Data Characteristics for this Category
Psychiatric disorder registration and declaration documents draw from diverse data sources. These include clinical trial reports, pharmacology and toxicology study data, manufacturing process information, quality standards, stability study data, and non-clinical pharmacokinetic data. This data often exists in multiple formats such as PDF documents, Word documents, Excel spreadsheets, images, and structured database records. Update frequency varies: clinical trial data updates dynamically with study progress, pharmaceutical research data may undergo frequent revisions during development, and regulatory documents change based on agency publication cycles. Document structure typically adheres to ICH-CTD (Common Technical Document) or National Medical Products Administration (NMPA) submission requirements, featuring strict hierarchical structures and content specifications. Field and unit specificities include precise recording of psychiatric scale scores (e.g., HAM-D, PANSS, CGI), rigorous drug dosage units (mg, μg/kg), blood concentration units (ng/mL), and standardized descriptions of adverse event severity and frequency.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The diversity and complex structure of psychiatric disorder declaration data place high demands on workflow orchestration. Multi-format documents mean the workflow must support parsing and content extraction from various file types. For example, accurately extracting psychiatric scale scores from clinical trial report PDFs or dosage data from Excel spreadsheets. The dynamic nature of data updates requires the workflow to have version management capabilities, ensuring it always processes the latest data and can trace historical versions. Strict document structures constrain information extraction and integration logic. The workflow needs to aggregate dispersed information into designated locations according to the hierarchical relationships of the declaration template, such as automatically populating conclusions from specific studies into CTD Module 2.5 Non-clinical Overview. Field and unit specificities require the workflow to perform strict validation and standardization during data processing, ensuring consistency of psychiatric scale scores, dosage, and concentration units to avoid data errors due to unit confusion. Furthermore, standardized descriptions of adverse events prompt the workflow to perform entity recognition and classification after information extraction, providing accurate data for subsequent safety assessments.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 | Accommodates lengthy descriptive text in psychiatric clinical reports, ensuring completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large PDF files or OCR recognition of scanned documents. |
Chunk size | 800–1200 characters | Balances semantic completeness of long sentences with model processing efficiency, adapting to complex medical terminology. |
Recall count | Top 10–15 entries | Ensures coverage of multi-dimensional, multi-chapter related information in psychiatric drug declaration documents. |
Similarity threshold | 0.75–0.85 | Balances recall precision and generalization ability, identifying highly relevant medical concepts and regulatory terms. |
Rerank result count | Top 5 entries | Refines the final results, focusing on core information strongly related to psychiatric disorder declarations. |
Three Common Pitfalls
- Workflow execution times out, with logs showing
Task execution timed out after X seconds. Cause: Insufficient consideration for parsing time of large clinical reports;PARSE_FILE_TIMEOUT_SECONDSparameter set too low. - Certain key fields in the generated results are empty or incorrect, for example, psychiatric scale scores are not extracted. Cause: The file parser has insufficient recognition capability for text within specific table formats or images, or the regular expressions in the workflow do not precisely match the target fields.
- Users report a
Key is error. You need to use the app key rather than the account keymessage when calling the workflow via a non-login window. Cause: A non-application level API Key was used when calling the workflow, resulting in insufficient permissions or a mismatched key type.
How to Verify Configuration
- Select a test dataset covering various file formats (PDF, DOCX, XLSX) and typical declaration modules (e.g., non-clinical overview, clinical summary). Run the workflow and cross-reference key information in the output results with the original data.
- For core data fields such as psychiatric scale scores and drug dosages, check the accuracy of their extraction and unit standardization. Compare with manually verified results to determine the error tolerance.
- Simulate concurrent invocation scenarios to observe the workflow's response time and resource consumption. Ensure stable service provision in actual use and adjust the concurrency limit based on business needs.
Note: 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.