BID). Units cover various medical and pharmaceutical standards, such as mmol/L, ng/mL, and mmHg.
Constraints Imposed by Data Characteristics on Document Parsing and Chunking
The unstructured nature and extensive use of specialized terminology in regulatory submission data demand high precision in document parsing. Complex table structures and nested information in documents make it difficult for traditional text chunking methods to accurately extract key data. For example, inclusion and exclusion criteria in trial protocols often list multi-level logic, requiring precise identification of parallel and hierarchical relationships between conditions. Accurate recognition and semantic understanding of medical and pharmaceutical terms directly impact pre-screening accuracy. Furthermore, different document types (e.g., ethics approvals versus clinical trial reports) vary significantly in information density and key information location, requiring chunking strategies to be type-adaptive. The low frequency of data updates means initial parsing accuracy is critical, as later modifications are costly. For units like mg/kg and IU, ensure they are correctly associated with numerical values during chunking to avoid semantic loss.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Balances contextual completeness with retrieval efficiency. Avoids excessively long chunks diluting key information or excessively short chunks losing context. |
Chunk Overlap Length | 50–100 characters | Ensures key information spanning across chunks can be retrieved, especially for statements involving logical conditions. |
File Type Whitelist | PDF, DOCX, XLSX, TXT | Covers common document formats used in regulatory submissions, ensuring key information sources can be processed. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accounts for the potentially long parsing time of large clinical trial reports, providing sufficient processing time. |
Table Parsing Mode | Structured Table Extraction | Clinical trial data often appears in tables. Precise extraction of rows, columns, and cell content is required. |
Named Entity Recognition | Medical Entities (Diseases, Drugs, Symptoms, Dosage Units) | Identifies specialized terminology to improve retrieval accuracy and support subsequent semantic understanding and condition matching. |
Common Mistakes
- After document upload, some table data does not display correctly in the knowledge base, leading to incomplete query results. This occurs when the default text chunking strategy fails to effectively identify and parse complex table structures, or treats table content as plain text.
- During retrieval, some critical inclusion or exclusion criteria are not recalled, or recalled chunks lack important dosage or frequency information. This happens when the chunk length is set too short, truncating a complete logical condition, or when chunk overlap is insufficient to connect key phrases.
- After knowledge base construction, queries for specific drug dosages do not return expected results, even when explicitly mentioned in the document. This may be because the vector model does not correctly understand the semantics of specialized medical units (e.g.,
µg/mL) or their association with numerical values, leading to significant differences in embedding vectors.
How to Verify Configuration
- Upload a clinical trial protocol containing complex tables. Use the knowledge base preview function to check if table content is correctly extracted and displayed in a structured format. Verify that key fields (e.g., subject ID, dosage) are not missing.
- Select a text segment from a document containing multiple inclusion and exclusion criteria. Test different chunk length and overlap length configurations to see if the knowledge base recalls chunks containing complete logical conditions. Evaluate the contextual completeness of the recalled chunks.
- Use query statements containing specific medical terms and dosage units to test the knowledge base's recall capability. Check if these specialized terms and units are accurately identified in the returned chunks and associated with relevant numerical information.
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.