首页/数据集/Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome

Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome

NIAID Data Ecosystem2026-05-02 收录

死亡风险预测

医疗机器学习

数据链接：

https://zenodo.org/record/13376076 数据链接链接失效反馈

官方服务：

资源简介：

Raw data of the manuscript

应用场景：

创建时间：

2024-09-05

重症监护评分

死亡风险预测

Purpose MELD-XI, an adapted version of Model for End-stage Liver Disease (MELD) score excluding INR, was reported to predict outcomes e.g. in patients with acute heart failure. We aimed to evaluate ME

NIAID Data Ecosystem60

Machine learning identifies girls with central precocious puberty based on multi-source data

中枢性性早熟

医疗机器学习

Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnR

DataONE2021-01-05 更新50

Cutoff serum creatinine levels for prediction of death.

功能评估

死亡风险预测

Cutoff serum creatinine levels for prediction of death.

Figshare2019-03-28 更新20

Multiple logistic regression analyses to predict 30-day mortality.

死亡风险预测

医学统计建模

Multiple logistic regression analyses to predict 30-day mortality.

NIAID Data Ecosystem50

Self-rated health status and illiteracy as death predictors in a Brazilian cohort

死亡风险预测

队列流行病学

Cohort studies assessing predictive values of self-rated health (SRH) and illiteracy on mortality in low-to-middle income countries are missing in the literature. Aiming to determine if these two vari

Figshare2018-07-12 更新30

Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome

资源简介：

相关数据集