Deployment and Upgrade for Real-World Evidence Registration

Data Characteristics in this Category

Real-World Evidence (RWE) registration and declaration documents draw from diverse data sources. These primarily include Electronic Health Records (EHR), medical insurance claims databases, disease registries, and patient-reported outcomes (PRO) data. This data typically exists as unstructured text, semi-structured tables, and structured database records. Update frequencies vary; EHR data might update in real-time, while medical insurance claims data usually aggregates quarterly or annually. Document structures are complex, encompassing clinical records, laboratory reports, imaging results, and follow-up records. They often involve extensive medical terminology, abbreviations, and specific coding systems (e.g., ICD-10, LOINC). Fields and units present a large volume of numerical data (e.g., dosage mg, duration days), categorical data (e.g., diagnosis results, administration routes), and free-text data (e.g., physician diagnostic descriptions). Unit systems may also be inconsistent, requiring additional preprocessing and standardization.

Constraints Imposed by these Characteristics on "Deployment and Upgrade"

The diversity and complexity of RWE data impose specific requirements on FastGPT's deployment environment and upgrade strategy. The heterogeneous nature of data sources necessitates robust file parsing capabilities and flexible data import mechanisms to handle various document formats and database export files. High-frequency data updates demand incremental update and version management capabilities for the knowledge base, preventing duplicate imports and data redundancy. The unique medical terminology and coding systems within documents require configuring domain-specific embedding models and vocabularies to enhance semantic understanding accuracy. The presence of extensive free-text data mandates more refined knowledge segmentation strategies to ensure contextual completeness. Simultaneously, sensitive patient information requires private deployment and strict configuration for data anonymization and permission management.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	RWE documents often include large PDFs or image files; this ensures single-upload capability.
`maxContext`	`1500 characters`	Clinical records and diagnostic descriptions are often lengthy; this increases context length to capture complete semantics.
`Chunk size` (Segment Length)	`300 characters`	Fine-grained segmentation helps accurately capture the semantics of medical terms and short sentences, improving recall.
`Recall count` (Recall Count)	`10 items`	Increasing the recall count covers more potentially relevant clinical details and research evidence.
`Similarity threshold` (Similarity Threshold)	`0.75`	RWE document semantic similarity requires a higher standard; this raises the threshold to filter out irrelevant results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Prevents parsing timeouts when processing large or complex documents (e.g., scanned PDFs with many images).

Three Common Pitfalls

Knowledge base query results omit critical information, manifesting as missing specific diagnoses or treatment plans in responses. This occurs when the segment length is set too large, leading to critical information being truncated or mixed with irrelevant content.
When importing large clinical study reports, the system displays a File Parsing Timeout (file parsing timeout) error. This happens because the PARSE_FILE_TIMEOUT_SECONDS parameter value is too small, not allowing sufficient time for complex documents to parse.
After private deployment, the knowledge base fails to recognize medical domain-specific abbreviations and disease codes, resulting in poor retrieval performance. This is due to not configuring or updating domain-specific embedding models and word vectors, leading to insufficient semantic understanding.

How to Confirm Correct Configuration

Upload an RWE document containing complex medical terminology and lengthy descriptions. Check if it imports successfully and segments correctly.
Use a query containing specific disease codes (e.g., ICD-10 I20.9). Verify if the returned results accurately link to relevant document segments.
Simulate different data update frequencies. Test the knowledge base's incremental update function to confirm new data is indexed and retrieved promptly.
Perform multiple question-and-answer sessions on imported documents. Evaluate the accuracy of key numerical values (e.g., dosage mg, cycle weeks) and the completeness of context in the responses.

The values provided are common starting points. Measure them against your own samples to determine the most suitable configuration.

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.