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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Updated: Jan 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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在自动ICD预测中使用医学命名实体识别.

Mohamad Kawas¹, Bassel Alkhatib², Khaled Omar²

¹Web Science Program, Syrian Virtual University, Damascus, Syria, svuonline.org.

BioMed research international

|October 1, 2025

概括

这项研究引入了一种高效的算法,用于从患者索赔中自动预测国际疾病分类 (ICD) 代码. 通过利用医疗命名实体识别 (NER) 和先进的BERT模型,它可以实现高精度,而不需要广泛的培训数据.

关键词:

贝尔特医疗 NERER 医疗临床BERT 临床BERT 临床BERT 自动ICD编码自动化ICD编码

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科学领域:

医疗保健中的人工智能
自然语言处理 (NLP) 用于医疗编码
机器学习在临床数据分析中的应用

背景情况:

国际疾病分类 (ICD) 是医学编码和健康统计的关键标准.
现有的自动ICD编码系统经常忽视原始文本输入中的关键医疗实体.
需要更复杂的算法,包括医疗实体识别,以准确预测ICD.

研究的目的:

建议和评估一种新的算法,用于直接从患者索赔中自动预测ICD代码.
通过整合医疗命名实体识别 (NER) 和先进的嵌入技术,提高ICD编码的准确性.
开发一个高效的ICD预测系统,需要最小的培训数据.

主要方法:

利用基于BERT的医疗命名实体识别 (NER) 模型来识别患者索赔中的关键医疗实体.
使用ClinicalBERT模型为提取的医疗实体生成嵌入.
创建了ICD目录的嵌入,专注于长描述,并计算了等号相似性来预测最相关的ICD代码.

主要成果:

拟议的算法成功识别了关键的医疗实体,并预测了相关的ICD代码.
该系统表现出高效率,不需要大型数据集进行培训.
医学数据集的实验结果显示,准确率约为90%.

结论:

开发的算法通过结合医疗实体识别,在自动ICD编码中提供了显著的改进.
这种方法提供了一种更准确和更有效的方法,用于从患者索赔中预测ICD代码.
该算法在有限的训练数据下表现良好的能力使其成为医疗保健应用中的实用解决方案.