Tool Calling and Plugins for Cardiovascular Clinical Trial

Data Characteristics

Cardiovascular clinical trial data is diverse and heterogeneous. Data sources include Electronic Health Records (EHR), imaging reports (e.g., ECG, echocardiography, CT/MRI), laboratory test results (e.g., blood lipids, cardiac enzymes, biomarkers), patient-reported questionnaires, and wearable device data. Data update frequencies vary; EHR data may update in real-time, while imaging reports or annual physical examination data update periodically. Document structures include unstructured clinical notes, semi-structured examination report templates, and structured database records. Fields and units also vary. Blood pressure is typically recorded as mmHg, heart rate as bpm. Lipid panel indicators like LDL-C and HDL-C are expressed in mmol/L or mg/dL. ECG interpretation involves complex waveform features and diagnostic terminology.

Constraints Imposed by Data Characteristics on Tool Calling and Plugins

The complexity of cardiovascular clinical trial pre-screening data imposes several constraints on tool calling and plugins. First, multi-source heterogeneous data requires plugins with robust data parsing and standardization capabilities. For unstructured text, Natural Language Processing (NLP) tools are necessary for entity recognition and relation extraction to identify key information such as disease diagnoses, medication history, and surgical history. Second, varying data update frequencies mean tool calls must support asynchronous processing and incremental updates to avoid reprocessing historical data. Processing imaging data and ECG waveforms requires specialized image recognition and signal processing plugins. These plugins must convert results into structured features suitable for rule matching or machine learning models. Inconsistent field units require built-in unit conversion functions within plugins to ensure accuracy when comparing data from different sources, for example, converting mg/dL to mmol/L.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`8000 tokens`	Balances information from multiple clinical documents with model processing efficiency.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Time required to process large imaging reports or complex medical records.
`similarity_threshold`	`0.75`	Ensures accurate recall of relevant clinical terms and symptom descriptions.
`rerank_top_k`	`5`	Further filters the most relevant items from high-similarity results.
`tool_request_timeout_ms`	`30000 ms`	Accommodates potential response delays from external medical knowledge bases or imaging analysis services.
`extract_entity_types`	`Disease, Symptom, Drug, LabResult`	Focuses on extracting core entity types for cardiovascular pre-screening.

Common Pitfalls

401 Unauthorized errors occur when calling external tools. This happens due to incorrect configuration or failure to pass authentication information, such as the Authorization field, in HTTP request headers.
Generated flowcharts or reports may have empty image links or parsing failures, resulting in un hiển thị images. This is caused by slow responses from external drawing services or response formats that do not match expectations.
Key laboratory indicators, such as the LDL-C field value, are missing from pre-screening results. This occurs when data parsing plugins fail to correctly identify fields with different units or aliases, leading to unit conversion failures.

Verification Steps

Call an external medical knowledge base API. Verify it correctly returns drug and diagnostic standard information related to cardiovascular diseases. Check that the returned JSON structure matches expectations.
Upload a patient medical record containing ECG and echocardiography reports. Confirm that image processing or text parsing tools successfully extract key imaging features and diagnostic conclusions.
Process multiple blood lipid reports with different units (e.g., mg/dL and mmol/L). Check that all relevant indicators are uniformly converted to the target unit and that values are correct.

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.