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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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使用多模式数据预测癌症患者的抑郁风险:算法开发研究

Anne de Hond1,2,3, Marieke van Buchem1,2,3, Claudio Fanconi3,4

  • 1Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Leiden, Netherlands.

JMIR medical informatics
|January 18, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测癌症患者在治疗早期的抑郁风险. 最好的模型将结构化数据与患者电子邮件相结合,显示出潜力,但需要进一步验证和偏差纠正.

关键词:
人工智能的人工智能是人工智能.癌症 癌症 癌症 癌症 癌症癌症护理 癌症护理癌症治疗 治疗 治疗 癌症关心的关心的关心的关心化疗 化疗是一种化学疗法.临床决策支持 临床决策支持决策支持提供了决策支持.抑郁 抑郁症 抑郁症 抑郁症 抑郁症抑郁症的风险 抑郁症的风险诊断 诊断 诊断 诊断 诊断 诊断机器学习是机器学习.心理健康 心理健康自然语言处理自然语言处理.瘤学 在瘤学方面.患有癌症的患者.预测模型 预测模型辐射疗法 辐射疗法验证验证的时间

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科学领域:

  • 在瘤学瘤学.
  • 精神病学是一个精神病学.
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 接受全身治疗的癌症患者经常经历抑郁症.
  • 早期识别有风险的个体对于及时干预至关重要.
  • 预测模型可以帮助医疗保健专业人员识别易受伤害的癌症患者.

研究的目的:

  • 开发和评估癌症患者在治疗的第一个月内对抑郁风险的预测模型.
  • 探索机器学习和自然语言处理对抑郁风险预测的有用性.

主要方法:

  • 利用了16159名癌症患者的电子健康记录数据和非结构化文本 (患者电子邮件,临床医生笔记).
  • 开发了使用最少绝对收缩和选择操作员 (LASSO) 物流回归和变压器双向编码器表示 (BERT) 的多式预测模型.
  • 使用接收器操作特征曲线 (AUROC) 下面面积,校准曲线和决策曲线分析评估模型性能.

主要成果:

  • 性能最好的模型,使用结构化数据和电子邮件分类得分的LASSO后勤回归,实现了0.74.7的AUROC.
  • 从变压器 (BERT) 的双向编码器表示模型显示中等性能 (AUROC ~0.71).
  • 仅基于临床医生的笔记或电子邮件分类得分的模型表现不佳;女性和黑人患者的风险被低估.

结论:

  • 机器学习和多式模式模型对预测癌症患者抑郁风险有前途.
  • 限制包括潜在的偏差和需要进一步改进和验证模型的需求.
  • 未来的研究应该专注于改进结果标签,预测指标和解决子组偏见.