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Extracts structured clinical entities such as diseases, symptoms, medications, procedures, and lab results from clinical text. Use when analyzing medical notes, patient records, or extracting structured information from unstructured healthcare data.
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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.
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.
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.
SOC 職業分類に基づく
| name | clinical-entity-extraction |
| description | Extracts structured clinical entities such as diseases, symptoms, medications, procedures, and lab results from clinical text. Use when analyzing medical notes, patient records, or extracting structured information from unstructured healthcare data. |
When extracting clinical entities, follow these steps:
Extract clinically meaningful entities, including:
Only extract entities that are explicitly mentioned in the text.
Do not infer or hallucinate information.
Assign each entity one of the following types:
If an entity does not clearly belong to one of these categories, do not include it.
Return the result as a JSON object:
{
"entities": [
{
"text": "...",
"type": "..."
}
]
}