术后访问数据集
包含围手术期临床数据和肺部超声影像，用于机器学习预测术后肺部并发症。
基本信息
资源简介
术后访问数据集
【数据集背景】
本数据集源站标题为《Postoperative visit.》。源站首次发布：2025-08-19。
【数据内容】
源站原始描述：Background Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technical resources. Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorit…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 3.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0329076.t003。
【主题与分类】
源站学科分类：Medicine、Genetics、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：standardised lung sonography、learning assisted approach、imaging data collected、considered effective tie、become clinically manifest、postoperative days 1、perioperative routine data、xlink "> clinicaltrials、risk patients immediately、clinical cohort study。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Postoperative_visit_/29943981
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Trautwein, Britta; Beer, Meinrad; Blobner, Manfred; Jungwirth, Bettina; Kagerbauer, Simone Maria; Götz, Michael (2025). Postoperative visit.. PLOS ONE. 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：Postoperative visit.
- DOI：10.1371/journal.pone.0329076.t003
- 发布日期：2025-08-19
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Postoperative_visit_/29943981
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/57293151"
文件清单
Table 3.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
Background
Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technical resources. Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorithm.
Methods
This clinical cohort study will follow the TRIPOD statement for multivariable prediction model development. Development of the prognostic model will require 512 patients undergoing elective surgery under general anaesthesia. Besides the collection of perioperative routine data, standardised lung sonography will be performed postoperatively in the recovery room on each patient. During the postoperative course, patients will be examined in a structured manner on postoperative days 1,3 and 7 to detect POPC. The endpoints determined in this way, together with the clinical and imaging data collected, are then used to train a machine learning model based on neural networks and ensemble methods to predict POPC in the early postoperative phase.
Discussion
In the perioperative setting, detecting POPC before they become clinically manifest is desirable. This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.
Trial registration
ClinicalTrials.gov ID: NCT05789953 (29th of March 2023)
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0329076.t003（source: https://plos.figshare.com/articles/dataset/Postoperative_visit_/29943981）。
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。
