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Updated: Jan 15, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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可解释的机器学习框架,用于动态监测疾病预后风险:回顾性队列研究

Tetsuo Ishikawa1,2,3,4,5,6, Masahiro Shinoda4, Megumi Oya1,3,4,6

  • 1Predictive Medicine Special Project, RIKEN Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan, 81 45-503-7000.

JMIR formative research
|October 14, 2025
PubMed
概括

这项研究为COVID-19患者开发了一个动态风险评估框架,改善了在住院期间早期检测死亡风险. 可解释模型提供及时,可解释的警告,以支持临床干预和资源分配.

关键词:
在 COVID-19 疫情中,临床决策支持 临床决策支持电子健康记录是电子健康记录.可解释的人工智能AI预测模型的预测模型.随机生存森林的森林.时间变化的生物标志物.

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