诊断和连续变量（n=780）
基于780例临床数据的急性阑尾炎诊断与预后分类数据集，包含连续变量和标签。
基本信息
资源简介
诊断和连续变量（n=780）
【数据集背景】
本数据集源站标题为《Diagnosis and continuous variables (n = 780).》，为 Diagnosis and continuous variables (n = 780). 的组成部分。源站首次发布：2026-01-21。
【数据内容】
源站原始描述：Acute appendicitis is a common but diagnostically challenging surgical emergency in children. Existing linear scoring systems lack sufficient accuracy for standalone use, while advanced imaging is constrained by risks of sedation, contrast, and radiation. Furthermore, no available tools provide prognostic guidance. We introduce Dharma, a machine learning framework consisting of a clinically grounded imputer and two random forest classifiers for diagnosis and severity assessment. Designed for rea…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 7.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pdig.0000908.t007。
【主题与分类】
源站学科分类：Space Science、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified。主题标签：negative predictive value、clinically grounded imputer、84 – 100、identified complicated appendicitis、beyond pediatric appendicitis、world bedside use、93 – 100、offs enable dharma、div >< p、91 – 99。
【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/_p_Diagnosis_and_continuous_variables_n_780_p_/31118111
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Kshetri, Anup Thapa; Pahari, Subash; Timilsina, Shashank; Chapagain, Binay (2026). Diagnosis and continuous variables (n = 780).. PLOS Digital Health. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：<p>Diagnosis and continuous variables (n = 780).</p>
- DOI：10.1371/journal.pdig.0000908.t007
- 发布日期：2026-01-21
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_p_Diagnosis_and_continuous_variables_n_780_p_/31118111
命令行下载
curl -L -o "Table 7.xls" "https://ndownloader.figshare.com/files/61200638"
文件清单
Table 7.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Acute appendicitis is a common but diagnostically challenging surgical emergency in children. Existing linear scoring systems lack sufficient accuracy for standalone use, while advanced imaging is constrained by risks of sedation, contrast, and radiation. Furthermore, no available tools provide prognostic guidance. We introduce Dharma, a machine learning framework consisting of a clinically grounded imputer and two random forest classifiers for diagnosis and severity assessment. Designed for real-world bedside use, Dharma is open-sourced and accessible through a web application. Dharma achieved excellent diagnostic performance, with an AUC-ROC of 0.98 [0.97–0.99] and accuracy of 93% [91–95]. For prognostic classification, it identified complicated appendicitis with high sensitivity (96% [93–99]) and negative predictive value (97% [94–99]). Even in cases without appendix visualization—a frequent limitation in resource-constrained settings—Dharma maintained strong performance (AUC-ROC 0.96 [0.93–0.99]), with specificity of 97% [93–100] and PPV of 93% [84–100] at a 44% threshold, and sensitivity of 92% [84–98] with NPV of 95% [91–99] at a 25% threshold. These threshold-dependent trade-offs enable Dharma to support both ruling in and ruling out appendicitis within diverse clinical workflows. Beyond pediatric appendicitis, Dharma’s open-source framework and clinically grounded design also provide a generalizable foundation for developing equitable and practical decision-support systems in healthcare.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pdig.0000908.t007（source: https://plos.figshare.com/articles/dataset/_p_Diagnosis_and_continuous_variables_n_780_p_/31118111）。
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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