Data Characteristics for this Category
Cardiovascular disease regulatory submission data comes from diverse sources. These include clinical trial reports, non-clinical study reports, pharmacovigilance data, post-market study reports, guidelines, and consensus documents. Data update frequencies vary. Clinical trial data typically releases in batches after study completion. Pharmacovigilance data may update in real-time. Document structures are primarily structured and semi-structured, such as CRF tables and Statistical Analysis Plans (SAP) from clinical trials. Unstructured text also exists, including investigator brochures and medical literature reviews. Fields and units are highly specialized. Examples include QRS complex width (unit: milliseconds) in electrocardiogram (ECG) data, systolic/diastolic blood pressure (unit: mmHg), and heart rate (HR) (unit: beats/minute). Additionally, extensive biomarker data exists, such as cardiac troponin I (cTnI) (unit: ng/mL), where normal ranges and clinical significance require strict differentiation.
Constraints from these Characteristics on Citation and Traceability
The complex data characteristics of cardiovascular submission documents impose multiple constraints on citation and traceability. First, multi-source heterogeneous data requires robust heterogeneous data processing capabilities. This ensures correct parsing and indexing of all document types. Second, specialized fields and units necessitate highly precise semantic matching during RAG retrieval. This prevents false recalls due to unit or abbreviation differences. High-frequency updates in pharmacovigilance data and post-market studies require rapid knowledge base synchronization of the latest information. It also requires ensuring the traceability of older data versions. Finally, critical numerical values in reports, such as clinical endpoints and adverse event rates, demand extremely high accuracy. Any citation deviation could lead to severe compliance risks. Therefore, the traceability mechanism must precisely point to specific paragraphs, tables, or even exact numerical values within original documents. This supports rigorous auditing and verification.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Cardiovascular report paragraphs are often long, describing multiple metrics. Increasing length ensures contextual completeness. |
Recall count (Recall Count) | Top 5 | Ensures coverage of multiple potentially relevant sources, balancing retrieval efficiency and result comprehensiveness. |
Similarity threshold (Similarity Threshold) | 0.78 | Clinical data requires high accuracy. A higher threshold excludes ambiguous or irrelevant content. |
Rerank result count (Rerank Return Count) | 3 entries | Filters for the most relevant few entries, facilitating quick manual review. |
Citation Link Format | File Name_Page Number | Clearly points to the specific page number in the original PDF or Word document, allowing for quick location and verification. |
Max File Size | 500 MB | Cardiovascular clinical trial report files are often large and require support for large file uploads. |
Three Common Mistakes
- Reply content contains incorrect numerical values related to cardiovascular drug dosage or indications. This occurs because the knowledge base contains outdated or incorrect clinical guidelines that were not updated promptly.
- Citation links point to PDF files that cannot be opened or have incorrect page numbers. This may be due to
OCR recognition errorsduring file parsing, leading to abnormal link generation, or changes in the original file path. - The system fails to cite the latest clinical research reports when answering questions about the newest treatment plans for specific cardiovascular diseases. This happens because the
knowledge base update frequencyconfiguration is insufficient to cover rapidly published new literature.
How to Confirm Correct Configuration
- Randomly select 10-15 complex questions related to cardiovascular regulatory submissions. Check if the file names and page numbers cited in FastGPT's responses match the content of the original documents.
- Upload and query different types of documents in the knowledge base (e.g., PDF, Word, Excel). Verify if the
citation link formataccurately navigates to the corresponding location in the original materials. - Simulate questions about detailed data from a specific cardiovascular clinical trial. Cross-reference the key numerical values returned by the system with the
data fieldsin the original report for exact matches. - Query recent authoritative guidelines or consensus documents in the cardiovascular field. Verify if the knowledge base has included them and can correctly cite them when questioned.
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.