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以自然语言处理驱动的人工智能模型用于诊断带有L5和S1根性病变的腰椎间板:初步评估

PeiYang Wang¹, Zhe Zhang², ZhiYang Xie¹

¹Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.

World neurosurgery

|June 15, 2024

概括

使用自然语言处理的人工智能 (AI) 模型可以有效地从电子健康记录 (EHR) 中诊断腰椎间盘 (LDH) 与L5和S1根性病变. 长期短期记忆模型在识别神经根压缩方面表现出很高的准确性.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
自然语言处理自然语言处理.

背景情况:

腰椎间盘 (LDH) 与L5和S1根细胞病变是一种常见的疾病,需要准确的诊断.
电子健康记录 (EHR) 包含有价值的临床信息,但需要先进的数据提取和分析方法.
开发自动诊断工具可以提高临床实践的效率和准确性.

研究的目的:

开发和验证基于自然语言处理 (NLP) 的人工智能 (AI) 模型,用于诊断带有 L5 和 S1 根性病变的腰椎间盘 (LDH).
为了利用电子健康记录 (EHR) 进行自动化疾病诊断.
评估机器学习模型在识别神经根压缩方面的性能.

主要方法:

收集了1681名患者的EHR数据,这些患者在2013年至2021年期间接受了LDH的腰椎切除术.
使用"目前疾病史"和"积极症状"文本创建数据集,专注于L5或S1根性病变.
开发和评估了两个机器学习模型:长期短期记忆 (LSTM) 网络和极端梯度增强 (XGBoost) 使用 7:3 列车测试分割.

主要成果:

使用积极症状文本的LSTM模型实现了最高的性能.
关键性能指标包括精度 (0.9054),回忆 (0.9405),准确度 (0.8950),F1得分 (0.9226) 和曲线下面积 (0.9485).
该模型在测试组中表现出强大的歧视能力.

结论:

初步验证支持使用NLP驱动的人工智能模型来从EHR诊断腰部疾病.
这种方法显示出开发更全面,更具临床影响力的AI诊断系统的潜力.
进一步的研究可以完善这些模型,使其具有更广泛的临床应用.

关键词:

人工智能的人工智能是人工智能.腰椎椎间盘的发生机器学习机器学习自然语言处理自然语言处理.根基病变是一种根基病变.

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