特征构成
包含从MIMIC-III提取的10311名心力衰竭患者的特征构成，用于预测短期和长期死亡率，并提供前15个重要特征。
基本信息
资源简介
特征构成
【数据集背景】
本数据集源站标题为《The composition of features.》。源站首次发布：2023-02-24。
【数据内容】
源站原始描述：Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid death and positively affect the health of patients. But in fact, machine learning models are difficult to gain good results on missing values, high dimensions, and imbalances HF data. Therefore, a deep learning system is proposed. In this system, we…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 5.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0276835.t005。
【主题与分类】
源站学科分类：Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：public database mimic、machine learning models、imbalanced problem better、gain good results、focal loss function、different kernel sizes、deep learning model、convolutional neural network、containing valid data、deep learning system。
【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/The_composition_of_features_/22158127
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Li, Dengao; Fu, Jian; Zhao, Jumin; Qin, Junnan; Zhang, Lihui (2023). The composition of features.. PLOS ONE. Dataset
【使用注意】
· 源站最后修改于 2023-06-21，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：The composition of features.
- DOI：10.1371/journal.pone.0276835.t005
- 发布日期：2023-02-24
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/The_composition_of_features_/22158127
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/39394411"
文件清单
Table 5.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid death and positively affect the health of patients. But in fact, machine learning models are difficult to gain good results on missing values, high dimensions, and imbalances HF data. Therefore, a deep learning system is proposed. In this system, we propose an indicator vector to indicate whether the value is true or be padded, which fast solves the missing values and helps expand data dimensions. Then, we use a convolutional neural network with different kernel sizes to obtain the features information. And a multi-head self-attention mechanism is applied to gain whole channel information, which is essential for the system to improve performance. Besides, the focal loss function is introduced to deal with the imbalanced problem better. The experimental data of the system are from the public database MIMIC-III, containing valid data for 10311 patients. The proposed system effectively and fast predicts four death types: death within 30 days, death within 180 days, death within 365 days and death after 365 days. Our study uses Deep SHAP to interpret the deep learning model and obtains the top 15 characteristics. These characteristics further confirm the effectiveness and rationality of the system and help provide a better medical service.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0276835.t005（source: https://plos.figshare.com/articles/dataset/The_composition_of_features_/22158127）。
数据缺失？
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