Clinical Assertion Negation Bert

🚀 临床断言/否定分类BERT

本模型可将临床患者信件中提及的医疗状况分类为“存在”、“不存在”和“可能存在”，有助于构建临床患者信件中的信息结构。

🚀 快速开始

临床断言和否定分类BERT模型在论文Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?中被提出。该模型基于Alsentzer等人的ClinicalBERT - Bio + Discharge Summary BERT Model，并在2010 i2b2挑战赛的断言数据上进行了微调。

✨ 主要特性

该模型能够将临床患者信件中提到的医疗状况分类为“存在”、“不存在”和“可能存在”，从而帮助结构化临床患者信件中的信息。

📦 安装指南

你可以通过transformers库加载该模型：

from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")

💻 使用示例

基础用法

该模型期望输入的格式为带有一个标记实体的跨度/句子，以将其分类为PRESENT(0)、ABSENT(1)或POSSIBLE(2)。相关实体通过特殊标记[entity]来标识。

示例输入和推理：

input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."

classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)

classification = classifier(input)
# [{'label': 'ABSENT', 'score': 0.9842607378959656}]

📚 详细文档

引用

在使用该模型时，请按如下方式引用我们的论文：

@inproceedings{van-aken-2021-assertion,
    title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?",
    author = "van Aken, Betty  and
      Trajanovska, Ivana  and
      Siu, Amy  and
      Mayrdorfer, Manuel  and
      Budde, Klemens  and
      Loeser, Alexander",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.nlpmc-1.5",
    doi = "10.18653/v1/2021.nlpmc-1.5"
}