Data Characteristics in this Category
Clinical decision support for clinical trial pre-screening primarily processes data from electronic medical record systems, laboratory information systems, medical imaging reports, genetic testing reports, and historical clinical trial records. This data typically exists as unstructured or semi-structured documents, such as PDF medical records, Word format examination reports, and TXT format genetic sequencing results. Data updates frequently; patient visits, examinations, and treatments all generate new data. Document structures are complex and diverse, containing extensive medical terminology, abbreviations, and specialized terms. Fields and units include both standardized international units (e.g., mmol/L, g/dL in complete blood counts) and proprietary indicators and classifications specific to clinical trial protocols (e.g., RECIST criteria assessment results).
Constraints Imposed by these Characteristics on Document Parsing and Chunking
High-frequency data updates require the parsing system to process data in real-time or near real-time. This ensures pre-screening results are based on the latest patient information. Complex document structures demand parsing tools effectively identify and extract key information from various document formats, including text, tables, and text within images. The prevalence of medical terminology and abbreviations challenges the accuracy of word segmentation and entity recognition. This requires integration with medical dictionaries and ontologies to prevent misinterpretation or information loss. Diverse fields and units necessitate the parser standardize data representation from different sources. For example, it must convert measurement values with different units, or identify and handle missing and anomalous values. These actions directly impact subsequent feature engineering and matching logic.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large imaging reports or multiple bundled medical records. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for complex PDFs or large text files. |
Chunk size (Chunk Length) | 800–1200 characters | Balances contextual completeness with retrieval efficiency, suitable for medical report paragraph structures. |
Overlap Length | 100–200 characters | Ensures key information across paragraphs remains linked, reducing context loss risk. |
OCR_ENABLED | true | Recognizes text information in scanned medical records and images. |
TABLE_RECOGNITION_ENABLED | true | Extracts structured data from tables like lab results and medication lists. |
Three Common Pitfalls
- The parsing service returns a file processing failure status code. This might occur if the file size exceeds system limits.
- Some PDF content in the knowledge base is not retrievable. This typically happens when the file is a scanned document, and OCR functionality is not enabled or OCR recognition is poor.
- After uploading a document, its content is not cited or is incompletely cited in conversations. This may relate to a chunk length setting that is too small, leading to key information being truncated.
How to Verify Correct Configuration
- Upload typical clinical trial pre-screening documents in various formats (PDF, DOCX, TXT). Check if all parsing statuses are successful. Verify file content is retrievable in the knowledge base.
- For documents containing tables and scanned text, retrieve relevant keywords. Verify table data and image text are accurately recognized and indexed.
- Adjust different chunk length and overlap length configurations. Test the question-answering effectiveness on long documents. Evaluate if contextual completeness meets expectations.
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.