Model Integration and Configuration for Hematologic Oncology

Data Characteristics in this Category

Hematologic oncology registration documents draw from diverse data sources. These primarily include clinical trial reports (ICH GCP standards), case report forms (CRF), investigator brochures (IB), medical literature, pharmacology and toxicology reports, manufacturing process documents (CMC), and regulatory guidelines from various agencies. Data update frequency varies: clinical trial data is generated in real-time during trials, with interim reports updated quarterly or annually. Regulatory documents are revised irregularly based on policy changes. Document structures are typically PDF, Word, and Excel, containing numerous tables, figures, and specialized terminology. Fields and units are highly standardized; for example, dosages are often in mg/kg or mg, time points in days, weeks, or months, and laboratory indicators like blood counts and biochemical parameters have defined unit ranges.

Constraints Imposed by these Characteristics on Model Integration and Configuration

The data characteristics of hematologic oncology registration documents impose specific requirements on model integration and configuration. First, data diversity and complexity, especially the presence of numerous tables and figures, demand models with strong multimodal processing capabilities and precise text extraction to ensure information completeness. Second, the specialized nature of clinical trial reports and regulatory documents means general models may misinterpret specific terminology and context. This necessitates enhancement through specialized knowledge bases or fine-tuning of domain-specific models to improve accuracy. The non-periodic nature of data updates, particularly regulatory document revisions, requires the knowledge base to support flexible and efficient incremental update mechanisms to prevent models from generating content based on outdated information. Finally, the high standardization of fields and units requires models to strictly adhere to predefined rules during information extraction and validation, such as accurate identification and conversion of dosage units, which directly impacts compliance of the submission documents.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500–800 characters`	Balances context completeness in long documents with information density per segment, reducing model inference costs.
`Recall count`	`Top 8 entries`	Ensures coverage of key related information in clinical trial data, improving recall comprehensiveness.
`Similarity threshold`	`0.75–0.85`	Filters out general content unrelated to hematologic oncology, enhancing retrieval precision.
`Rerank result count`	`Top 5 entries`	Prioritizes core content most relevant to the submission topic, reducing interference from redundant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses the time-consuming parsing of large clinical trial report PDFs, preventing file processing failures due to timeouts.
`modelId`	`gpt-4o` or `claude-3-opus-20240229`	Selects models optimized for understanding complex medical text and multimodal content, improving generation quality.

Common Pitfalls

Model responses consistently include a general disclaimer at the end, such as "FastGPT is a knowledge base Q&A system based on large language models (LLM)." This typically occurs because the system_prompt parameter in the model configuration was not cleared or modified.
When processing specific tabular data, model output fields are empty or incorrectly formatted. This often results from insufficient support for complex table structures by the file parser, or the Chunk size setting being too large, leading to overly sparse information within a single text block.
After updating regulatory documents, the model continues to generate responses based on old regulations. This usually indicates that the knowledge base update mechanism was not triggered in time, or the indexing strategy was not set for incremental updates.

How to Confirm Proper Configuration

Upload a clinical trial report containing complex tables and figures. Check if the model accurately extracts key dosages, time points, and laboratory indicators. Compare with the original document to verify information completeness.
For a specific hematologic oncology indication, ask questions about the registration process or required documents. Observe if the model's reply accurately cites clauses from the latest regulatory documents. Confirm the source by querying the knowledge base log.
Simulate a multi-turn dialogue scenario involving medical terminology and data unit conversions. Check if the model handles context understanding and specialized terminology smoothly and accurately. Verify that token consumption is within the expected range.
Call the GET /api/v1/app/getDatasetFiles API to check file parsing status and segment count, ensuring all documents have been successfully parsed and indexed.

The values provided 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.