Data Characteristics in this Domain
Mental illness clinical trial data originates from diverse sources. These include Electronic Health Records (EHR), patient self-report scales, clinician interview notes, imaging reports, and genomic data. Data update frequencies vary. EHR data may update in real-time, while scales and interview notes update based on visit cycles. Document structures often include unstructured text, such as doctor's notes and patient narratives, or semi-structured tables for scale scores and medication records. Field specifics include diagnostic criteria (e.g., DSM-5 classification), symptom descriptions (e.g., anxiety, depression levels), cognitive function scores, and treatment response indicators. For units, scales typically use Likert scale scoring. Drug dosages are in milligrams (mg) or milliliters (mL). Precise parsing is required for these units.
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The highly unstructured nature of mental illness data requires robust text parsing capabilities for tool calling. This extracts key symptoms and diagnostic evidence from doctor's notes. Inconsistent data update frequencies mean plugins must flexibly synchronize with various API sources to retrieve the latest patient information. They must also handle potential data delays. For example, genomic data is typically static, while daily symptom records are dynamic; plugins need to differentiate processing. A large number of domain-specific terms and abbreviations exist, such as "MDD" (Major Depressive Disorder) and "GAD" (Generalized Anxiety Disorder). This demands high accuracy in semantic understanding and entity recognition to avoid misdiagnosis or missed diagnoses. Standardized units for scale scores and drug dosages constrain data extraction. Unit conversion or validation is necessary to ensure numerical correctness, which impacts subsequent decision logic.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for this Recommendation |
|---|---|---|
toolCallStrategy | auto | Allows the model to autonomously decide whether to call tools based on context, adapting to unstructured data parsing needs. |
maxContext | 8000 tokens | Ensures that complete patient medical history and scale data can be loaded, supporting complex condition assessment. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides a parsing time window for large unstructured text files, such as multi-year EHR records. |
similarityThreshold | 0.75 | Improves the accuracy of recalling relevant documents, reducing interference from irrelevant information in mental illness diagnosis. |
Chunk size | 500–800 characters | Optimizes text segmentation, ensuring each segment contains sufficient semantic information while avoiding redundancy from excessive length. |
recallTopK | Top 10 entries | Retrieves a sufficient number of potentially relevant pieces of information during the initial recall phase, providing a basis for subsequent re-ranking. |
Common Pitfalls
- Plugin call failures, with logs showing "API rate limit exceeded." This occurs due to improper configuration of API call frequency limits or concurrent requests, leading to too many requests to external systems in a short period.
- The model fails to correctly identify specific symptoms or diagnostic criteria, leading to deviations in pre-screening results. This happens when training data lacks sufficient and accurately annotated domain-specific terms and contexts for mental illnesses, resulting in inadequate understanding of professional terminology by the model.
- The plugin fails to correctly extract scale scores or drug dosages, returning empty values or incorrect formats. This is due to regular expressions or data parsing logic not adequately covering the various common recording formats and unit representations found in mental illness data.
Validation of Configuration
- Run a test set containing typical mental illness cases. Verify that tool calling accurately extracts diagnostic keywords from unstructured medical records.
- Check the plugin's score extraction results for various common mental illness scales (e.g., PHQ-9, GAD-7). Ensure numerical accuracy and compare with manual verification results to determine acceptable thresholds.
- Monitor system logs. Confirm that API calls do not encounter "rate limit exceeded" or other external service connection errors when handling a large number of concurrent requests. This evaluates concurrent processing capability.
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.