Molecular Diagnostics Pharmacovigilance Workflow

Data Characteristics in This Domain

Molecular diagnostics data in pharmacovigilance primarily originates from gene sequencing reports, pathology analysis reports, clinical trial data, and real-world data. This data often exists as unstructured text, such as gene locus mutation descriptions, biomarker expression levels, and detection methods with result interpretations. Data update frequency varies by detection type and clinical practice, ranging from hourly (e.g., companion diagnostic results) to monthly (e.g., long-term follow-up genomics data). Document structures are diverse, containing specialized medical terminology, abbreviations, and complex figures. Key fields include gene name, mutation type, variant frequency, testing institution, test date, report number, and annotations related to drug efficacy or adverse reactions. Units involve base pairs (bp), copy numbers (CN), and percentages (%).

Constraints Imposed by These Characteristics on Workflow Orchestration

The heterogeneity of molecular diagnostics data requires robust text parsing and entity recognition capabilities within the workflow to accurately extract key information from unstructured reports. The complexity of integrating multi-source heterogeneous data means data cleaning, standardization, and deduplication are necessary before knowledge base construction to ensure data quality. The specialized and diverse nature of test reports makes predefined keyword matching or regular expressions insufficient for comprehensive coverage, necessitating more flexible AI models for semantic understanding. Varying data update rhythms require the knowledge base indexing strategy within the workflow to adapt to different data ingestion frequencies, such as incremental updates for high-real-time detection results. Furthermore, sensitive information and privacy protection requirements in the data impose strict demands on data anonymization and permission management within the workflow.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096 tokens`	Molecular diagnostics reports contain extensive professional descriptions, requiring a sufficiently long context window for complete understanding.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures a single segment can contain a complete gene variant description or test result interpretation.
`Recall count` (Recall Count)	`Top 10`	Increases recall to cover more potentially relevant gene variants or adverse reaction association information.
`Similarity threshold` (Similarity Threshold)	`0.78`	Balances recall and accuracy, filtering out semantically irrelevant results and reducing false positives.
`Rerank result count` (Rerank Return Count)	`Top 5`	After reranking, prioritizes presenting the most relevant gene-drug-adverse reaction associations to the query intent.
`PARSER_MODE`	`SEMANTIC_SPLITTER`	Addresses the complexity of unstructured text; semantic splitting better preserves contextual integrity.
`UPLOAD_FILE_TYPES`	`pdf, docx, txt, json`	Covers common document formats for molecular diagnostics reports, ensuring ingestion of diverse data sources.

Three Common Pitfalls

In AI conversation node results, the cited knowledge snippets have weak relevance to the question. This usually occurs when Similarity threshold (Similarity Threshold) is set too high or Recall count (Recall Count) is too low, preventing the model from acquiring sufficient relevant information.
During workflow execution, some test reports fail to parse, leading to data loss. This happens when UPLOAD_FILE_TYPES configuration is incomplete, not including all actual report file formats, or PARSER_MODE fails to effectively handle specific document structures.
Knowledge base search nodes do not return expected results, indicating no access permission. This is typically due to incorrect user permission configuration or the knowledge base not being correctly associated with the corresponding access control policy.

How to Verify Configuration

Select ten molecular diagnostics reports from different sources and formats. Manually verify if the workflow accurately extracts all key gene loci, mutation types, and related adverse reaction information.
Input a series of queries about specific gene variants and drug adverse reactions into the AI conversation node. Check if the returned results accurately cite relevant content from the knowledge base and verify the presentation of the cited content template.
Simulate knowledge base search operations for users with different permission levels. Confirm that access control is effective and that authorized molecular diagnostics data can be correctly retrieved.
Monitor workflow log outputs to ensure no errors occur during large-batch data processing due to context length, parsing timeouts, or insufficient memory.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

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.