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Converts unstructured clinical text into structured JSON by extracting key medical information such as symptoms, diagnoses, medications, procedures, and temporal data. Use when transforming clinical notes into machine-readable format.
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| name | clinical-data-structuring |
| description | Converts unstructured clinical text into structured JSON by extracting key medical information such as symptoms, diagnoses, medications, procedures, and temporal data. Use when transforming clinical notes into machine-readable format. |
When structuring clinical data, follow these steps:
Identify and extract relevant clinical elements:
Only extract explicitly mentioned information.
Group extracted information into the following categories:
symptomsdiagnosesmedicationsprocedureslab_resultstimelineIf a category has no data, return an empty list.
"time": "unknown"Return the result as JSON:
{
"symptoms": [],
"diagnoses": [],
"medications": [],
"procedures": [],
"lab_results": [],
"timeline": [
{
"time": "...",
"event": "..."
}
]
}
Classifies clinical text into predefined categories such as severity, urgency, or clinical type based on patient information. Use when assigning labels for triage, prioritization, or categorization of medical cases.
Extracts and organizes clinical events into a chronological timeline based on patient records or medical notes. Use when reconstructing the sequence of symptoms, diagnoses, treatments, or hospital events.
Generates structured clinical reports (e.g., discharge summaries, medical reports, or clinical notes) based on patient data. Use when transforming clinical information into clear, standardized medical documentation.
Answers questions based strictly on clinical text, such as patient records or medical notes. Use when extracting specific information from clinical documents in a reliable and grounded manner.
Assesses patient risk level (e.g., low, medium, high) based on clinical information such as symptoms, history, and findings. Use when evaluating potential severity, complications, or need for prioritization in clinical contexts.
Maps clinical text to standardized diagnosis codes (e.g., ICD-10) based on explicitly mentioned conditions. Use when extracting or assigning diagnostic codes from medical records, clinical notes, or patient summaries.