Data Characteristics
Data for psychiatric clinical trial pre-screening comes from various sources. These include public clinical trial registries (e.g., ClinicalTrials.gov), medical journal articles, conference abstracts, drug inserts, treatment guidelines, and patient recruitment protocols. Data update frequencies vary. Clinical trial registration information updates in real-time or weekly. Medical papers update according to journal publication cycles. Document structures are primarily unstructured text, such as PDF files of research protocols or HTML/XML formats of papers. Fields and units are highly specific. Examples include diagnostic criteria (DSM-5 or ICD-10 codes), scale scores (e.g., HAM-D, PANSS, CGI-S), specific numerical values in inclusion/exclusion criteria (e.g., age range 18-65 years, BMI 18.5-24.9), and specific drug dosage units (e.g., mg/day).
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The highly specialized and unstructured nature of psychiatric data places specific demands on knowledge base construction and retrieval. Accurate identification and semantic understanding of specialized terms like diagnostic criteria and scale scores are critical. General-purpose tokenizers may struggle with these. Numerical information in inclusion/exclusion criteria, such as age, BMI, and specific scale thresholds, requires the retrieval system to support numerical range matching. Due to diverse data sources and varying update frequencies, the knowledge base needs to support integration of multi-source heterogeneous data and incremental updates. Patient recruitment protocols often contain extensive narrative text. Retrieval results must match keywords and capture deeper semantic meaning to avoid missed recalls due to synonyms, near-synonyms, or differing phrasing. The system needs to distinguish subtle differences between various guideline versions or research protocols to prevent information confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Clinical research protocols and papers in psychiatry often have long paragraphs with complex logic and multiple inclusion/exclusion criteria. Longer segment lengths help preserve contextual completeness. |
Chunk Overlap Length (Segment Overlap Length) | 128 characters (characters) | This ensures critical information is not lost at segment boundaries, improving retrieval continuity. |
Recall count (Number of Retrieved Items) | Top 8–12 entries (top 8–12 items) | Given the complexity of psychiatric clinical trial pre-screening, retrieving enough candidate items allows the model to make comprehensive judgments and avoid missing critical information. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Psychiatric terminology is highly specific. A higher similarity threshold helps filter out irrelevant general text, improving retrieval precision. |
Rerank result count (Number of Reranked Items) | Top 5 entries (top 5 items) | Reranking further optimizes retrieval results, focusing on the few most relevant items to the user query, reducing the processing burden on the subsequent large language model. |
Max Context Token Count | 4096-8192 | Psychiatric clinical trial pre-screening often requires understanding complex inclusion/exclusion logic and multi-dimensional patient information, necessitating a larger context window for processing. |
Common Pitfalls
- Knowledge base retrieval results are empty or return a small amount of irrelevant general text. This happens when knowledge base data does not cover specialized terms or specific diagnostic criteria in the user query, or when the segmentation strategy truncates critical information.
- Knowledge base search behavior in conversations is inconsistent; it sometimes triggers and sometimes does not. This is due to an improperly set
Similarity threshold(Similarity Threshold) or query routing logic that fails to effectively identify psychiatric-related professional queries. - The system cannot automatically select the corresponding knowledge base for searching based on the user's input disease type. This occurs when the
Knowledge Base IDvariable is not configured or is configured incorrectly, preventing the system from dynamically switching knowledge bases, or when the discriminator logic does not cover all expected disease types.
How to Confirm Correct Configuration
- For typical psychiatric clinical trial pre-screening queries (e.g., "Can a patient diagnosed with severe depression, HAM-D score greater than 20, and aged 18-65 be enrolled?"), check if knowledge base recall results include relevant inclusion/exclusion criteria, scale thresholds, and diagnostic bases.
- Simulate multi-turn conversations and observe if the system consistently and accurately triggers knowledge base searches during the conversation, returning information highly relevant to the current turn's query intent.
- Verify that when processing queries for different psychiatric disorders (e.g., schizophrenia, bipolar disorder), the system correctly retrieves and recalls specialized data from the corresponding knowledge bases based on disease characteristics, such as specific scales or drug information for the relevant disorder.
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.