Knowledge Base Retrieval and Recall for Ophthalmic Clinical

Data Characteristics in This Domain

Ophthalmic clinical trial pre-screening data originates from diverse sources. These include medical literature databases (e.g., PubMed, Medline), clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), de-identified patient data from Electronic Health Record (EHR) systems, and internal documents from pharmaceutical companies and research institutions such as trial protocols, Investigator's Brochures (IBs), and Informed Consent Forms (ICFs). Data update frequencies vary; literature and registry information may update weekly or monthly, while EHR data generates in real-time. Document structures differ: literature often consists of unstructured text, while trial protocols and IBs are typically structured or semi-structured PDF documents with clear section headings, tables, and figures. Field-specific parameters and units are crucial in ophthalmology, including visual acuity (e.g., LogMAR, Snellen), intraocular pressure (mmHg), retinal thickness (µm), and visual field defect severity (dB). These metrics are essential for describing disease states, inclusion/exclusion criteria, and efficacy evaluation, often associated with specific measurement methods and device models.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The highly specialized and diverse nature of ophthalmic data places specific demands on knowledge base retrieval and recall. Unstructured text containing specialized terms and abbreviations, such as "AMD" (Age-related Macular Degeneration) or "IOP" (Intraocular Pressure), requires robust semantic understanding. Information embedded in tables and images within structured documents can lose context during text segmentation, impacting recall accuracy. For example, an inclusion/exclusion criterion might be defined within a table, and extracting only the text would not fully convey its meaning. Furthermore, ophthalmic metrics have widely varying units and normal ranges. If the knowledge base fails to correctly parse and associate these values, it could lead to misjudgments during pre-screening regarding patient eligibility. Inconsistent data update frequencies mean the knowledge base needs to support incremental updates and version management to ensure retrieved information is current, preventing decisions based on outdated criteria.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Ophthalmic clinical document sections are relatively independent. This length helps preserve the complete semantic meaning of a paragraph, preventing critical information from being truncated.
`Chunk Overlap Length` (Overlap Length)	`100–150 characters`	Ensures sufficient contextual overlap between adjacent paragraphs, especially when processing inclusion/exclusion criteria that span across paragraphs.
`Recall count` (Recall Count)	`8–12 items`	Given the complexity of ophthalmic trial protocols, increasing the recall count appropriately improves coverage and reduces missed information.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	While ensuring the relevance of recalled items, a slightly more lenient threshold can capture more potentially matching clinical terms.
`Rerank result count` (Reranked Return Count)	`4–6 items`	After optimization by a reranking model, this number is sufficient to present the most relevant core information, avoiding information overload.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient parsing time for large PDFs or documents with complex structures, preventing some content from not being indexed due to timeouts.

Three Common Pitfalls

Knowledge base retrieval results return only citations without specific answers. This occurs when the maxContext parameter in the model settings is too small, preventing the model from integrating recalled knowledge and generating a complete response.
When classifying questions across multiple knowledge bases, questions are incorrectly routed to a general fallback category. This happens when the classification model fails to accurately recognize ophthalmic-specific terminology and context, due to insufficient training data for the classification model or inadequately designed category labels.
The accuracy of knowledge base answers from uploaded .docx or .excel documents is low, manifesting as missing information or comprehension errors. The issue lies with the document parser's incomplete extraction of tables, figures, or specifically formatted text, leading to reduced quality of knowledge embeddings.

How to Confirm Proper Configuration

Select a batch of test questions covering typical ophthalmic diseases (e.g., glaucoma, cataracts) and trial phases (e.g., Phase I, Phase II). Observe whether the knowledge base's recall results include all relevant inclusion/exclusion criteria and key metrics, and verify the accuracy of citation sources.
Pose questions involving ophthalmic professional terms, abbreviations, and specific measurement units (e.g., LogMAR 0.3, IOP > 21 mmHg). Confirm the system can correctly understand and link these to corresponding numerical ranges and descriptions in the knowledge base, validating its semantic understanding capabilities.
Upload an ophthalmic clinical trial protocol PDF containing complex tables and figures. Ask questions about key data points within it. Check if the knowledge base can extract accurate data from the tables and provide answers, evaluating the effectiveness of document parsing and information extraction.

Note: The values provided are common starting points. Measure them against your own samples to determine the most suitable configuration.

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.