Deployment and Upgrade for Clinical Trial Pre-screening in

Data Characteristics

Clinical trial pre-screening in health management uses data from personal health records, physical examination reports, wearable device data, and patient self-reported information. Data update frequencies vary. Physical examination reports typically update annually, wearable device data may upload in real-time, and patient self-reported information updates based on consultation frequency. Document structures are diverse. Physical examination reports are often structured or semi-structured PDFs, containing blood counts, biochemical indicators, and imaging reports. Personal health records may include medical history and medication use, often as unstructured text. Wearable device data commonly appears as time series. Common numerical fields include blood glucose, blood pressure, heart rate, and BMI, with units like mmol/L, mmHg, beats/min, and kg/m². These require precise identification and standardization.

Constraints on Deployment and Upgrade

Heterogeneous health management data sources require deployment to be compatible with multiple data formats, such as parsing structured PDFs and unstructured text effectively. Varying data update frequencies necessitate incremental update mechanisms for the knowledge base. This requires support for periodic batch updates and real-time small batch updates to avoid resource consumption from full index rebuilding. Numerous numerical indicators and their units mean that data preprocessing must include unit standardization and outlier detection, which impacts data cleaning module configuration. Additionally, the sensitive nature of personal health data mandates strict data security and privacy protection in the deployment environment, such as encrypted data storage and access control. This affects network configuration and storage policies.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates PDF files containing multi-page imaging reports or detailed physical examination reports.
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances the completeness of detailed descriptive paragraphs in physical examination reports with model processing efficiency.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Ensures large or complex PDF documents have sufficient time for parsing, preventing timeouts.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures accuracy for clinical trial pre-screening, preventing potential eligible candidates from being missed due to low similarity.
`Recall count` (Recall Count)	`Top 10`	Given the multi-dimensional nature of health management data, this increases recall to cover more relevant information.
`S3_ENDPOINT`	Internal S3 service address	Ensures health data files are stored in a controlled internal environment, complying with data security requirements.

Common Pitfalls

PDF document parsing fails, with logs showing PDF parsing timeout. This occurs when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, insufficient for parsing large or complex PDFs.
After a knowledge base update, newly uploaded physical examination report content is not retrieved. This happens when the incremental update channel for the indexing model is not configured correctly, or an external parsing service (e.g., MinerU) is not properly integrated with the FastGPT instance, leading to new data not being indexed promptly.
System retrieval results show numerical comparisons with inconsistent units, leading to incorrect pre-screening condition judgments. This is due to a lack of unit standardization for numerical fields during data preprocessing, or the parser failing to correctly identify and convert units.

Verification Steps

Upload a physical examination report PDF containing multiple pages of charts and text. Confirm the file parses correctly and its content is properly chunked and stored.
Upload a health record with only minor updates to key indicators. Verify that the knowledge base performs incremental updates and that both new and old data are retrievable.
Submit a query via API or UI containing a specific numerical value (e.g., blood glucose 5.5 mmol/L). Check if the units in the returned numerical fields are consistent and if they accurately match the predefined screening conditions.
Review system logs to confirm no abnormal error messages related to file parsing, data storage, or index construction.

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.