Data Characteristics for This Category
Data for mental illness clinical trial pre-screening comes from diverse sources. These include electronic health record systems, patient self-assessment scales, clinician diagnostic reports, genomic data, and imaging data. Data update frequency varies by source. For example, electronic health records update in real-time, while genomic data might update only at specific research stages. Document structures typically include unstructured physician diagnostic text, structured scale scores, and semi-structured examination reports. Fields and units are specific. Examples include the Hamilton Depression Rating Scale (HAM-D) score, the Positive and Negative Syndrome Scale (PANSS) score, and mutation information for specific gene loci. This data often involves extensive text descriptions and is highly privacy-sensitive.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The multi-source and heterogeneous nature of mental illness data requires FastGPT to have flexible data ingestion capabilities during deployment. This is particularly true for parsing and vectorizing unstructured text. High privacy sensitivity makes data anonymization and permission management critical deployment points. Secure data transmission and storage are essential. Inconsistent update frequencies, such as the continuous influx of electronic health records, demand real-time processing capabilities and incremental update mechanisms. This prevents outdated data from affecting pre-screening accuracy. Specific fields and units, like scale scores, require customized knowledge extraction rules and ontology mapping. This ensures models effectively understand and utilize this key information. Furthermore, large data volumes necessitate advance planning for hardware resources and scalability.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Imaging reports or large genomic files for mental illness can be substantial, requiring sufficient upload capacity. |
maxContext | 8192 | Clinical diagnostic texts and medical record descriptions are often lengthy, requiring a larger context window to capture complete information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex unstructured medical record texts and scanned scale documents can be time-consuming. This prevents parsing timeouts. |
Chunk size | 800–1200 characters | Ensures each text block contains sufficient semantic information while avoiding excessive length that could degrade vectorization accuracy. |
Recall count | Top 10 entries | Mental illness diagnosis and treatment involve many reference factors. Increasing recall items improves coverage of relevant information. |
Similarity threshold | 0.78 | Clinical pre-screening demands high accuracy. Appropriately raising the similarity threshold helps reduce interference from irrelevant information. |
Three Common Mistakes
- After upgrading FastGPT, existing customized data parsing scripts or tool integrations fail. Logs show
ModuleNotFoundError. This occurs when dependency library versions change or paths adjust during the upgrade, preventing custom code from loading correctly. - The frontend interface only shows a login option, with no ability to create new users. This happens if the
ALLOW_REGISTERenvironment variable is not correctly set totrueduring deployment, or if this configuration is not mapped into the container in a Docker Compose deployment. - Knowledge base retrieval results are missing specific scale scores or gene locus information, or the format is incorrect. This indicates that during the data preprocessing stage, extraction rules for mental illness-specific fields were insufficient. Key information was not correctly structured before vectorization.
How to Confirm Correct Configuration
- Upload a simulated medical record file containing HAM-D scores, PANSS scores, and genetic test results. Verify that the knowledge base accurately extracts and displays this structured data.
- Attempt to log in to the system with a preset test account. Verify that it has the expected knowledge base access permissions and pre-screening function operation permissions.
- Perform a pre-screening query with a complex diagnostic description. Check if the returned results include reference information from multiple data sources (e.g., medical record text, scale data). Also, check if the relevance scores are reasonable.
- Review system logs for a large number of
ParserErrororEmbeddingErrorwarnings. Confirm that the data parsing and vectorization processes are free of anomalies.
The values given are common starting points and should be measured 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.