Deployment and Upgrades for Phase I Clinical

Data Characteristics

Phase I clinical trial pharmacovigilance data primarily comes from patient case reports, laboratory test results, and investigator-submitted Adverse Event (AE) and Serious Adverse Event (SAE) reports during the trial. Data updates are frequent, especially during the trial, as AE/SAE reports can be generated in real-time. Document structures typically follow the ICH GCP E2B standard. These documents include patient demographics, medication history, adverse event details (onset time, description, outcome, severity, causality assessment), and abnormal laboratory values. Field units are strict. For example, drug dosages are in milligrams (mg) or grams (g), and laboratory indicators like creatinine are in micromoles/liter (µmol/L) or milligrams/deciliter (mg/dL). Timestamps are precise to the hour or even minute. The data volume is relatively small, but descriptive text content for individual events is rich.

Constraints on Deployment and Upgrades from Data Characteristics

The high frequency and real-time nature of Phase I clinical pharmacovigilance data challenge FastGPT's data ingestion and indexing update mechanisms. The system must support near real-time data synchronization or incremental updates to ensure the AI Agent accesses the latest pharmacovigilance information. The standardized ICH GCP E2B document structure facilitates data parsing but requires strict adherence to specific XML or JSON format parsers. This ensures all critical fields are correctly extracted and structured. Adverse event descriptions contain extensive medical terminology and abbreviations, requiring robust medical domain vocabularies and entity recognition capabilities. Phase I clinical trials have short durations, demanding high deployment efficiency and smooth upgrades to avoid data processing interruptions due to system maintenance. The strictness of field units requires numerical precision retention and unit identification/conversion during data cleaning and vectorization.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Phase I clinical reports are often structured data, and individual files are typically small; this setting is sufficient.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Most E2B reports are complex to parse; this provides ample time to avoid parsing timeouts.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances completeness of adverse event descriptions with retrieval efficiency, avoiding splitting critical information.
`Recall count` (Recall Count)	`Top 5 entries` (Top 5)	Phase I data volume is relatively small; increasing recall helps capture all relevant events.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recalled context is highly relevant to the query, filtering out unrelated medical text.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (Top 3)	Selects the most relevant few events for in-depth analysis and display based on high recall.

Common Pitfalls

The AI Agent misunderstands or omits critical medical terms when answering adverse event-related questions. This occurs because the knowledge base construction lacks pre-processing or entity recognition configuration for Phase I clinical medical vocabulary.
The system fails to reflect newly submitted adverse event reports promptly, leading to outdated information from the AI Agent. This happens when the data synchronization strategy is not configured for incremental updates or real-time monitoring, or the indexingInterval parameter is set too long.
When importing a batch of E2B format reports, some files fail to parse and report XML_PARSE_ERROR. This indicates the parser does not fully adapt to all E2B versions or specific vendor XML variations, requiring parser module updates or custom parsing rules.

Verification Steps

Upload a Phase I clinical trial report containing a typical adverse event description. Verify the system correctly parses it and generates knowledge snippets. Check that key fields like AE_TERM and ONSET_DATE are accurate.
Simulate submitting a new SAE report. Observe if the AI Agent can retrieve and reference this new data within a short period (e.g., 5 minutes - 5 minutes).
Use query statements containing specific medical terms and dosage units, such as "patient experienced adverse reaction of creatinine 200 µmol/L." Check if the AI Agent's response precisely matches and explains this information.

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.