Data Characteristics in this Category
Pharmacovigilance data in molecular diagnostics originates from clinical trial reports, real-world studies, spontaneous adverse event reporting systems, and medical literature. This data typically combines structured and unstructured formats. Structured data includes patient demographics, diagnostic results, medication details, adverse event codes (e.g., MedDRA), and severity scores. Unstructured data encompasses detailed case descriptions, physician orders, laboratory test results, and imaging reports. Data updates frequently, especially during post-market surveillance, where new adverse event reports may arise daily. Document structures are complex, often involving PDF-formatted clinical study reports, HL7 CDA standard documents, and various free-text records. Fields and units are highly specialized, such as gene mutation sites, sequencing depth (X), Ct values, and specific biomarker concentrations (ng/mL or U/L).
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The highly specialized and complex nature of molecular diagnostic data challenges the accuracy and robustness of multi-turn conversations. Extracting key information from unstructured documents requires more refined text parsing capabilities to identify and associate molecular diagnostic results with adverse events. High update frequency demands that the knowledge base quickly synchronizes with the latest data, ensuring the timeliness of conversation content. For example, when a user asks about the association between a specific gene mutation and an adverse reaction, the system must access the latest clinical evidence. Multi-turn conversations require contextual understanding to handle user questions that progressively refine, such as shifting from "risk of a certain test result" to "adverse reactions of a specific drug related to that test result." Prompt design must accurately consider professional terminology to avoid misjudgments due to ambiguous terms. It must precisely locate information within complex data structures and present it in an easily understandable way for the user.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
maxContext | 6 | Ensures sufficient context is maintained in multi-turn conversations to understand complex molecular diagnostic questions. |
Chunk size (Segment Length) | 800-1000 characters (characters) | Accommodates the long sentences and detailed descriptions typical of molecular diagnostic reports, preventing important information from being truncated. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Improves the precision of information recall, reducing interference from irrelevant or low-relevance documents, especially for specialized terminology. |
Recall count (Number of Retrieved Items) | Top 5 entries (top 5) | Balances recall efficiency with relevance, ensuring coverage of multiple potential molecular diagnostic or pharmacovigilance-related documents. |
Rerank result count (Number of Reranked Items) | Top 3 entries (top 3) | After optimization by the reranking model, prioritizes displaying the most relevant key information to the user's query. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds (seconds) | Accounts for the time required to parse large clinical reports or sequencing result files, preventing parsing failures due to timeouts. |
Three Common Mistakes
- During a multi-turn conversation, if a user introduces a new molecular diagnostic-related question, the system fails to correctly switch topics or loses the original context, leading to off-topic answers or repetitive questioning. This occurs because
maxContextis set too low or the context management logic is incomplete. - Uploaded molecular diagnostic report files consistently fail to parse, and backend logs show file parsing timeouts or format errors. This happens because
PARSE_FILE_TIMEOUT_SECONDSis insufficient to handle large or complex report formats, or specific file types (e.g., HL7 CDA) are not pre-processed. - AI responses contain a large amount of irrelevant or low-relevance information, failing to precisely locate the specific gene mutation or adverse event the user is asking about. This is due to
Similarity threshold(Similarity Threshold) being set too low, leading to the recall of many generic documents.
How to Confirm Proper Configuration
- Select real user questions covering different molecular diagnostic types and adverse reaction symptoms. Conduct multi-turn conversation tests to verify the system's ability to accurately understand and maintain context.
- Upload molecular diagnostic reports in various formats and sizes (e.g., PDF, TXT, CDA files). Check if all can be successfully parsed and their content referenced in conversations.
- For specific molecular diagnostic indicators (e.g., a certain gene mutation) or adverse reactions to specific drugs, ask questions to verify if the information recalled by the system is precise, complete, and can differentiate varying degrees of association.
The values provided are common starting points and should be measured 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.