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在文字健康记录中识别老年人的健康状况,使用基于深度学习的自然语言处理

Jake Lin^1,2, Anna Kuukka¹, Tomi Korpi¹

¹Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Computational and structural biotechnology journal

|January 7, 2026

概括

基于深度学习的自然语言处理有效地从电子健康记录中识别出未被报告的健康状况,如老年人的跌倒和失禁,从而改善患者对死亡风险的评估.

科学领域:

老年学是指老年学的学科.
医疗信息学医疗信息学
医疗保健中的人工智能

背景情况:

老年人临床上显著的健康状况通常在结构化的电子健康记录中未得到报告或错过.
医疗记录中的非结构化自由文本条目包含了用于患者护理和预后的宝贵数据.

研究的目的:

开发和评估基于深度学习的自然语言处理模型,用于识别老年人报告不足的疾病.
将自然语言处理模型的性能与传统诊断代码进行比较.
评估已识别的条件对所有原因死亡率的预测价值.

主要方法:

利用了芬兰102525名年龄在50-80岁的患者 (2010-2022) 电子健康记录中的1060万个自由文本条目.
采用基于深度学习的命名实体识别 (NER) 来识别跌倒,失禁,孤独和移动性限制.
将NER发现与诊断代码进行比较,并使用考克斯回归来分析死亡率预测.

主要成果:

在识别健康状况方面,NER模型取得了卓越的性能 (精度,回忆,F1>0.80).
与诊断代码相比,NER发现的跌倒和失禁病例显著更多.
使用NER识别的疾病与全因死亡率的关联比使用诊断代码的疾病更强.

结论:

基于深度学习的NER模型提供了一种强大的方法,可以从非结构化的电子健康记录中提取有价值的健康信息.
这种方法增强了易受伤害的老年人识别,并提高了预后准确性.
利用自由文本数据可以显著改善临床相关健康事件的捕获.

关键词:

电子健康记录是电子健康记录.布布布的布布的失禁失禁是一种失禁.孤独感孤独感孤独感行动的限制行动的限制自然语言处理自然语言处理.

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