Data Characteristics
Rehabilitation equipment clinical trial pre-screening data originates from electronic medical record (EMR) systems, manufacturer product documentation, and clinical trial protocols. EMR data includes patient diagnoses, treatment plans, rehabilitation assessment results (e.g., FIM scores, Berg Balance Scale), imaging reports, and lab results. This data exists as unstructured text, semi-structured tables, and structured fields. Manufacturer documentation is primarily in PDF format, covering device parameters, usage instructions, indications, and contraindications. Clinical trial protocols are structured documents detailing inclusion/exclusion criteria, trial procedures, and observation metrics. Data update frequencies vary: EMRs are real-time or near real-time, while device documentation and trial protocols update with new versions. Fields and units are highly specialized; for example, rehabilitation assessment scales have specific scoring criteria and ranges, and device parameters involve power, frequency, and waveforms, all requiring precise identification.
Constraints Imposed by Data Characteristics on Model Integration and Configuration
The specialized and diverse nature of rehabilitation equipment data places strict demands on model integration. Unstructured text, such as medical record descriptions, requires models with strong entity recognition and relation extraction capabilities to accurately extract key patient information. Semi-structured tables and structured fields require models to precisely parse data structures, preventing information loss or misalignment. For example, specific scoring items and their corresponding values in rehabilitation assessment scales must be correctly associated by the model. PDF-formatted device documentation requires efficient document parsing to separate images and text and identify key parameters. Inconsistent data update frequencies necessitate knowledge bases with incremental update and version management mechanisms, ensuring the model always uses the latest information for pre-screening. Recognizing specialized fields and units is central to the model's understanding of clinical trial inclusion/exclusion criteria, such as identifying and differentiating applicable age ranges or disease stages for various devices to avoid misjudgments.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Accommodates lengthy medical record descriptions and device technical documents, ensuring contextual completeness. |
Chunk size (Segment Length) | 500 characters (characters) | Balances textual semantic integrity and retrieval efficiency, ensuring key information is not fragmented. |
Recall count (Retrieval Count) | 15 entries (items) | Enhances the ability to retrieve relevant information from large document sets, covering more potential matches. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures retrieved results are highly relevant to the query intent, reducing low-quality matches. |
Rerank result count (Reranked Return Count) | 5 entries (items) | Focuses on the most relevant results, improving the accuracy and efficiency of the model's final judgment. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing of large PDF documents, especially device manuals containing complex charts and tables. |
Common Pitfalls
- The model fails to correctly identify the correspondence between rehabilitation assessment scale names and specific scoring items, resulting in empty patient assessment result fields. This occurs because the model's training lacked sufficient exposure to this specific format of medical text structure.
- Uploading a PDF-formatted device manual fails to parse, returning a
500error code, or parsed content is missing significant table data. This typically happens when the PDF document has a complex internal structure, including non-standard fonts or embedded images, which the parser cannot effectively handle. - In a private deployment environment, the model encounters a
Connection refusederror when calling an external API. This often results from network configuration or firewall policies blocking the connection between the model service and the external API endpoint.
Validation Steps
- Upload a patient medical record containing a complete rehabilitation assessment scale and structured fields. Verify the accuracy of key information extracted by the model, especially numerical indicators and categorical labels.
- Upload a typical rehabilitation device product technical manual (PDF format). Check if the text content parsed by the model is complete, particularly device parameter tables and indications descriptions.
- Use a clinical trial protocol with different inclusion/exclusion criteria. Ask the model questions about whether a patient meets specific trial criteria. Observe if the model correctly cites protocol clauses and provides justification for its judgment.
Note: The values provided are common starting points. Measure performance against your own data samples for optimal configuration.
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.