Document Parsing and Chunking for Psychiatric Drug Safety

Data Characteristics

Psychiatric drug safety data comes from diverse sources. These include clinical trial reports, real-world evidence (RWE) data, case reports, medical literature, drug labels, and regulatory safety updates. Update frequencies vary. Clinical trial reports typically release after study completion. Drug labels and regulatory updates dynamically adjust throughout a drug's lifecycle.

Document structures are complex. They contain highly structured tabular data, such as adverse event lists and patient baseline characteristics. They also include extensive unstructured text descriptions, like case details, physician notes, and patient self-reports.

Fields include common identifiers like patient ID, drug name, dosage, and administration route. They also include symptom descriptions (e.g., hallucinations, delusions, mood swings), severity scores (e.g., PANSS, HAM-D), diagnostic criteria (e.g., DSM-5), and adverse reaction onset time, duration, and outcome. Units involve dosage (mg, g), frequency (times/day), time (hours, days, months), and rating scale scores.

Constraints on Document Parsing and Chunking

The complexity of psychiatric drug safety documents places specific demands on document parsing and chunking.

First, multi-source heterogeneous data requires flexible parsing strategies to accommodate various input formats. Symptom descriptions and diagnostic criteria in unstructured text often contain extensive medical terminology and narrative language. This demands finer text segmentation to ensure key information remains intact.

Second, psychiatric symptoms are often subjective and ambiguous. Rating scale scores must be accurately identified and extracted. Improper chunking can lead to context loss, affecting subsequent information extraction accuracy.

Third, temporal information, such as adverse drug reaction onset and duration, is scattered across different paragraphs. Chunking must maintain the integrity of these temporal elements to facilitate event chain reconstruction.

Finally, differing update frequencies mean the parsing system must support incremental updates and version management. This ensures processing of the latest and most complete safety information.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports or large literature sets can be substantial. Ensure full upload capability.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDFs or complex DOCX files can be time-consuming. Prevent timeout interruptions.
`Chunk size`	`800–1200 characters`	Balances completeness of symptom descriptions with contextual relevance. Avoids truncating key medical terms.
`Chunk Overlap Length`	`150–200 characters`	Ensures sufficient contextual overlap between adjacent chunks. Aids subsequent understanding and correlation.
`EnabledTable Recognition`	`Yes`	Clinical trial reports contain numerous structured tables. Accurate extraction of adverse events and patient baselines is necessary.
`EnabledSmart Chunking`	`Yes`	For unstructured text, smart chunking better identifies semantic boundaries, improving chunk quality.

Common Pitfalls

When uploading large PDF files, the parsing node returns a 404 error. This typically occurs due to incorrect file upload directory permissions or insufficient storage space on the server.
The parsed results are missing score fields for symptom rating scales (e.g., PANSS). This happens when the chunk length is too short, separating the score from its corresponding symptom description.
A specific model version (e.g., Qwen3-14B) cannot extract expected fields from parsing results, while another version (e.g., Qwen2.5-14B) can. This is usually due to the model's compatibility with a specific parser output format or differences in training data.

Validation Steps

Select a typical psychiatric drug safety document containing structured tables and long narratives. Upload it and examine the parsing results. Verify that tabular data is fully extracted and text chunks align with semantic logic.
Inspect the parsed chunk content. Ensure key medical terms, symptom descriptions, and diagnostic criteria are not truncated. Confirm that score values remain within the same or adjacent chunks to their corresponding items.
Test uploads with files of varying sizes and complexities. Observe if the PARSE_FILE_TIMEOUT_SECONDS parameter effectively covers the longest parsing times, preventing parsing failures.
For specific fields (e.g., drug dosage, adverse event onset time), conduct random checks in the parsing results. Validate extraction accuracy and completeness. Adjust Chunk size and Chunk Overlap Length based on actual needs.

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.