Knowledge Base Retrieval and Recall for Clinical Trial

Data Characteristics

Data for medical record quality control in clinical trial pre-screening primarily originates from Electronic Health Record (EHR) systems, imaging reports, laboratory reports, and research medical record forms. This data updates frequently, often in real-time or near real-time, with patient visits and examination results. Document structures are typically semi-structured or unstructured text, containing extensive medical terminology, abbreviations, and free-text descriptions. Imaging and laboratory reports are mostly structured or semi-structured, including numerical values, units, and diagnostic conclusions. Field content is complex, covering diagnosis codes (e.g., ICD-10), drug names, dosages, frequencies, examination items, result values and their units (e.g., mg/dL, mmol/L, U/L), and vital sign data.

Constraints on Knowledge Base Retrieval and Recall

The high update frequency of medical record data requires an efficient incremental update mechanism for the knowledge base to ensure real-time retrieval. The semi-structured and unstructured nature of the text means simple keyword matching cannot accurately capture complex medical concepts and clinical logic, necessitating advanced semantic understanding capabilities. Extensive medical terminology and abbreviations challenge tokenization and entity recognition, potentially leading to incomplete or incorrect recall. The combination of numerical values and units, such as "blood glucose 5.6 mmol/L," requires the knowledge base to identify and correctly process numerical ranges and unit conversions to support filtering based on numerical conditions. Furthermore, the heterogeneity of different document types (medical records, reports) means a single recall strategy may not cover all data sources, requiring support for unified management and retrieval of multi-source heterogeneous data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`300–500 characters`	Balances the completeness of medical text context with the efficiency of embedding vectors.
`Chunk Overlap Length`	`50–80 characters`	Ensures medical terms and logic are not fragmented across segments.
`Recall count`	`10–20 entries`	Increases initial recall quantity to improve coverage of potentially relevant information.
`Similarity threshold`	`0.75–0.85`	High accuracy is required in the medical domain; avoids low-relevance results.
`Rerank result count`	`5–8 entries`	Selects the most relevant results after re-ranking to improve final presentation quality.
`Maximum Concurrent File Processing`	`Based on server resources`	Large volume of medical records requires system stability during high-concurrency processing.

Common Pitfalls

Newly entered medical record data is not retrievable because the knowledge base's incremental indexing mechanism failed to trigger or index construction failed.
Retrieval results contain many irrelevant medical record fragments, indicating that the Similarity threshold (similarity threshold) is set too low, leading to the recall of semantically distant content.
Queries for specific laboratory indicators, such as "high white blood cell count," fail to accurately match patients within the specific numerical range. This occurs because the knowledge base does not correctly extract and compare numerical values when processing structured information like numbers and units.

Verification of Configuration

Upload a batch of test medical records containing various medical terms, abbreviations, and numerical units. Verify that the knowledge base correctly segments, embeds, and indexes them by checking the segment preview in the knowledge base details.
Formulate multiple query statements based on predefined clinical trial inclusion/exclusion criteria. Observe whether the retrieved medical record fragments are accurate and complete. Evaluate the reasonableness of the Recall count (recall count) and Rerank result count (re-ranked return count) settings.
Use queries with clear numerical range conditions, such as "fasting blood glucose greater than 7.0 mmol/L." Check if the returned results precisely match medical records that meet the numerical conditions by comparing with the original medical record data and recall results.
Monitor the knowledge base's update logs to ensure that newly uploaded or modified medical record data is indexed promptly at the expected frequency, without PARSE_FILE_TIMEOUT_SECONDS or other index construction failure errors.

The values provided are common starting points and should be measured against the reader's own 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.