基于临床指标的敌草快中毒风险预测模型数据集
基于60名敌草快中毒患者的临床指标构建预后风险预测模型的数据集，涵盖基本特征、实验室检查和中毒严重程度。
基本信息
资源简介
基于临床指标的敌草快中毒风险预测模型数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，主题涵盖 Diquat、plasma concentration、poisoning severity index、risk prediction model、drug blood concentration，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《Construction of a risk prediction model of diquat poisoning based on clinical indicators》。配套论文《Construction of a risk prediction model of diquat poisoning based on clinical indicators》发表于 Critical Reviews in Toxicology（2024），DOI：10.1080/10408444.2024.2433242。项目资助方：This work was supported by the Chengdu Department of Science and Technology Major Science and Technology Application Project "Precision Diagnosis and Treatment System Construction and Regional Linkage Management Model Innovation and Application Demonstration based on High-content Poison Screening Technology" [No. 2022-YF09-00021-SN]; and Sichuan Natural Science Foundation Project [No. 2022NSFSC0669].。源站首次发布：2025-05-27。
【数据内容】
源站原始描述：This study aims to explore the clinical characteristics and prognostic factors in patients with diquat (DQ) poisoning and to develop a clinical risk assessment model to improve diagnosis and treatment strategies. Data from 60 patients with DQ poisoning, including basic characteristics, poisoning severity, and inflammatory response indicators, were collected. The plasma concentration of DQ was measured using liquid chromatography–mass spectrometry. The included patients were categorized into surv…
【数据结构与技术规格】
数据集 DOI：10.6084/m9.figshare.28045417.v2。
【主题与分类】
源站学科分类：Medicine、Cell Biology、Pharmacology、Biotechnology、Immunology、Biological Sciences not elsewhere classified。主题标签：Diquat、plasma concentration、poisoning severity index、risk prediction model、drug blood concentration。
【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://tandf.figshare.com/articles/online_resource/Construction_of_a_risk_prediction_model_of_diquat_poisoning_based_on_clinical_indicators/28045417
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Construction of a risk prediction model of diquat poisoning based on clinical indicators. Taylor & Francis. Qian, Weiwei; Xie, Xuxin; Zhou, Jian; Ren, Yan; Bai, Yangjuan; Peng, Aihua; et al
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。
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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。
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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：Construction of a risk prediction model of diquat poisoning based on clinical indicators
- DOI：10.6084/m9.figshare.28045417.v2
- 发布日期：2025-05-27
- 源站页面：https://tandf.figshare.com/articles/online_resource/Construction_of_a_risk_prediction_model_of_diquat_poisoning_based_on_clinical_indicators/28045417
按页面 Download 按钮获取。
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
This study aims to explore the clinical characteristics and prognostic factors in patients with diquat (DQ) poisoning and to develop a clinical risk assessment model to improve diagnosis and treatment strategies. Data from 60 patients with DQ poisoning, including basic characteristics, poisoning severity, and inflammatory response indicators, were collected. The plasma concentration of DQ was measured using liquid chromatography–mass spectrometry. The included patients were categorized into survival and death groups based on their 30-day outcomes. Fisher’s exact test was used to identify statistically significant clinical indicators (p p < .05) between the survival and death groups. The GLM-based risk assessment model demonstrated high predictive accuracy, with an area under the ROC curve (AUC) of 0.97 (SE 0.017, 95% CI 0.939–1.001), indicating potent prognostic capability. The Wilcoxon test revealed that ODD, Myo, SIDP, aspartate transferase (AST), creatine kinase (CK), hemoglobin (Hb), cardiac troponin (cTnT), and serum creatinine (Cr) levels were significantly higher in the high-risk group. The clinical risk assessment model effectively predicts the prognosis of patients with DQ poisoning, aiding clinicians in personalizing treatment strategies to improve patient outcomes.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.28045417.v2（source: https://tandf.figshare.com/articles/online_resource/Construction_of_a_risk_prediction_model_of_diquat_poisoning_based_on_clinical_indicators/28045417）。
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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