Data Characteristics for This Category
Data for rational drug use clinical trial pre-screening primarily originates from multiple sources. These include Electronic Medical Record (EMR) systems, drug inserts, medical guidelines, clinical trial protocols, and pharmacology databases. Data update frequencies vary. Drug inserts and medical guidelines may be updated annually or revised based on regulatory requirements. Clinical trial protocols can be dynamically adjusted during a study. EMR data is generated in real-time. In terms of document structure, drug inserts are typically PDF files. They contain structured fields like indications, contraindications, and dosage, but also include extensive unstructured descriptive text. Medical guidelines are often lengthy Word or PDF documents. Pharmacology databases primarily consist of structured data tables with fields such as drug name, mechanism of action, drug interactions, and adverse reactions. Units are usually explicit, for example, dosage in milligrams (mg) and action time in hours (h).
Constraints Imposed by These Characteristics on Deployment and Upgrades
The diversity of rational drug use data sources and varying update frequencies impose specific constraints on deployment and upgrades. The real-time nature of EMR data requires the system to support high-concurrency processing and low-latency data ingestion mechanisms to ensure timely pre-screening. The unstructured nature of drug inserts and medical guidelines makes text parsing and knowledge extraction critical, demanding strong NLP capabilities. This may require periodic re-indexing to reflect updated content. The heterogeneity of different data sources necessitates flexible data models and mapping capabilities within FastGPT's data integration layer. Furthermore, the structured nature of pharmacology databases makes precise field identification and matching fundamental for improving pre-screening accuracy. Deployment requires targeted configuration of data source connectors and field mapping rules. The coexistence of multiple document versions also demands robust knowledge base version management and rollback capabilities.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 characters | The effective information length of a single paragraph in drug inserts and medical guidelines typically falls within this range. |
Chunk size (Segment Length) | 500 characters | Balances semantic integrity of long texts with retrieval efficiency, avoiding excessive fragmentation. |
Recall count (Recall Count) | 8 entries | Covers multiple potentially relevant pharmacological knowledge points and clinical guideline recommendations, increasing recall rate. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall accuracy and recall rate, reducing interference from irrelevant results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDF medical guidelines and drug insert documents, preventing timeouts. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports the need for uploading large medical guidelines and multiple drug inserts in batches. |
Three Common Mistakes
- Pre-screening results are still based on old data after a knowledge base update. This occurs because the knowledge base index was not rebuilt promptly or the cache was not refreshed.
- Docker containers fail to connect to the database after startup. This is typically due to container network configuration issues, preventing the container from resolving or accessing host or external database addresses.
- Key information like drug dosage or frequency is missing or inaccurate in pre-screening results. This happens when units or numerical values in original documents are not correctly identified and extracted, or when field mapping errors exist during knowledge base construction.
How to Verify Correct Configuration
- Upload the latest version of a drug insert. Verify the system can correctly parse the document structure and extract key fields like indications, contraindications, and dosage.
- Simulate typical patient cases. Input drug names and relevant symptoms. Check if pre-screening results accurately recall relevant drug interactions and adverse event information. Compare these results against known medical guidelines to confirm appropriate threshold settings.
- Conduct concurrent pre-screening request tests during peak hours. Observe if system response times meet business requirements. Check database connections and resource utilization.
- After upgrading the FastGPT version, verify that all configured data source connectors and knowledge base indexes are functioning correctly. Ensure no disconnections or data loss.
Note that the values provided are common starting points. They 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.