痴呆症风险预测使用医疗笔记中的决策重点内容选择.
Shengyang Li1, Paul Dexter2, Zina Ben-Miled3
1Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, 46202, USA.
一种新的以决策为中心的方法有效地选择相关的患者笔记句子,用于使用Longformer语言模型预测痴呆风险. 这种方法通过保留上下文来提高准确性,优于旧方法.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 通用语言模型 (LMs) 在文本分析方面表现有前途,但需要适应医疗领域.
- 电子健康记录 (EHR) 在医疗笔记中包含有价值的患者病史,但LM输入长度限制需要内容选择.
- 现有的方法,如截断或总结,可能会导致信息丢失,冗余或无关紧要.
研究的目的:
- 提出一种新的以决策为中心的医疗笔记内容选择技术.
- 开发一个痴呆风险预测模型,利用这种技术和Longformer LM架构.
- 评估模型的性能与现有的内容选择方法对比.
主要方法:
- 开发了一种以决策为中心的内容选择方法,以确定在特定观察期内与目标结果相关的句子.
- 选择的内容被用来训练基于Longformer LM架构的痴呆风险预测模型.
- 模型的性能使用曲线下的面积 (AUC) 度量来评估.
主要成果:
- 拟议的框架实现了78.43的AUC,摘要限制在1024个令牌.
- 这一表现超过了之前提出的内容选择技术.
- 该模型准确地估计了痴呆症风险,使用仅一年的医疗笔记来预测一年的预测时间.
结论:
- 这种以决策为重点的内容选择技术有效地解决了处理LM的长长医疗笔记所面临的挑战.
- 开发的痴呆风险预测模型表现出卓越的性能,并保留了上下文信息,与表格方法不同.
- 这种方法绕过了LM计算复杂性的局限性,并使原始文本的功能工程成为可能.
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