Document Parsing and Chunking for mRNA Vaccine Clinical

Data Characteristics

mRNA vaccine clinical trial pre-screening data originates from study protocols, subject screening logs, medical history records, laboratory test reports, and imaging data. This data is stored in PDF, Word, CSV, or XML formats. Study protocols and informed consent forms are typically structured PDFs, containing extensive text descriptions and embedded tables. Laboratory test reports are often semi-structured CSVs or PDFs, including numerical values for blood counts, biochemical indicators, and viral loads. Medical history records and imaging reports primarily consist of unstructured text. Data update frequency varies across clinical trial stages; screening data is generated intensively, while follow-up data updates periodically according to visit schedules. The data includes numerous medical terms, abbreviations, and specialized units, such as ng/mL (nanograms/milliliter) and IU/mL (international units/milliliter) for antibody titers, and Ct values (cycle threshold) for nucleic acid detection.

Constraints Imposed by Data Characteristics on Document Parsing and Chunking

The complex text structures of study protocols and medical history records in mRNA vaccine clinical trial pre-screening data require parsers to effectively handle multi-level headings, nested lists, and cross-page tables. Numerical data and medical units in laboratory reports necessitate retaining complete numerical context during chunking to prevent unit-value separation. Medical abbreviations and synonyms in documents challenge the semantic integrity of chunks, requiring each chunk to contain sufficient contextual information for subsequent semantic understanding. Image-based tables common in PDF documents demand OCR capabilities to extract table content. Furthermore, since data may contain sensitive subject information, parsing and chunking processes must consider data anonymization requirements to ensure privacy compliance. The frequency of document updates, particularly for screening logs and laboratory results, means the parsing pipeline needs high throughput and incremental update capabilities to handle rapidly changing data streams.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800–1200 characters`	Ensures the contextual integrity of medical terms and numerical units, preventing critical information truncation.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–200 characters`	Connects semantic meaning between different chunks, handling long sentences and related information in medical descriptions.
`File Type Whitelist`	`pdf, docx, csv, xml`	Covers the main formats for clinical trial pre-screening documents, supporting structured and unstructured data.
`OCR_ENABLED`	`true`	Identifies image-based tables and scanned documents in PDFs, extracting key numerical values and text.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processes large study protocols or complex documents containing extensive image-based content.
`MAX_CHUNKS_PER_FILE`	`Calibrate by actual measurement`	Limits the number of chunks generated per file, preventing memory overflow while ensuring information coverage.

Common Pitfalls

File parsing status remains stuck or fails after upload, with logs showing File processing timeout. This usually occurs when PARSE_FILE_TIMEOUT_SECONDS is set too short, preventing the processing of PDF files with extensive images or complex tables.
The model's output for table content is incomplete or malformed, displaying ...[hide XXX char]. This often happens when Markdown-formatted tables in the original document are truncated during parsing or chunking, leading to semantic discontinuity.
In retrieval results, numerical values and units in medical test reports are separated, for example, antibody titer 100 without IU/mL. This indicates that the chunking strategy failed to effectively retain the complete context of numerical data, possibly due to an insufficient Chunk size (Chunk Length) or Chunk Overlap Length (Chunk Overlap Length).

Validation Steps

Select various document types (study protocols, laboratory reports, medical history records) at random, upload them, and check if parsing is successful and within expected timeframes.
Preview parsed documents and verify that key information (e.g., subject ID, main inclusion/exclusion criteria, key indicator values and units) is completely and accurately retained within the chunks.
Use FastGPT's retrieval test function to input queries containing specific medical terms or numerical values. Check if the returned chunks contain complete contextual information, especially numerical values and their corresponding units.
Simulate a clinical trial pre-screening scenario. Use the model for question-answering tests on the parsed documents. Evaluate the accuracy, completeness, and professionalism of the answers, paying special attention to the understanding of table data and medical terminology.

The values provided are common starting points. Measure them against your own samples to find the best fit.

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.