Data Characteristics
Infectious disease registration application data comes from various sources. These sources include clinical trial reports, pharmacokinetic studies, pharmacodynamic studies, toxicology reports, manufacturing process documents, quality standards, stability studies, literature reviews, and epidemiological data. Data update frequencies vary. Clinical trial data typically updates centrally after study completion, while epidemiological data may update quarterly or annually. Document structures often follow international formats, such as ICH M4Q/M4S/M4E guidelines, which usually include clear chapters and sub-chapters. Fields and units are highly specialized. Examples include "plasma concentration (ng/mL)" and "half-life (h)" in pharmacokinetic curves, "minimum inhibitory concentration (MIC, μg/mL)" in antimicrobial susceptibility testing, and "cure rate (%)" and "adverse event incidence (%)" in clinical efficacy evaluations. The data frequently contains numerous charts, statistical analysis results, and specialized terminology.
Constraints on Workflow Orchestration
The complexity and diversity of infectious disease registration application data impose specific requirements on workflow orchestration. Dispersed data sources and varying update frequencies mean workflows need flexible data access and synchronization mechanisms to integrate the latest information from different systems. Highly standardized document structures require parsing nodes in the workflow to accurately identify and extract specific chapters and fields, such as "primary endpoint indicators" or "safety data" from clinical trial reports. The presence of specialized fields and units necessitates precise entity recognition and unit normalization during data processing and knowledge base construction to avoid misinterpretations due to inconsistent units. Furthermore, the abundance of charts and statistical analysis results requires the workflow to process non-textual information and convert chart data into structured text descriptions understandable by AI models. AI nodes in the workflow must strictly adhere to medical terminology standards and the rigor of application documents when generating content, avoiding ambiguity.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Infectious disease data has strong contextual relevance; a longer text capacity ensures information completeness. |
Chunk size | 500 characters | Balances semantic integrity with recall efficiency, avoiding noise or loss of critical information from overly long paragraphs. |
Recall count | Top 10 entries | Ensures coverage of multi-dimensional, multi-chapter relevant information from registration applications, improving accuracy. |
Similarity threshold | 0.75 | Strictly controls relevance, reducing the impact of irrelevant or low-relevance documents on results, ensuring medical rigor. |
maxTokens (AI Node) | 2000 tokens | Allows AI to generate longer professional responses, meeting the need for detailed explanations and justifications in application documents. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large clinical trial reports or complex literature, preventing timeout interruptions. |
Common Pitfalls
- AI node output lacks specialized terminology or contains factual errors. This occurs when system prompts insufficiently emphasize professionalism or the knowledge base lacks comprehensive medical coverage.
- Workflow execution times out or file parsing fails. Logs show
TimeoutErrororParserError. This usually happens when uploaded documents are too large, have complex formats, or contain many images, leading to extended parsing times. - Key data fields (e.g., dosage, batch number) are missing or inaccurate in AI-generated results. This occurs when document parsing fails to precisely identify and extract structured information, or context is lost during multi-turn conversations.
How to Verify Configuration
- Select typical infectious disease registration application documents. Run them through the workflow. Check if the key content generated by the AI is accurate and compare it with the original documents.
- Test the workflow's parsing capability for different formats (PDF, Word, Excel) and sizes of application documents. Confirm no timeouts or parsing failures, and check the completeness of the parsed text.
- Design test cases with specialized terminology and data queries. Validate the AI node's response quality and professionalism for complex medical questions, ensuring compliance with the rigor required for application documents.
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.