基于PermFIT-DNN的混淆矩阵
基于PermFIT-DNN方法生成的肺栓塞混淆矩阵数据集，用于识别关键临床特征并辅助风险预测。
基本信息
资源简介
基于PermFIT-DNN的混淆矩阵
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 symptoms like dyspnea、may present asymptomatically、deep neural network、many factors influencing、effective framework using，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Confusion matrix via PermFIT-DNN.》。源站首次发布：2023-09-28。
【数据内容】
源站原始描述：A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE de…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 4.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0292185.t004。
【主题与分类】
源站学科分类：Space Science、Medicine、Cell Biology、Pharmacology、Environmental Sciences not elsewhere classified、Immunology。主题标签：symptoms like dyspnea、may present asymptomatically、deep neural network、many factors influencing、effective framework using、prevent misdiagnosing pe、classifying pe status、important features identified、analysis results show、asthma exacerbation patients。
【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/Confusion_matrix_via_PermFIT-DNN_/24213510
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Zou, Baiming; Zou, Fei; Cai, Jianwen (2023). Confusion matrix via PermFIT-DNN.. PLOS ONE. Dataset
下载信息
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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：Confusion matrix via PermFIT-DNN.
- DOI：10.1371/journal.pone.0292185.t004
- 发布日期：2023-09-28
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Confusion_matrix_via_PermFIT-DNN_/24213510
命令行下载
curl -L -o "Table 4.xls" "https://ndownloader.figshare.com/files/42498939"
文件清单
Table 4.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0292185.t004（source: https://plos.figshare.com/articles/dataset/Confusion_matrix_via_PermFIT-DNN_/24213510）。
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。
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