Multiturn Conversation and Prompts for Cardiovascular

Data Characteristics

Cardiovascular clinical trial pre-screening data originates primarily from Electronic Health Record (EHR) systems, medical imaging reports (e.g., ECG, echocardiogram), laboratory test results (e.g., blood lipids, blood glucose, cardiac enzymes), and patient self-reported questionnaires. Data update frequencies vary; EHR data may update in real-time, while imaging and lab results generate according to treatment cycles. Document structures are complex, including unstructured physician diagnostic notes, structured lab report tables, and semi-structured imaging descriptions. Fields and units are highly specialized medical terms, such as cardiac function classification (NYHA Class I-IV), ejection fraction (%), troponin (ng/mL), and blood pressure (mmHg). Extensive medical abbreviations and synonyms are present.

Constraints on Multiturn Conversation and Prompts

Complex data structures and diverse information sources require the multiturn conversation system to have robust information extraction and integration capabilities. Unstructured physician diagnostic records necessitate the model accurately understands medical terminology and context to precisely identify a patient's cardiovascular status in multiturn conversations. High-frequency updates for certain metrics (e.g., blood pressure, heart rate) demand the system retrieve the latest data to avoid judgments based on outdated information, impacting real-time knowledge base synchronization strategies. Specialized fields, units, extensive medical abbreviations, and synonyms challenge prompt robustness. Prompts must handle these variations and guide the model to clarify and confirm information during multiturn interactions, preventing pre-screening errors due to terminology misunderstandings. Furthermore, the mixture of different data types (text, numerical) requires the conversation system to handle them flexibly, for example, effectively combining patient verbal input and structured test results when querying numerical ranges.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`6`	Cardiovascular pre-screening often involves long inquiry chains, requiring more historical conversations to understand the patient's full picture.
`Chunk size`	`800 characters`	Medical text is dense; long segments may introduce too much noise, while short segments can lose context.
`Recall count`	`Top 10 entries`	Ensures coverage of more relevant medical knowledge and patient information, improving pre-screening accuracy.
`Similarity threshold`	`0.75`	Symptoms and indicators of cardiovascular diseases have subtle differences; a high threshold helps with precise matching.
`Rerank result count`	`Top 5 entries`	Further prioritizes the most relevant information after initial retrieval through re-ranking.
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Accounts for potentially large medical imaging reports or detailed medical record documents, providing sufficient upload space.

Common Pitfalls

After uploading a file during a conversation, a 503 Service Unavailable message appears, but backend logs show a successful upload. This typically indicates a file parsing timeout, especially for large or complex cardiovascular imaging reports, caused by an excessively low PARSE_FILE_TIMEOUT_SECONDS parameter.
When using a strict Q&A template, the model replies "No answer found" when encountering an entry in the knowledge base that is an image URL. This occurs because strict Q&A templates have limited ability to process non-textual information and cannot directly parse image link content.
The model repeatedly asks for already provided cardiovascular indicators (e.g., blood pressure values) in multiturn conversations, or misunderstands medical terms self-reported by the patient. This may be due to insufficient clarification guidance for medical professional terms in the prompt, or the knowledge base not adequately covering relevant synonyms.

Validation Steps

Conduct full pre-screening process tests for typical cardiovascular disease cases (e.g., coronary heart disease, heart failure) to check if the model accurately captures key diagnostic criteria and contraindications.
Upload simulated medical record data in various formats (PDF, image, text) and sizes. Observe if the file upload and parsing process is smooth, and check if the parsed knowledge base content is complete.
Test the model's ability to understand the correlation between various cardiovascular indicators (e.g., abnormal ECG, elevated troponin) provided by the patient in multiturn conversations, ensuring correct information integration.
Check the model's recognition and handling of medical abbreviations (e.g., "EF", "BP") and common synonyms (e.g., "chest tightness" and "angina"), validating prompt robustness.

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.