生存模型性能
基于肝移植案例的生存模型性能评估数据集，包含CASE框架改进的预测指标。
基本信息
资源简介
生存模型性能
【数据集背景】
本数据集源站标题为《Survival model performance.》。源站首次发布：2025-01-17。
【数据内容】
源站原始描述：Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictio…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0315928.t002。
【主题与分类】
源站学科分类：Medicine、Cell Biology、Biotechnology、Ecology、Immunology、Biological Sciences not elsewhere classified。主题标签：guide medical decisions、rsf survival models、augmented survival estimation、case additionally provides、provide accurate long、level survival curve、improve class imbalance、term survival prediction、term predictions due、term prediction。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Survival_model_performance_/28232642
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Shourabizadeh, Hamed; Aleman, Dionne M.; Rousseau, Louis-Martin; Zheng, Katina; Bhat, Mamatha (2025). Survival model performance.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Survival model performance.
- DOI：10.1371/journal.pone.0315928.t002
- 发布日期：2025-01-17
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Survival_model_performance_/28232642
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/51766517"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a classification task that ultimately yields survival curves, beginning with dataset augmentation to improve class imbalance for use with any classification model. Unlike other approaches, CASE additionally provides an exact survival time prediction. We demonstrate CASE on a liver transplant case study to predict >20 years survival post-transplant, finding that CASE dataset augmentation improved AUCs from 0.69 to 0.88 and F1 scores from 0.32 to 0.73. Compared to Kaplan-Meier, Cox, and RSF survival models, the CASE framework demonstrated better performance across various existing survival metrics, as well as our novel metric, mean of individual areas under the survival curve (mAUSC). Further, we develop novel temporal feature importance methods to understand how different features may vary in survival importance over time, potentially providing actionable insights in real-world survival problems.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0315928.t002（source: https://plos.figshare.com/articles/dataset/Survival_model_performance_/28232642）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
