Data Characteristics in this Domain
Dermatology clinical trial pre-screening involves diverse data types. These primarily include electronic health records (EHRs), imaging data (e.g., dermoscopy images, histopathology slides), genomic data, and patient history of medication and allergies. This data often resides in various medical information systems and databases. EHR data typically uses HL7 or FHIR formats, with frequent updates (daily or weekly). Imaging data is often in DICOM or JPEG format and has large file sizes. Genomic data usually comes in VCF or FASTQ formats, with less frequent updates (one-time or on-demand generation). Structurally, EHR data contains extensive unstructured text descriptions, such as physician-written diagnostic notes and treatment plans.
Constraints Imposed by these Characteristics on "HTTP Interface and External Systems"
The diversity and distributed nature of dermatology clinical trial pre-screening data impose specific requirements on HTTP interface design and external system integration. The prevalence of unstructured text necessitates advanced natural language processing (NLP) during data ingestion to extract key information like disease diagnoses, treatment outcomes, and adverse reactions. Imaging data transfer requires interfaces that support large file uploads and downloads, ensuring stable and complete transmission to prevent data corruption from network fluctuations. Genomic data processing demands interfaces capable of handling complex bioinformatics formats. The heterogeneous nature of data sources also requires multiple independent HTTP endpoints or a flexible adaptation layer to connect with external systems using different protocols and data structures, such as hospital HIS/LIS systems, Picture Archiving and Communication Systems (PACS), and gene sequencing platforms.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Accommodates lengthy text descriptions common in dermatology medical records |
Chunk size (Segment Length) | 500 characters | Balances semantic completeness with model processing efficiency |
Recall count (Recall Count) | 20 items | Ensures coverage of sufficient clinically relevant information |
Similarity threshold (Similarity Threshold) | 0.75 | Filters document segments highly relevant to pre-screening conditions |
Rerank result count (Reranked Return Count) | 5 items | Focuses on the most relevant information, reducing downstream model burden |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for large imaging reports and genomic data files |
Common Pitfalls
- Calling an external imaging service returns
HTTP 504 Gateway Timeout: This typically occurs when transferring large DICOM image files, leading to an API gateway or backend processing service timeout. - RAG results lack critical medical history information: This happens when symptom descriptions in unstructured medical record text are not effectively extracted and indexed, causing omissions during the recall phase.
- The patient allergy history field returned by the HTTP interface is empty or incomplete: This may be due to a mismatch between the external system's data format and FastGPT's expected field structure, or incorrect mapping during the data cleaning phase.
Verification Steps
- Using FastGPT's debugging interface, upload a simulated medical record containing typical dermatological diagnoses, treatment plans, and imaging links. Check if text extraction and RAG recall results accurately include all key information.
- Perform an end-to-end pre-screening process for a real patient's complete data, including EHR text and imaging reports. Verify that all external system interface calls are successful and data transfer is accurate.
- Import various formats of dermatology-related documents (e.g., clinical guidelines, drug instructions) into the FastGPT knowledge base. Conduct query tests to ensure proper parsing and retrieval functionality for different document types.
The values provided are common starting points and should be measured against specific 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.