Data Characteristics for This Category
Hematologic oncology clinical trial pre-screening data primarily comes from patient electronic medical records, genetic testing reports, imaging examination results, and previous treatment records. This data updates frequently. Specific indicators during treatment can have new records daily or even hourly. Document structures are complex. They include unstructured physician diagnostic descriptions, structured laboratory test results, and semi-structured gene mutation site information. Field and unit specificities are diverse. For example, gene mutation frequency often uses percentages. Tumor burden may use log values or copies/mL. Drug concentrations involve ng/mL or μmol/L. Different testing institutions also have varying report formats and field naming conventions.
Constraints on Deployment and Upgrade from These Characteristics
High-frequency data updates require efficient data synchronization and incremental indexing. This prevents inaccurate pre-screening results due to outdated data. Complex document structures and diverse field units demand stronger parsing and standardization during data preprocessing. This is especially true for unstructured text, which requires more resources for entity recognition and relationship extraction. Varying report formats mean the data ingestion layer needs more flexible adapters or smarter pattern recognition algorithms. Deployment must reserve sufficient storage and computing resources. This handles massive and heterogeneous data and ensures pre-screening response speed. During upgrades, compatibility checks for data schema changes are critical. This prevents pre-screening logic failure due to upstream data source format adjustments.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Hematologic oncology patient gene sequencing or imaging reports are large. Large file uploads require support. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing and unstructured text processing take significant time. This prevents processing failures due to timeouts. |
maxContext | 8000 tokens | Complex medical history and genetic information contain extensive text. A longer context window is needed for comprehension. |
Chunk size (Segment Length) | 500 characters | This ensures individual text segments contain sufficient information. It also avoids excessive length that leads to redundancy or parsing difficulty. |
Recall count (Number of Retrieved Items) | Top 15 entries (Top 15) | Clinical trial pre-screening needs to consider multiple relevant factors comprehensively. Increasing recall improves coverage. |
Similarity threshold (Similarity Threshold) | 0.75 | This ensures matching accuracy and reduces false positives. A high threshold ensures retrieved results are highly relevant to the query. |
Three Common Mistakes
- The frontend interface displays incompletely after an upgrade. For example, a registration button is missing. This usually happens when frontend resources are not fully updated or browser caches are not cleared. This leads to loading old version resources.
- Service startup fails after executing
docker compose pull. Errors indicate port conflicts or missing dependencies. This can occur if the new image version is incompatible with the current environment. Alternatively, a port configured indocker-compose.ymlis already in use by another process. - Custom developed features are lost or stop working after an upgrade. This happens when custom code is not correctly integrated into the new deployment process. It also occurs if core components are directly replaced during upgrade without code merging.
How to Verify Correct Configuration
- Upload a hematologic oncology patient report with complex gene mutation information. Verify the system correctly parses and extracts key fields, such as
EGFRmutation frequency. - Query with medical record text containing specific chemotherapy regimens and treatment outcomes. Verify the system recalls accurate clinical trial information covering the required treatment stages.
- Check log output. Confirm
PARSE_FILE_TIMEOUT_SECONDSis effective. Ensure no timeout errors occur when processing large report files. Verify the data indexing process is error-free. - Simulate high-concurrency pre-screening requests. Monitor system resource usage. Ensure response times remain acceptable after configuring
UPLOAD_FILE_MAX_SIZEandmaxContext.
The values provided are common starting points. Measure them against your 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.