隔离森林投票融合多输出:一种基于异常样本检测多维输出的中风风险分类方法
Hai He1, Haibo Yang2, Francesco Mercaldo3
1School of Big Data and Information Industry, Chongqing City Management College, Chongqing 401331, China.
Computer methods and programs in biomedicine
|June 4, 2024
概括
这项研究引入了使用电子医疗记录预测中风风险的新算法,达到79.59%的准确性. 该模型通过识别风险水平和预测中风类型来增强中风查.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 卒中是全球领先的健康威胁,发病率,死亡率和复发率高.
- 目前基于电子医疗记录 (EMR) 的中风查在准确性和风险水平识别方面面临挑战,原因是系统错误和数据收集问题.
- 评估指标的主观性和潜在的错误报告进一步复杂化了精确的中风风险评估.
研究的目的:
- 开发一种先进的算法,以使用EMR数据改进中风查和风险预测.
- 通过采用一种新的计算方法来解决当前中风风险评估方法的局限性.
- 为医疗保健专业人员提供多维辅助决策信息.
主要方法:
- 开发了一种新的隔离森林投票融合多输出算法,并应用于处理和规范化查数据.
- 综合特征评分指数被用来分析各种中风风险因素的重要性.
- 该算法识别异常样本并进行分类,输出风险因子的重要性,异常样本标签,风险水平和中风预测.
主要成果:
- 拟议的算法将中风风险分为五个级别:零,低,高,暂时性缺血性发作 (TIA) 和出血性中风 (HE).
- 使用该模型预测中风的平均准确率达到79.59%.
- 该模型提供了多维输出,包括风险因素的重要性,异常样本识别,风险分层和中风预测.
结论:
- 隔离森林投票融合多输出算法有效地提高了中风风险水平的识别和预测准确度.
- 该模型能够输出多维辅助信息,这有助于医务人员在临床决策中.
- 这种方法显著提高了中风查过程的效率和准确性.
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