Data Extracted from Literature

Name: Data Extracted from Literature
Creator: Hennessy, Hannah
Published: 2025-10-17T00:00:00+08:00

Figshare2025-10-17 更新2026-04-08 收录

人工智能治理

医疗预测模型

数据链接：

https://figshare.com/articles/dataset/Data_Extracted_from_Literature/30382972/1 数据链接链接失效反馈

官方服务：

资源简介：

Data extracted for a scoping review on AI governance frameworks for prediction models in healthcare

提供机构：

Hennessy, Hannah

创建时间：

2025-10-17

Understanding key issues in current artificial intelligence development and governance

人工智能治理

AI可持续发展

At present, as global artificial intelligence advances vigorously, the industry is engaged in intense discussions about the future direction of its development and governance, where certain consensus

中国科学数据2026-05-07 更新50

Case breakdown of the model building and model testing data sets for all individuals included.

神经疾病诊断

医疗预测模型

Abbreviations: MS, multiple sclerosis; CIS, clinically isolated syndrome; iOND, other neurological diseases with inflammation; OND, other neurological diseases.

Figshare2015-12-02 更新20

Comparing the model performances when predicting the external adult and pediatric test sets.

医疗预测模型

人群特异性模型对比

Bold numerical values denote superior performance in their respective columns.

NIAID Data Ecosystem10

Data for: Development of a Predictive Model for Extubation Failure in weaning from mechanical ventilation: a retrospective cohort study.

医疗预测模型

机械通气撤机

This dataset was made at a University Hospital with 40 medical-surgical beds in the ICU. The Ethics Committee of the Hospital and School of medicine at Universidad de Antioquia approved the research p

doi.org40

Artificial intelligence forecasting mortality at an intensive care unit and comparison to a logistic regression system

死亡率预测

医疗预测模型

ABSTRACT Objective To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to

Figshare2021-05-01 更新10

Data Extracted from Literature

资源简介：

相关数据集