Knowledge Base Retrieval and Recall for Clinical Trial

Data Characteristics in this Domain

In clinical trial pre-screening for pharmaceutical e-commerce platforms, data primarily comes from pharmaceutical companies' clinical trial recruitment information, user-filled health questionnaires, electronic medical record summaries, and drug package inserts. This data updates frequently, especially with new drug development progress and clinical trial protocol adjustments. Recruitment information typically consists of unstructured text, detailing trial objectives, inclusion/exclusion criteria, and treatment regimens. User health data is often semi-structured, like JSON or XML, with fields such as age, gender, medical history, and medication history. Drug package inserts are standardized documents, containing drug ingredients, indications, and contraindications. The data frequently involves numerous specialized terms and diverse units, such as dosage units (mg/kg), time units (weeks, months, years), and various medical test indicator units.

Constraints Imposed on Knowledge Base Retrieval and Recall by these Characteristics

The characteristics of pharmaceutical e-commerce data impose specific requirements on knowledge base retrieval and recall. The vast amount of unstructured clinical trial recruitment text necessitates efficient text segmentation strategies. This ensures that critical inclusion/exclusion criteria are not split, which would lead to semantic loss. The semi-structured nature of user health data requires the knowledge base to integrate structured information with unstructured text retrieval. For example, it must match text descriptions of diseases and precisely filter users within specific age ranges. High data update frequency means the knowledge base needs to support rapid incremental updates and version management to avoid retrieving outdated information. The specialized nature of medical terminology and diverse units requires the knowledge base vector model to have robust semantic understanding. It must recognize synonyms and near-synonyms, and handle unit conversions, ensuring that "hypertension" and "elevated blood pressure" are effectively linked, or that "200mg" and "0.2g" are treated as equivalent.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Size`	800–1200 characters	Balances completeness of clinical trial recruitment text paragraphs with vectorization efficiency.
`Overlap Size`	100 characters	Ensures contextual continuity, reduces risk of critical information being cut off.
`Recall Count`	Top 5–8 items	Balances retrieval efficiency with comprehensive recall, covering main matches.
`Similarity Threshold`	Calibrate by measurement	Based on expert evaluation, ensures highly relevant results are recalled.
`Rerank Return Count`	3 items	Prioritizes displaying the most relevant clinical trial information, reducing user cognitive load.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Handles large clinical trial protocols or package insert files, preventing parsing timeouts.

Common Pitfalls

Retrieval results contain many irrelevant clinical trials: This occurs when the Similarity Threshold is set too low, leading to excessive generalized recall.
Some eligible users are not recommended any trials: This occurs when Chunk Size is too short or Overlap Size is insufficient, causing critical inclusion/exclusion criteria to be truncated and not effectively matched.
Retrieval results still display old information after knowledge base content updates: This occurs when the knowledge base index is not rebuilt in time or the incremental update mechanism is not active.

How to Verify Configuration

Select a batch of user profiles known to meet specific clinical trial inclusion/exclusion criteria. Simulate queries and verify if the recall results include the relevant trial.
For core diseases or drugs, test different phrasing in query statements. This verifies the Similarity Threshold and the vector model's semantic understanding.
Upload a document containing the latest clinical trial information and immediately perform a retrieval. Confirm that the new content is quickly indexed and recalled.

The values provided are common starting points. Measure performance against your own samples to determine 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.