Data Characteristics
Cardiovascular intervention data primarily originates from Electronic Health Record (EHR) systems, imaging reports, surgical records, adverse drug event reports, and clinical trial protocols. This data updates frequently. For hospitalized patients, new physiological indicators and treatment records generate daily or even hourly.
Document structures vary. EHRs are often semi-structured text, containing extensive free-text descriptions like chief complaints, history of present illness, physical examinations, and auxiliary test results. Imaging reports (e.g., ECG, echocardiogram, coronary angiography) typically follow fixed templates, but diagnostic conclusions remain free text. Clinical trial protocols are usually lengthy PDF or Word documents with strict structures, including research background, inclusion/exclusion criteria, methodology, and statistical analysis plans. Fields and units are standardized, covering blood pressure (mmHg), heart rate (beats/min), blood oxygen saturation (%), and troponin (ng/mL).
Constraints Imposed on Document Parsing and Chunking
The semi-structured nature of cardiovascular intervention data challenges document parsing. Free-text paragraphs require more refined splitting strategies to avoid semantic loss or over-generalization. High update frequency necessitates knowledge bases capable of rapid incremental updates and local index reconstruction to ensure timely retrieval results.
Lengthy clinical trial protocols emphasize understanding overall document structure and hierarchy. Chunking must balance chapter integrity and content coherence. For example, inclusion/exclusion criteria often scatter across multiple sub-sections; related conditions must aggregate into a single knowledge chunk. Values and conclusions in imaging reports often appear as "value + unit" or "descriptive phrase." Accurate identification and extraction during parsing are crucial for subsequent Q&A and inference. Accurate recognition of specific medical terminology and abbreviations directly impacts chunking quality and information retrieval precision.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 400–600 characters | Balances semantic integrity and recall efficiency, avoiding overly long or short chunks. |
Chunk Overlap Length (Chunk Overlap Length) | 50–100 characters | Ensures contextual continuity and reduces information fragmentation due to chunking. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large clinical trial protocol PDF documents. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports document sizes containing large amounts of imaging or medical record data. |
File Type Whitelist | pdf, docx, txt, csv | Covers common formats for clinical trial documents, medical records, and data reports. |
Custom Separator (Custom Separators) | Calibrate based on actual data, e.g., \n\n, ###, --- | Enables precise splitting according to actual document structure, especially for semi-structured medical records. |
Common Pitfalls
- Symptom: Uploaded clinical trial protocol PDF files time out or fail during parsing. Reason:
PARSE_FILE_TIMEOUT_SECONDSis set too short, unable to handle the parsing time required for very large files or complex layouts. - Symptom: Multiple rows of patient records imported from Excel merge into a single knowledge chunk. Reason:
Custom Separator(Custom Separators) is not correctly configured or enabled. The system applies default chunking rules, ignoring row delimiters. - Symptom: When users ask about "inclusion/exclusion criteria," recall results are incomplete or have low relevance. Reason:
Chunk size(Chunk Length) is set too small, splitting semantically related inclusion/exclusion conditions, which are scattered across different document paragraphs, into separate knowledge chunks.
Verification Steps
- Upload a typical cardiovascular intervention clinical trial PDF document. Check its parsing status to ensure no timeout or parsing error messages.
- Import a structured patient medical record CSV file. Randomly inspect multiple knowledge chunks to verify that each patient record or critical information forms an independent chunk.
- Conduct simulated queries using core cardiovascular intervention terms, such as "coronary stenosis," "myocardial infarction," and "stent implantation." Observe the completeness and relevance of recall results to determine if they effectively link to knowledge chunks containing these terms.
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.