Data Characteristics in this Domain
Psychiatric drug safety data comes from diverse sources. These include drug labels, clinical trial reports, real-world evidence (RWE) data, case reports, medical literature, and national drug adverse event monitoring reports. Data update frequencies vary. Drug labels and monitoring reports often have fixed update cycles, while clinical cases and literature are continuously generated.
Document structures are diverse. Structured tabular data includes adverse event lists and patient demographics. Semi-structured data includes clinical report summaries and literature conclusions. Unstructured text includes detailed case descriptions and physician diagnostic records.
Fields include general drug safety information (drug name, batch, adverse event name, occurrence time, severity). They also include psychiatry-specific scale scores (e.g., Hamilton Depression Rating Scale (HAM-D), Positive and Negative Syndrome Scale (PANSS)), medication adjustment records during treatment, and descriptions of the patient's mental state. Units for dosage are typically milligrams (mg) or international units (IU). Time units include hours, days, weeks, and months. Scale scores are unitless integers or decimals.
Constraints Imposed by These Characteristics on Citation and Traceability
The high heterogeneity of psychiatric drug safety data presents multiple challenges for citation and traceability.
First, the richness of unstructured text requires the knowledge base to have strong semantic understanding. It must accurately extract key adverse event information from lengthy case descriptions.
Second, unique psychiatric scale scores and medication adjustment records require the RAG system to identify and associate these specific fields during retrieval. This ensures traceability to specific quantitative evidence.
Differences in update frequency mean the knowledge base needs a flexible update mechanism. This keeps it synchronized with the latest drug label changes or monitoring reports, avoiding outdated information.
Document structure diversity places higher demands on the knowledge base's chunking strategy. Simply chunking by fixed length can break the logical integrity of the original text, affecting traceability accuracy.
Finally, psychiatric diagnosis and treatment often involve multiple disciplines and stages. A single adverse event may be scattered across multiple documents. The system must perform cross-document traceability and association to provide comprehensive citation context.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Balances completeness and semantic coherence of psychiatric case descriptions. |
Recall count (Retrieval Count) | Top 8–15 items | Ensures coverage of relevant information from different sources and time points. |
Similarity threshold (Similarity Threshold) | Calibrate empirically | Balances recall and precision based on actual query performance. |
Rerank result count (Reranked Return Count) | Top 3–5 items | Focuses on the most relevant citations, avoiding information overload. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates parsing time for large clinical trial reports or multi-page literature. |
maxContext | 4000–8000 Tokens | Ensures sufficient citation context to support complex adverse event analysis. |
Three Common Mistakes
- After knowledge base content updates, citation results still show old version information. This typically occurs because the knowledge base index was not rebuilt promptly or due to caching mechanisms.
- The AI response cites seemingly irrelevant or semantically biased passages. This happens when the
Similarity threshold(similarity threshold) for semantic retrieval is too high, leading to the recall of overly generalized content. - Queries for psychiatry-specific scale scores do not reflect specific values or trends in the citation results. This may be because the knowledge base chunking failed to effectively preserve the context of such structured data.
How to Confirm Proper Configuration
- Select typical adverse event cases, including psychiatry-specific scale data and complex medication histories. Verify that the AI's citations accurately point to key information in the original documents.
- Simulate a drug label update. Re-submit old version-related queries. Check if the AI can cite the updated content and identify the update date.
- For specific drug adverse events, submit queries with ambiguous semantics. Check if all recalled citation items are highly relevant. Adjust the
Similarity threshold(similarity threshold) until satisfactory.
The values provided are common starting points. Measure them against your 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.