Data Characteristics for This Category
Monoclonal antibody (mAb) clinical trial data, as biological macromolecule drugs, exhibit both highly structured and semi-structured characteristics. Data sources primarily include clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), internal databases of biopharmaceutical companies, medical literature (PubMed, Scopus), and patent databases. Data update frequencies vary; registries may update weekly or monthly, while literature and patents are continuously published. Document formats are diverse, including protocols, investigator brochures, clinical study reports (CSRs), adverse event (AE) reports, and statistical analysis plans (SAPs).
Key fields include Target, Indication, Mechanism of Action (MoA), Route of Administration, Dosage, Frequency, Phase, Inclusion/Exclusion Criteria, Primary/Secondary Endpoints, and Adverse Events. Dosage units are typically mg/kg or mg, and concentration units are µg/mL or ng/mL, often accompanied by time point information. Inclusion/Exclusion Criteria are usually long text descriptions containing complex medical terminology and logical relationships.
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
Monoclonal antibody clinical trial pre-screening significantly impacts multi-turn conversation and prompt design due to its data characteristics. First, precise matching of core fields like target, indication, and mechanism of action is crucial for successful conversations. Prompts must accurately guide user input and identify synonyms or hypernyms. Multi-turn conversations need to handle complex logic within inclusion/exclusion criteria, such as "age between 18 and 65 years, and no severe hepatic or renal insufficiency." This requires the conversational system to understand and parse conditional statements, converting user-provided conditions into queryable structured expressions.
Second, dosage and frequency units and numerical ranges require validation during the conversation to prevent invalid user input. For example, when a user mentions "dosage," the system should differentiate between "mg" and "mg/kg" and prompt the user for unit information. Due to varying data update frequencies, prompt design must consider timeliness, guiding users to specify a query time range to ensure result relevance. Additionally, adverse event reports are often semi-structured text. Prompts need to extract key adverse event types and severity from free text, potentially requiring named entity recognition (NER) techniques.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | Accommodates long clinical protocol descriptions, ensuring complete context understanding |
promptTemplate | Structured Output Prompt | Guides the AI to extract key information like target, indication, and inclusion/exclusion criteria |
similarityThreshold | 0.75 | Improves the accuracy of relevant clinical trial document recall |
rerankReturnCount | 10 | Provides more potentially relevant documents for secondary analysis after initial screening |
Chunk size | 800 characters | Balances semantic completeness of long texts with retrieval efficiency |
maxTokensPerResponse | 500 token | Ensures a single response can include key screening results and explanations |
Three Common Mistakes
- AI conversations contain logical errors or omissions in screening conditions, for example, interpreting "hypertension AND diabetes" as "hypertension OR diabetes." This occurs because prompts do not sufficiently emphasize the logical relationships (AND/OR) between conditions.
- Workflow variables are empty when attempting to obtain code execution output. This is typically due to a mismatch between the
workflowOutputvariable name and the actual workflow configuration output name. - The conversational system fails to recognize user-entered dosage units, leading to inaccurate or invalid query results. This happens because prompt design does not pre-set recognition for various dosage units or confirm units in multi-turn conversations.
How to Confirm Proper Configuration
- Test multiple complex user queries, including nested inclusion/exclusion criteria, to verify the AI correctly parses and matches qualifying clinical trials.
- Check conversation logs to confirm the
maxContextconfiguration fully captures multi-turn conversation history, especially for contexts involving long text descriptions. - Query for different targets and indications to verify
similarityThresholdandrerankReturnCountconfigurations recall a sufficient number of highly relevant clinical trial documents. - Simulate user input for various dosages, frequencies, and other numerical values. Observe if the system correctly identifies and validates units or prompts for unit clarification when necessary.
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.