Data Characteristics
Phase I clinical trial prescreening involves early data from healthy volunteers or a small number of patients. The primary goal is to assess drug safety, tolerability, and preliminary pharmacokinetic (PK) and pharmacodynamic (PD) profiles. Data sources are diverse, including subject recruitment questionnaires, physical examination reports, laboratory test results (e.g., complete blood count, liver and kidney function, electrocardiogram), imaging reports, and adverse event (AE) records. This data exists in both structured (e.g., numerical, categorical variables in clinical databases) and unstructured (e.g., physician notes, imaging report text) forms. Data update frequency is high during the early stages of a trial, especially during dosing and follow-up, with new data potentially generated daily or even hourly. Document structure adheres to ICH GCP guidelines. Fields include dosage, administration route, vital signs, and AE grading. Units are strict, such as mg/kg, mmol/L, and mmHg.
Deployment and Upgrade Constraints
High sensitivity and strict compliance requirements for Phase I clinical trial data demand high standards for deployment environment security and data isolation. Frequent data updates and mixed data types (structured and unstructured) require the model to process and integrate information in real-time. Unstructured text data contains extensive medical terminology and abbreviations, necessitating specialized medical domain knowledge from the model. Strict unit and field specifications mean the RAG (Retrieval Augmented Generation) system must precisely match and understand context during recall and generation to avoid misleading information. Furthermore, early trial protocol adjustments can lead to data schema changes, requiring the system to have flexible schema adaptability and version management capabilities. Isolation and resource allocation for execution environments like fastgpt-sandbox also require careful consideration to ensure stable and secure code execution.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large imaging reports or bulk laboratory result files. |
maxContext | 4000 characters | Covers a subject's complete medical history and current physiological indicators. |
Chunk size (Chunk Length) | 300 characters | Fine-grained segmentation of medical terminology text improves retrieval recall accuracy. |
Recall count (Recall Count) | 8 entries | Ensures coverage of multi-source data, such as laboratory, physical examination, and AE records. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall rate and accuracy, avoiding the introduction of irrelevant or ambiguous information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for complex structured files (e.g., PDFs). |
Common Pitfalls
- AI chat variables cannot obtain code execution output after an upgrade: This usually indicates incompatibility between the
fastgpt-sandboxcontainer version and the main program, leading to changes in data transfer interfaces or serialization failures in the sandbox environment. fastgpt-sandboximage not found after deployment: This often occurs because the Docker daemon's configured image source is inaccessible or lacks pull permissions, causing image download failure or blockage by a firewall.ollamacannot be called after local model configuration: Common causes include inconsistent model names or port configurations in the.env.localfile compared toollama's actual running parameters, oroneapichannel authentication failure.
Verification Steps
- Upload a PDF file containing complex medical terminology. Check if the file parser correctly extracts all text content and if the segmentation results meet expectations.
- Using FastGPT's debugging interface, simulate a clinical trial prescreening query. Observe if the RAG-recalled document snippets are highly relevant to the query intent and include information from different data sources.
- Execute a workflow involving
fastgpt-sandbox. Check if code execution results are correctly passed back to the AI chat, ensuring proper communication between the sandbox environment and the main program. - Engage in a conversation using the configured local model. Check response speed and content accuracy, ensuring the model can handle medical professional questions.
The values provided are common starting points and should be measured against specific 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.