Data Characteristics in This Category
Data from medical devices in pharmacovigilance primarily originates from device log files, alarm records, parameter trend data, and device-generated reports. This data typically exists in structured or semi-structured formats, such as CSV, JSON, or proprietary binary formats. Data update frequency varies based on device operational status and configuration, ranging from seconds (e.g., heart rate, SpO2) to hours (e.g., device self-test reports).
In terms of document structure, medical device data often includes fields like device model, serial number, firmware version, patient ID (if integrated with an EMR system), timestamp, physiological parameters (e.g., SpO2, HR, NIBP_SYS, NIBP_DIA), alarm type and level, and operator intervention records. Units are usually international standard units, such as percentages, millimeters of mercury (mmHg), or beats per minute. Some data may contain device-specific codes or status words, requiring specific parsing rules.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The diversity and high-frequency updates of medical device data impose specific requirements on FastGPT deployment and upgrades. First, data sources are decentralized, requiring integration of multiple data ingestion methods to support various log file formats and real-time data streams. Second, the high volume of physiological parameter data presents challenges for knowledge base storage capacity and indexing efficiency, necessitating efficient data preprocessing and cleansing mechanisms. Device-specific codes and status words require standardization before data import or the establishment of corresponding terminology mappings within the knowledge base. During upgrades, compatibility between new and old data formats is critical. When device firmware updates lead to data structure changes, the knowledge base must transition smoothly and correctly parse historical data. Furthermore, due to patient safety concerns, data processing real-time performance and accuracy requirements are very high. System stability and fault recovery capabilities are key deployment considerations.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Medical device log files can be large, requiring sufficient upload capacity. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large or complex device log files requires longer parsing times. |
maxContext | 3000 Tokens | Physiological parameter trend data has a long context, requiring more tokens to capture continuity. |
Chunk size | 800–1200 characters | Balances the completeness of alarm events and local trends of physiological parameters, ensuring semantic coherence. |
Similarity threshold | 0.75 | Pharmacovigilance scenarios demand high accuracy in information matching to avoid false positives or negatives. |
Recall count | Top 10 entries | Ensures coverage of various relevant alarm records and abnormal physiological parameter data. |
Three Common Pitfalls
- After importing knowledge base training data, query results lack relevance or contain significant irrelevant information. This occurs when unstructured alarm descriptions in raw device logs are not effectively extracted and standardized, leading to semantic understanding deviations.
- After upgrading FastGPT, some historically imported medical device data queries fail or return null values. This happens when the new version's data parser compatibility with old device-specific fields is not checked, resulting in data structure parsing errors.
- When responding to abnormal events, the model cannot provide specific physiological parameter values or timestamps. This is due to not correctly identifying timestamps and key numerical fields as retrievable or structured entities during data import, preventing the model from accurate referencing.
How to Confirm Correct Configuration
- Upload representative medical device log files. Check if the knowledge base correctly parses device models, timestamps, main physiological parameters (e.g.,
HR,SpO2), and alarm events. - Conduct simulated queries for typical pharmacovigilance scenarios, such as "hypotension alarm events for a specific device within a particular time frame." Check if the returned results include accurate alarm records and relevant physiological parameter trends.
- After a FastGPT upgrade, randomly select log data from different time points and device models for import and querying. Compare query results before and after the upgrade to assess data parsing and retrieval compatibility.
- Verify the mapping relationships for device-specific codes or status words in the knowledge base. Query specific codes to confirm that corresponding Chinese explanations or risk levels are returned.
Note: 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.