Data Characteristics for This Category
Phase I clinical research data primarily originates from study protocols, case report forms (CRFs), informed consent forms, ethics approvals, laboratory test reports, and safety follow-up records. These documents typically have stable update rhythms after study initiation, but undergo concentrated updates during protocol revisions or severe adverse events. Document structures are predominantly semi-structured and unstructured. For example, informed consent forms and study protocols contain extensive narrative text, while CRFs and laboratory reports include tabular data. Fields and units, such as medical terminology, dosage units (e.g., mg/kg, mL), time units (e.g., days, weeks), biomarker units (e.g., ng/mL, U/L), and safety event classification codes, demand high specialization and standardization.
Constraints Imposed by These Characteristics on "Model Integration and Configuration"
The highly specialized and semi-structured nature of Phase I clinical R&D documents imposes specific requirements on FastGPT's model integration and configuration. First, medical terminology and abbreviations within documents require strong semantic understanding from the model for accurate identification and association. Second, safety events and adverse reaction reports often exist as unstructured text. However, structured parsing requires extracting specific entities (e.g., event name, occurrence time, severity), which demands precise entity recognition and relationship extraction capabilities from the model. Numerical values and units in laboratory test reports require the model to correctly distinguish values from units during data extraction and handle potential unit conversion needs. Furthermore, since document updates may involve protocol revisions, the knowledge base must support incremental updates and version management to ensure the model always reasons based on the latest data. For image-based reports (e.g., ECGs, imaging reports), image understanding models are necessary to assist in parsing and extracting key information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Phase I clinical documents often contain long narratives, requiring a sufficiently large context window to capture complete semantics. |
Chunk size (Segment Length) | 300 characters | Balances semantic completeness with retrieval efficiency, preventing long paragraphs from diluting key information. |
Recall count (Retrieval Count) | Top 5 | Ensures the model obtains enough relevant snippets to handle complex queries. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures retrieved document snippets are highly relevant to the query, filtering out noise. |
Rerank result count (Reranked Return Count) | 3 | Further refines the most relevant snippets from high-quality recall for model inference. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large study protocols or safety reports, preventing parsing timeouts. |
Three Common Mistakes
- Knowledge base query results lack generalization ability, leading to model refusal to answer: This often occurs because the knowledge base segmentation strategy is too conservative, causing key information to be fragmented, or the
Similarity threshold(Similarity Threshold) is too high, failing to retrieve sufficient relevant context. - Model cannot extract information from image reports: The image understanding model was not configured or enabled during knowledge base creation, preventing the model from processing image-based laboratory test reports or imaging data.
- Inaccurate safety event extraction, missing or misplaced fields: This may be due to
maxContextbeing set too small, preventing the model from obtaining a complete event description, or the entity recognition model not being sufficiently fine-tuned for medical entities.
How to Confirm Proper Configuration
- Upload an informed consent form containing complex medical terminology and multiple narrative sections. Verify the model's ability to accurately identify and extract key information.
- Submit a query about a specific adverse event. Check if the model can retrieve relevant paragraphs from safety follow-up records and perform inference.
- Upload a case report form containing tabular data. Verify the model's ability to correctly parse the table structure and extract specified field values and units.
- Perform an incremental update on the knowledge base, then query the updated content. Confirm the model can access and utilize the latest data for answering.
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.