Data Characteristics
Ophthalmic clinical trial pre-screening data originates from several sources. Clinical trial protocol documents, typically in PDF or Word format, detail trial design, inclusion/exclusion criteria, and study procedures. Patient Electronic Health Record (EHR) data includes diagnoses, medications, and test results, existing in both structured and unstructured forms. Medical imaging data, such as fundus photography and OCT (Optical Coherence Tomography), are usually in DICOM or JPEG format with associated metadata. Additionally, research papers from medical literature databases, mostly in PDF format, are used. Data update frequencies vary: clinical trial protocols are relatively stable, EHR data updates in real-time, and literature data updates according to journal publication cycles. Fields and units are highly specialized, for example, LogMAR values for visual acuity, mmHg for intraocular pressure, and dB for visual field defects. These specialized terms and units require precise identification during data processing.
Constraints on Reference and Traceability
The diversity of ophthalmic data imposes specific requirements on reference and traceability. The fixed nature of clinical trial protocols means their referenced content must be highly stable and not subject to arbitrary changes. The real-time and unstructured characteristics of EHR data require the system to process high-frequency text updates, accurately extract key information from free text, and trace references to specific patient records and timestamps. Medical image metadata is crucial for traceability, requiring the system to link text references with corresponding image reports. Specialized fields and units, such as intraocular pressure (mmHg) or visual acuity (LogMAR), require the knowledge base to precisely identify and differentiate these terms, preventing confusion or misinterpretation during referencing. Furthermore, many ophthalmic literature and clinical guideline references need to be precise down to the page number or paragraph to ensure information reliability and verifiability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Clinical trial protocols and medical record paragraphs have moderate length, balancing semantic completeness and recall accuracy. |
Recall Count | Top 8 | Needs to cover multiple relevant inclusion/exclusion criteria and patient characteristics to ensure comprehensive pre-screening. |
Similarity Threshold | 0.78–0.85 | Ophthalmic terminology requires high precision to avoid incorrect matches due to semantic drift. |
Rerank Return Count | 3 | Reduces interference from irrelevant information, focusing on the most relevant reference snippets for display. |
Parse Timeout | 600 seconds | For processing large PDF clinical trial protocols and complex documents containing image reports. |
Vector Model | text-embedding-ada-002 | Provides good semantic understanding of ophthalmic professional terms and medical text. |
Common Pitfalls
AI_RESPONSE_EMPTYerrors or missing references in source citations indicate an incorrectly configured PDF parser, preventing effective reading of complex clinical trial protocol documents.- Quoted ophthalmic numerical values, such as visual acuity, in AI responses do not match original medical records. This occurs when the vector model lacks sufficient accuracy in recognizing specific units (e.g.,
LogMAR), or when chunking separates values from their units. OutOfMemoryErrorerrors occur when processing large volumes of patient medical record data. This is due to not setting a reasonable limit for theUPLOAD_FILE_MAX_SIZEfor single file processing, leading to memory overflow.
Verification Steps
- Upload a clinical trial protocol PDF containing various ophthalmic professional terms and numerical values. Check the chunking results to ensure all key information is correctly extracted and segmented.
- Select multiple test cases with clear inclusion or exclusion criteria. Input them into the system for pre-screening. Verify the system's cited source documents, page numbers, or medical record IDs to ensure a complete traceability chain.
- Simulate a high-concurrency query request. Observe system response times and resource utilization. Confirm that results with reference sources are returned within the specified time and without timeout or memory errors.
Note: The values provided are common starting points. Measure against your own samples to find optimal settings.
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.