Data Characteristics
Cardiovascular intervention clinical trial pre-screening data comes from various sources. These include Electronic Health Record (EHR) systems, Clinical Trial Management Systems (CTMS), medical imaging reports (e.g., coronary angiography, echocardiography), and patient-reported questionnaires. Data updates frequently, especially during patient follow-up. Vital signs, laboratory results, and imaging reports can update daily or weekly.
EHR data typically consists of structured fields such as diagnosis codes (ICD-10), medication dosages, and test result values. It also includes extensive unstructured physician progress notes and discharge summaries. Medical imaging reports contain structured measurement data and unstructured image descriptions. Fields and units are highly specialized. For example, "Left Ventricular Ejection Fraction (LVEF)" is expressed as a percentage, "Vessel Stenosis Degree" as a percentage, and "Troponin I" in ng/mL.
Constraints Imposed by Data Characteristics on Multi-turn Conversation and Prompts
The high update frequency of cardiovascular intervention data requires multi-turn dialogue systems to quickly integrate the latest information. This prevents judgments based on outdated data. For instance, changes in a patient's cardiac function might affect their enrollment criteria; the system must identify and update this promptly.
The coexistence of structured and unstructured data in documents means prompt design must precisely extract specific numerical information (like LVEF) and understand complex clinical reasoning in physician text descriptions. Specialized fields and units demand domain knowledge from the dialogue model. The model must correctly interpret medical terms like "coronary stenosis > 70%" and handle unit conversions or identify missing units. Additionally, subjective descriptions in patient-reported questionnaires require the model to have semantic understanding to integrate them with objective medical data for comprehensive evaluation.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 10 | Clinical pre-screening multi-turn conversations often involve patient history, test results, and inclusion/exclusion criteria. This retains enough turns to maintain conversational coherence and balance efficiency. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Medical text segments are often long, containing detailed medical history and examination descriptions. This length helps maintain semantic integrity. |
Recall count (Recall Count) | Top 10 entries (top 10 items) | Clinical pre-screening requires comprehensive consideration of various patient indicators. Increasing the recall count improves the coverage of relevant information and reduces omissions. |
Similarity threshold (Similarity Threshold) | 0.75 | For precise matching of medical terminology and clinical descriptions, a higher threshold ensures the accuracy and relevance of recalled information. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5 items) | After initially filtering a large amount of information, reranking selects the most relevant core items, improving the efficiency of presenting key information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Medical imaging reports and progress note files can be large and require more time to parse. This configuration prevents parsing failures due to timeouts. |
Common Pitfalls
- The dialogue log shows numerous "unintelligible medical terms" or "data unit errors." This indicates insufficient model training or a lack of specific domain vocabulary and unit definitions in the knowledge base.
- After multiple turns of dialogue, the system's recommended pre-screening results do not match the patient's latest clinical data. This is due to an imperfect knowledge base update mechanism, failing to synchronize the latest EHR or CTMS data in a timely manner.
- When the API returns data in a streaming output, the frontend perceives the return speed as too slow. This occurs because the
STREAM_RESPONSE_BUFFER_SIZEparameter is set too low or backend processing is delayed, causing data to accumulate before being sent all at once.
Verification of Configuration
- Conduct simulated clinical pre-screening dialogues. Verify that the system's patient inclusion/exclusion judgments align with human judgments, focusing on the citation of key indicators such as LVEF and stenosis degree.
- Examine dialogue logs. Confirm that the model correctly references and coherently understands patient history, medication history, and other information mentioned in previous turns during multi-turn conversations.
- Through API calls, observe the response speed and data integrity of streaming output. Ensure that, under normal network conditions, the interval between single data packet transmissions is below a set threshold (e.g.,
1-2 seconds(seconds)). - Randomly select clinical documents. Perform file parsing and verify that the parsed segment content includes all key medical information without significant sentence breaks. Check that the
Chunk size(segment length) meets expectations.
The values provided are common starting points. They should be measured 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.