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使用机器学习技术在1780名患者中预测中风后不良心理结果的可解释风险

Chien Wei Oei^1,2, Eddie Yin Kwee Ng², Matthew Hok Shan Ng³

¹Management Information Department, Office of Clinical Epidemiology, Analytics and kNowledge (OCEAN), Tan Tock Seng Hospital, Singapore 308433, Singapore.

Sensors (Basel, Switzerland)

|September 28, 2023

概括

机器学习准确地预测中风后不良心理结果 (PSAMO),如抑郁和焦虑. 早期识别有风险的患者可能会允许进行干预,以改善中风幸存者的健康结果.

科学领域:

神经学神经学
精神病学是一个精神病学.
人工智能的人工智能

背景情况:

脑卒中后的抑郁和焦虑,称为脑卒中后不良心理结果 (PSAMO),影响大约30%和20%的中风幸存者,分别.
在中风幸存者中,PSAMO与较差的健康结果,增加的死亡率和更大的功能障碍有关.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测中风患者PSAMO风险.
通过可解释AI (XAI) 方法识别PSAMO的关键预测特征.

主要方法:

对1780名中风患者的回顾性研究,分为PSAMO和非PSAMO组.
利用了人口统计,生活质量,中风相关,病史和并发症数据.
采用递归特征消除,八个ML算法,贝叶斯优化用于超参数调整,以及SHAP用于模型解释.

主要成果:

梯度增强树模型以74.7%的二进制分类准确度实现了最高的性能.
SHAP分析确定了与现有临床发现一致的关键预测特征.
一些已识别的风险因素是可修改的,这表明早期干预的潜力.

结论:

ML模型可以有效地预测中风幸存者的PSAMO风险.
功能重要性分析突出了针对性干预的可修改因素.
对PSAMO的早期鉴定和干预可以改善中风幸存者的长期健康结果.

关键词:

人工智能的人工智能是人工智能.自动化风险预测和预测机器学习是机器学习.在中风后的不良心理结果.

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