Data Characteristics for This Category
Real-world evidence (RWE) product data primarily originates from electronic health records (EHRs), medical insurance claims databases, patient registries, wearable devices, and pathology reports. This data typically exists as unstructured text, semi-structured tables, and structured numerical values. Examples include clinical treatment records, imaging report text, gene sequencing results, and medication usage records. Data update frequencies vary; some clinical data may update daily, while patient follow-up data might update quarterly or annually. Document structures are complex, often containing extensive medical terminology, abbreviations, and specific codes, such as ICD-10 disease codes and LOINC laboratory codes. Fields and units are diverse, involving physiological indicators (e.g., mmol/L, mmHg), drug dosages (e.g., mg, IU), and timestamps (e.g., YYYY-MM-DD HH:MM:SS).
Constraints on Deployment and Upgrades from These Characteristics
The diversity and complexity of real-world evidence data impose high demands on the computing resources of the deployment environment. Processing large-scale unstructured text requires significant processing power and memory configuration. Inconsistent data update frequencies necessitate flexible data synchronization mechanisms in the deployment solution to accommodate periodic or event-driven data ingestion. The complexity of document structures, especially medical terminology and coding, requires more refined text processing and semantic understanding capabilities for knowledge base construction and retrieval. This means model selection should prioritize large models with a better understanding of specialized domain knowledge, or models enhanced through pre-training or fine-tuning for domain adaptation. Additionally, integrating and standardizing multi-source heterogeneous data increases the complexity of the data preprocessing stage, potentially extending initial deployment configuration and debugging time. For upgrades, new versions must be compatible with complex formats of old data and seamlessly handle continuously incoming incremental data.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
GPU_MEMORY_GB | 24 GB or higher | Sufficient video memory is necessary for processing large-scale medical text and complex model inference. |
maxContext | 8192 token | Real-world evidence documents often contain long patient histories or reports, requiring models to process longer contexts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | File parsing can be time-consuming when processing large PDFs or multi-page scanned documents. |
Chunk size (Chunk Size) | 800–1200 characters | Ensures the integrity of medical concepts, preventing critical information from being truncated during chunking. |
Recall count (Recall Count) | 10–15 items | Improves the accuracy of retrieving relevant medical information, covering more potentially relevant passages. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, e.g., 0.78 | Precise matching of medical concepts significantly impacts results; adjust based on the specific dataset. |
Three Common Mistakes
- Knowledge base queries return empty results or irrelevant content because medical entities and key information were not effectively identified or extracted during data preprocessing.
- Model response times are excessively long or frequent out-of-memory errors occur, typically due to insufficient deployed hardware resources (e.g., video memory or RAM) to support the selected model's complexity and data volume.
- After a system upgrade, some historical data cannot be indexed or queried correctly. This may be due to incompatibility between the new version's data model or parsing logic and old data formats, lacking necessary migration or compatibility handling.
How to Confirm Correct Configuration
- Upload typical medical report documents. Check if the file parser correctly identifies and extracts key fields such as basic patient information, diagnostic results, and medication lists.
- Ask specific medical questions. Verify if the model's answers accurately cite relevant literature or clinical guidelines from the knowledge base and confirm the cited sources.
- Simulate high-concurrency query scenarios. Monitor system resource utilization (CPU, memory, GPU usage) to ensure it remains within stable ranges and that response times meet expected performance thresholds.
- After data updates, perform incremental indexing tasks. Verify that new data is timely and correctly incorporated into the knowledge base and confirm its accessibility through queries.
The values provided are common starting points and should be measured against specific 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.