高原红细胞增多症机器学习早期预警评分系统开发与验证：表1
该数据集为高原红细胞增多症机器学习早期预警评分系统研究的表1文档，基于高海拔队列生活方式变量开发验证风险评分。
基本信息
资源简介
高原红细胞增多症机器学习早期预警评分系统开发与验证：表1
【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 16.97 KB，主题涵盖 early warning system (EWS)、high altitude polycythemia、lifestyle risk factors、machine learning (ML)、random forest，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《Table 1_Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.docx》。配套论文《High altitude polycythemia and its maladaptive mechanisms: an updated review》发表于 Frontiers in Medicine（2024），DOI：10.3389/fmed.2024.1448654。源站首次发布：2026-01-21。
【数据内容】
源站原始描述：BackgroundHigh-altitude polycythemia (HAPC) lacks a lifestyle-focused risk-stratification tool among lifelong high-altitude residents. Here we aimed to develop and validate a novel machine-learning predictive scoring system for HAPC using readily modifiable lifestyle variables in this population. MethodsIn a high altitude cohort (≥4,500 m, n = 1,089), 82 candidate variables were reduced to seven lifestyle predictors via LASSO, Logistic regression, XGBoost and random forest models were trained an…
【数据结构与技术规格】
文件数 1 个，合计 16.97 KB。Table 1_Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，16.97 KB。数据集 DOI：10.3389/fpubh.2025.1739909.s004。
【主题与分类】
源站学科分类：Public Health and Health Services not elsewhere classified。主题标签：early warning system (EWS)、high altitude polycythemia、lifestyle risk factors、machine learning (ML)、random forest。
【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://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_based_early_warning_scoring_system_for_high_altitude_polycythemia_docx/31108690
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Suona, Yangzong; Danzeng, Zhuoga; Gesang, Luobu; Zhuoma, Panduo; Baima, Yangjin; Pubu, Zhuoma; et al. (2026). Table 1_Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.docx. Frontiers. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Table 1_Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.docx
- DOI：10.3389/fpubh.2025.1739909.s004
- 发布日期：2026-01-21
- 文件数：1 个
- 文件总体积：16.97 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_based_early_warning_scoring_system_for_high_altitude_polycythemia_docx/31108690
命令行下载
curl -L -o "Table 1_Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.docx" "https://ndownloader.figshare.com/files/61172320"
文件清单
Table 1_Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.docx（16.97 KB）
文件总体积 16.97 KB（源站 API 实测）。
数据说明
BackgroundHigh-altitude polycythemia (HAPC) lacks a lifestyle-focused risk-stratification tool among lifelong high-altitude residents. Here we aimed to develop and validate a novel machine-learning predictive scoring system for HAPC using readily modifiable lifestyle variables in this population.
MethodsIn a high altitude cohort (≥4,500 m, n = 1,089), 82 candidate variables were reduced to seven lifestyle predictors via LASSO, Logistic regression, XGBoost and random forest models were trained and compared (10 fold cross validation).
ResultsLogistic regression achieved the best balance (AUC 0.848, sensitivity 0.81, specificity 0.79). Low SpO2 (This score equips frontline health workers in extremely high-altitude, resource-scarce settings to rapidly pinpoint high-risk residents and initiate low-cost lifestyle interventions, thereby curbing the incidence of chronic altitude-related illnesses, easing local medical burdens, and improving overall quality of life for native high-altitude populations.
Trial RegistrationChiCTR2100047945.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fpubh.2025.1739909.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_based_early_warning_scoring_system_for_high_altitude_polycythemia_docx/31108690）。
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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