Clinical Text Analysis

Clinical entity detection, assertion status assignment, and relation extraction are essential in medical text analysis. These techniques enable healthcare professionals, researchers, and medical NLP practitioners to derive valuable insights from clinical literature, electronic health records, and patient notes, enhancing the understanding and management of patient data. This API uses state of the art medical models and is perfect for healthcare data analysts, clinical researchers, and healthcare AI application developers looking to extract detailed and actionable insights from unstructured clinical text.<br/>

<br/>Use the provided Streamlit playground application to test this service.

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**Key Features:**

- **Entity Recognition** Extracts a wide array of clinical entities using state of the art medical model.
- **Assertion Status**  Determines the assertion status of each entity and classifies it as hypothetical, past, planned, present, and more.
- **Relation Extraction** Links related entities, like drugs with their dosages and frequencies, test results with the tests, and more, which is crucial for building a connected data graph from disjointed text.<br/>

**Supported labels:**

- **Clinical Entity Labels** Includes categories like Age, Gender, Symptoms, Diseases, Medications, Vital Signs, and many others.
- **Assertion Status Labels** Categorizes entities into statuses such as Hypothetical, Past, Present, Planned, and others to provide context.
- **Relation Extraction Labels** Identifies relations such as is_finding_of, is_date_of, is_result_of, Drug_BrandName-Dosage, Drug_BrandName-Frequency, Drug_BrandName-Route , Drug_BrandName-Strength, Drug_Ingredient-Dosage, Drug_Ingredient-Frequency, Drug_Ingredient-Route, Drug_Ingredient-Strength.<br/><br/>

Benchmarking information :<br/>[Entity Extraction](https://nlp.johnsnowlabs.com/2022/10/19/ner_jsl_en.html#benchmarking)

[Assertion Status](https://nlp.johnsnowlabs.com/2021/07/24/assertion_jsl_en.html#benchmarking)

[Relation Extraction](https://nlp.johnsnowlabs.com/2021/02/24/re_test_result_date_en.html)

搜集汇总

背景与挑战

背景概述

该数据集提供先进的临床文本分析API，支持识别医疗实体、分类断言状态及提取实体间关系，适用于电子健康记录和临床文献的数据挖掘。其核心功能包括提取症状、药物等临床实体，判断实体状态（如过去、现在等），以及建立药物与剂量等关键关系。