Knowledge Base Retrieval and Recall for Phase I Clinical

Data Characteristics

Phase I clinical trial pre-screening data primarily originates from sponsor-provided study protocols, investigator brochures, informed consent forms, ethics committee approvals, subject screening logs, and laboratory examination reports. These documents update infrequently, typically when the study protocol revises, safety data updates, or regulatory agencies require changes. Documents are mostly unstructured text, such as PDF study protocols. They contain extensive medical terminology, abbreviations, and tabular data. Fields and units are highly specialized, for example, dosage units like mg/kg, time units like h and d, and normal ranges for various clinical indicators. Additionally, subject medical history and concomitant medication information exist as free text, requiring refined processing.

Constraints on Knowledge Base Retrieval and Recall

Low update frequency for Phase I clinical data means knowledge base re-indexing frequency can be lower. However, each re-indexing demands extremely high accuracy. Documents are mostly unstructured text with extensive specialized terminology and abbreviations. This challenges tokenization and entity recognition, requiring more specialized dictionary support. The specialized nature of medical fields and units requires the retrieval system to understand and differentiate values with different units, avoiding confusion. For example, for dosage queries, 10 mg and 10 µg are distinctly different. Free text in subject medical history requires stronger semantic understanding to accurately match disease names, symptom descriptions, and drug mechanisms of action. These factors directly influence knowledge base chunking strategies and recall algorithm selection.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`800–1200 characters`	Ensures individual chunks contain sufficient contextual information. Avoids excessive length, which leads to information redundancy and reduced recall efficiency, especially for paragraphs in study protocols.
`Chunk Overlap Length`	`100–200 characters`	Guarantees contextual continuity between chunks. Effectively handles medical terminology and logical relationships spanning multiple chunks.
`Recall Count`	`Top 8–12 entries`	Phase I clinical pre-screening demands high accuracy. Appropriately increasing the recall count improves candidate set coverage and reduces the risk of missed recalls.
`Similarity Threshold`	Calibrate by actual measurement	Requires testing with a small amount of labeled data. Balances recall rate and precision, ensuring the rigor of screening condition matching.
`Rerank Return Count`	`Top 3–5 entries`	After reranking, focuses on a small number of the most relevant entries. Facilitates quick review and decision-making by engineers.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Considers that documents like study protocols may contain numerous charts and detailed information. The file size limit must accommodate such files.

Common Pitfalls

Retrieval results contain numerous irrelevant medical terms or abbreviations, making it difficult for users to identify valid information. This occurs when knowledge base chunking does not adequately consider the completeness of medical vocabulary or when a specialized dictionary is not configured for tokenization.
After uploading large PDF documents, some content is not retrievable. This happens when the document parser has insufficient capability to process complex layouts or scanned documents, leading to incomplete text extraction or formatting errors, which then affects knowledge base indexing.
When querying specific dosages or time points, the system returns inaccurate values or confused units. This occurs when the knowledge base does not standardize units or establish relationships between numerical values and units when processing numerical data.

Validation Steps

Select test cases containing key screening criteria (e.g., subject age, specific disease diagnosis, concomitant medication contraindications). Query and check if recall results accurately include this information. Verify the contextual completeness of returned entries.
Upload a Phase I clinical study protocol PDF file containing complex tables and figure captions. Confirm all text content is correctly parsed and indexed, especially key data fields within tables.
Query specific medical terms or abbreviations. Check if recall results accurately identify their meaning and link to relevant definitions or explanations.
Use queries containing specific dosages, frequencies, or time windows. Verify if the system can accurately match and differentiate values with different units. For example, check if 5 mg and 5000 µg are correctly distinguished.

The values provided are common starting points. Measure them 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.