数据表格列表
尼日利亚HIV感染者电子病历表格数据，用于预测治疗中断风险。
基本信息
资源简介
数据表格列表
【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 potential predictor variables、2 %, specificity、demographic features played、cross river state、address data biases，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《List of data tables.》。源站首次发布：2025-04-24。
【数据内容】
源站原始描述：Nigeria, with the second-largest HIV epidemic globally, faces challenges in achieving its HIV epidemic control goals by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. This paper aims to identify predictors and measure the performance of mode…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pgph.0004497.t001。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer、Science Policy。主题标签：potential predictor variables、2 %, specificity、demographic features played、cross river state、address data biases、study analyzed data、overall iit rate、optimal resource allocation、adaboost model demonstrated、7 %, ranging。
【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/List_of_data_tables_/28860904
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Ijaiya, Mukhtar; Troncoso, Erica; Mutloatse, Marang; Ifeanyi, Duruanyanwu; Obasa, Benjamin; Emerenini, Franklin; et al. (2025). List of data tables.. PLOS Global Public Health. 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：List of data tables.
- DOI：10.1371/journal.pgph.0004497.t001
- 发布日期：2025-04-24
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/List_of_data_tables_/28860904
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/53962756"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Nigeria, with the second-largest HIV epidemic globally, faces challenges in achieving its HIV epidemic control goals by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. This paper aims to identify predictors and measure the performance of models used to predict the risk of IIT among People Living with HIV (PLHIV) on antiretroviral therapy (ART). We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. The study analyzed data from 41,394 PLHIV, with 266,520 observations receiving treatment across four Nigerian states. The overall IIT rate was 33.7%, ranging from 17.7% in Cross River State to 42.4% in Niger State. The AdaBoost model demonstrated the best performance, with a sensitivity of 69.2%, specificity of 82.3%, F1 score of 0.678, and PR-AUC and ROC-AUC values of 0.563 and 0.843, respectively. Key predictors included PLHIV prior behavior, visit history, and geographic factors, while demographic features played a lesser role. This study highlights the utility of ML, particularly the AdaBoost model, in stratifying PLHIV by the risk of IIT. By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. However, further research is needed to address data biases and contextual challenges in resource-constrained settings.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pgph.0004497.t001（source: https://plos.figshare.com/articles/dataset/List_of_data_tables_/28860904）。
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。
