Cardiovascular Clinical Trial Pre-screening: HTTP Interface

Data Characteristics

Cardiovascular clinical trial pre-screening data comes from Electronic Health Record (EHR) systems, medical imaging reports, laboratory test results, and genomic data. This data updates frequently. Key indicators like electrocardiograms and blood pressure monitoring data may update in real-time or near real-time. Document structures are complex. They often include unstructured physician notes, structured lab reports, and semi-structured imaging descriptions. Fields include physiological indicators such as heart rate, blood pressure (systolic_bp, diastolic_bp), blood lipids (LDL_C, HDL_C, TG), and blood sugar (FPG, HbA1c). Units must be strictly differentiated, for example, mmHg, mmol/L, mg/dL. Data also includes disease diagnostic codes (e.g., ICD-10) and medication records (drug_name, dose).

Constraints on the HTTP Interface and External Systems

High update frequency for cardiovascular data requires the HTTP interface to have efficient data synchronization mechanisms. This prevents pre-screening result deviations due to outdated data. Complex document structures, especially unstructured physician notes, challenge data parsing capabilities. This requires robust natural language processing modules for information extraction. Diverse fields and units demand strict data type validation and unit conversion during data transmission. This prevents calculation errors from inconsistent units. For example, blood pressure data may exist in both mmHg and kPa. The interface must convert it to a standard unit. Genomic data is often large. This places high demands on interface bandwidth and processing capabilities. Batch or asynchronous transmission may be necessary.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Accommodates medical imaging reports and genomic data file sizes, providing ample space.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Processing complex structured and unstructured medical text takes longer.
`MAX_REQUEST_BODY_SIZE`	`50 MB`	Supports bulk uploads of ECGs or lab results, reducing the number of batches.
`DATA_SYNC_INTERVAL_MINUTES`	`15 minutes`	Addresses the high update frequency of cardiovascular indicators, ensuring data timeliness.
`CONCURRENT_REQUEST_LIMIT`	`20`	Balances system load and data processing efficiency, preventing overload.
`ERROR_RETRY_TIMES`	`3 times`	Handles network fluctuations or temporary external system failures, improving data transmission success rates.

Common Pitfalls

An HTTP status code of 400 with an empty request body occurs when Content-Type: application/json or Content-Type: multipart/form-data is not set correctly. The server cannot parse the data.
An interface response timeout, indicated by HTTP 504 Gateway Timeout in logs, happens when a large genomic file is uploaded but PARSE_FILE_TIMEOUT_SECONDS is too short. File parsing does not complete within the allotted time.
Abnormal or missing physiological indicator data in pre-screening results occurs when the systolic_bp field value from an external system has an unexpected unit. The interface did not perform unit conversion or validation.

Verification Steps

Use Postman or curl to send a POST request to http://localhost:3000/api/core/dataset. Include sample data with all expected fields and various data formats (e.g., JSON, XML, PDF). Confirm data is successfully ingested and correctly indexed.
Monitor system logs. Confirm data synchronization tasks trigger on time within the DATA_SYNC_INTERVAL_MINUTES interval and show no significant errors.
Select a batch of test cases with known pre-screening results. Submit them via the interface. Verify the returned pre-screening results match expectations, especially for critical physiological indicator values and units.
Simulate network interruptions or temporary external system unavailability. Check if the interface's error retry mechanism retries the configured ERROR_RETRY_TIMES and ultimately processes or reports failure correctly.

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.