Data Characteristics
Phase I clinical trial R&D documents originate from sponsors, CROs, and research centers. These documents include research protocols, informed consent forms, ethics approvals, case report forms (CRFs), laboratory test reports, pharmacokinetic reports, safety reports, and statistical analysis plans and reports. Data updates are frequent during the trial, involving subject enrollment, visits, adverse event records, and sample test results. A single trial typically lasts several months to a year. Document structures are relatively standardized, following guidelines like ICH-GCP, but specific phrasing and formats vary between institutions. Core fields include subject ID, visit date, drug dosage, vital signs, adverse event descriptions, laboratory indicators (e.g., liver and kidney function, complete blood count), and PK/PD parameters. Units are diverse, requiring handling of mg/kg, ng/mL, mmHg, and ℃.
Constraints on Deployment and Upgrades
Deploying structural analysis for Phase I clinical documents requires considering data source complexity and update real-time requirements. The multi-source heterogeneous nature of documents demands strong multi-format file processing capabilities from the RAG system and tolerance for some degree of non-standardized structure. Frequent data updates mean higher frequency for index rebuilding or incremental updates, which stresses system resources and processing efficiency. Accurate identification and unit conversion for critical fields like safety and PK/PD are central. This requires high accuracy for specific entity recognition (NER) and relationship extraction during model parsing. Additionally, data sensitivity (patient privacy) mandates localized deployment and data isolation, restricting public cloud service options.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Phase I clinical documents can contain many images or scanned copies, leading to large single files. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large or complex PDF documents can take a long time; this avoids parsing timeouts. |
Chunk size | 800–1200 characters | Ensures completeness of information in a single segment while controlling context window size, balancing detail and overall coherence. |
Recall count | Top 8–12 entries | Increases recall rate for relevant information, covering multi-dimensional clinical data. |
Similarity threshold | 0.75 | Based on Phase I clinical data characteristics, this avoids too much irrelevant information while ensuring critical information is not missed. |
Rerank result count | Top 5 entries | Selects the most relevant information, reduces model processing burden, and focuses on core issues. |
Common Pitfalls
- Symptom: A locally deployed FastGPT cannot connect to a DeepSeek model on Ollama, showing connection timeouts or unresponsiveness. Reason: The Ollama service is not listening on an external IP address, or the server firewall has not opened the DeepSeek model port.
- Symptom: After uploading a document, parsing progress stalls for a long time or reports an error, with logs indicating an unsupported file type or parsing failure. Reason: Phase I clinical documents contain many scanned copies or non-standard PDFs; the OCR engine or parser configuration is insufficient to effectively extract text content.
- Symptom: In the structural analysis results, critical fields like drug dosage and laboratory indicators are incorrectly identified or units are missing. Reason: Model training data or prompts do not sufficiently cover the unique field expressions, abbreviations, and unit conversion rules specific to Phase I clinical trials.
Verification Steps
- Upload and parse a typical Phase I clinical trial protocol containing charts and text. Check if all section titles and body content are fully extracted.
- Upload a subject CRF. Verify if key fields (e.g., subject ID, visit date, adverse event description) are correctly identified and structured.
- Test with a PK/PD report containing specific drug dosages and laboratory indicators. Check if the numerical values and units in the parsed results are accurate and consistent with the original document.
The values provided are common starting points and should be measured against the reader's 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.