Multiturn Conversation and Prompts for Neurodegenerative

Data Characteristics for This Category

Neurodegenerative disease pharmacovigilance data primarily comes from clinical trial reports, real-world evidence (RWE), post-market surveillance reports (e.g., CIOMS I forms), medical literature, and patient self-reporting systems. This data updates frequently. Clinical trial data releases periodically with trial progress. Post-market surveillance data accumulates continuously and summarizes regularly. Document structures typically include patient demographics, medication history, adverse event descriptions (including onset time, severity, outcome), relevant test results, and healthcare professional assessments. Fields cover diagnostic codes (e.g., ICD-10), drug codes (e.g., ATC codes), adverse event terms (e.g., MedDRA codes), and medication details like dosage, frequency, and administration route. Units mainly involve time (days, months, years), dosage (milligrams, units), and frequency (times/day). Adverse event descriptions often contain extensive unstructured natural language.

Constraints from These Characteristics in "Multiturn Conversation and Prompts"

The complex disease progression and variable symptoms of neurodegenerative diseases make adverse event descriptions highly context-dependent. Multiturn conversations must accurately capture continuous descriptions of symptom evolution and medication adjustments from patients or healthcare professionals. The large volume of unstructured text data requires prompt design to balance extraction precision with robustness to vague descriptions. For example, early symptoms may be atypical, requiring multiple follow-up questions to clarify their drug-relatedness. Data format differences across various report sources challenge prompt generalization, requiring the ability to identify key information from different structured or semi-structured reports. The slow progression of these diseases means adverse events may appear only after long-term medication use. The conversation system needs to handle historical data correlation across timelines to avoid information silos.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000 tokens`	Ensures capacity for detailed descriptions of disease progression and medication history in multiturn conversations
`temperature`	`0.3`	Guarantees accuracy and consistency in information extraction, reducing hallucinations
`top_p`	`0.7`	Balances answer diversity with result reliability
`Chunk size`	`512 characters`	Accommodates text length of adverse event descriptions, improving recall efficiency
`Recall count`	`Top 10 entries`	Covers potentially relevant historical events and medical literature
`Similarity threshold`	`0.85`	Precisely matches specialized terminology unique to neurodegenerative diseases

Three Common Mistakes

The conversation frequently includes "thought content" or fails to extract information effectively. This happens when prompts do not effectively suppress the model's intermediate reasoning processes or do not clearly specify the required information type.
Historical record queries return records not belonging to the current session. This occurs when customUid is not correctly passed or effectively associated by the backend in the historical record query interface.
The system fails to recognize certain specialized medical terms or variations of disease symptoms. This happens when the knowledge base lacks coverage for polysemous words, synonyms, and diverse symptom descriptions related to neurodegenerative diseases.

How to Confirm Proper Configuration

Conduct multiple rounds of simulated conversations. Focus on whether the system can accurately track and integrate changes in patient symptoms, medication dosages, and timelines. Compare results against a predefined gold standard to determine the reasonableness of maxContext and temperature.
Initiate multiple sets of conversations using different customUid values. Then, query historical records and verify that the returned results are strictly limited to the sessions corresponding to each customUid. This validates the historical record association logic.
Input adverse event descriptions for neurodegenerative diseases using various forms of expression. Observe whether the system consistently extracts key information. Expert evaluation ensures that Similarity threshold and Recall count meet practical requirements.

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.