Autoimmune Pharmacovigilance: Tool Calling and Plugins

Data Characteristics

Autoimmune disease pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE), post-market surveillance systems (such as FDA FAERS, EMA EudraVigilance), and medical literature. Data update frequencies vary. Clinical trial data typically releases after study completion, while post-market surveillance data accumulates continuously and is regularly made public. Document structures are diverse, including structured Case Report Forms (CRFs), semi-structured medical records, and unstructured free-text reports. Fields cover patient demographics, disease diagnosis, concomitant medications, adverse event (AE) descriptions, severity, outcomes, and causality assessments. Adverse event descriptions often include medical terminology (e.g., MedDRA codes). Dosage units may involve milligrams (mg), International Units (IU), milliliters (mL), and often accompany dosing frequencies (e.g., once daily, twice weekly).

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The multi-source and heterogeneous nature of autoimmune disease pharmacovigilance data requires tool calling to have robust data integration capabilities, handling diverse data input formats and structures. Uncertain update frequencies necessitate plugin designs that support periodic or event-driven data synchronization mechanisms to ensure access to the latest information. Diverse document structures, especially free-text reports, demand high accuracy from Natural Language Processing (NLP) and Named Entity Recognition (NER) tools to extract critical drug, adverse event, and patient information from unstructured text. The presence of specialized terminology like MedDRA codes requires tool calling to integrate with medical dictionaries or ontology services for terminology standardization and concept mapping. Furthermore, parsing fields with units like dosage and frequency requires precise unit identification and conversion capabilities to prevent analysis errors due to unit confusion.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Accommodates large clinical reports or bulk imported CSV/JSON files.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Adapts to parsing times for complex PDF files (e.g., hundreds of pages of clinical study reports).
`Chunk size`	`800–1200 characters`	Balances semantic completeness of text with RAG retrieval efficiency, considering lengthy adverse event descriptions.
`Recall count`	`Top 10 entries`	Increases relevant information coverage, especially for complex queries and multi-dimensional information retrieval.
`Similarity threshold`	`0.75`	Ensures the medical relevance of retrieval results and filters out loosely related information.
`ENABLE_MEDDRA_MAPPING`	`True`	Enables integration with MedDRA dictionary for standardizing adverse event terminology.

Common Pitfalls

400 Bad Request error during tool invocation, indicating incorrect parameter format. This occurs when dosage or frequency information extracted from free text is not converted to the specific format required by the API. For example, 10mg qd is not correctly parsed into {"value": 10, "unit": "mg", "frequency": "qd"}.
Empty or incomplete result sets after plugin execution. This happens when the text processing stage fails to accurately identify and extract all relevant medical entities, leading to missing necessary input parameters for subsequent tool calls or overly strict query conditions.
Parsing tasks for large PDF documents time out or remain unresponsive for extended periods. This results from a default PARSE_FILE_TIMEOUT_SECONDS that is too short to accommodate the processing time for large or complexly formatted files.

Verification Steps

Upload representative structured and unstructured data files. Check tool call logs to confirm that all key fields (e.g., drug names, adverse events, dosage units) are correctly identified and extracted, without garbled characters.
Execute queries containing specialized medical terminology. Verify that the plugin, through MedDRA mapping, matches query terms with standardized terms in the knowledge base and returns relevant results.
Test parsing with PDF documents of varying complexity. Observe the completion time of parsing tasks to ensure they finish within PARSE_FILE_TIMEOUT_SECONDS and that content integrity meets expectations.

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.