住院患者多病共存模式的影响：哮喘案例研究

数据集获取（Figshare）

• 源站标题：Impact of multimorbidity patterns in hospital admissions: the case study of asthma
• DOI：10.6084/m9.figshare.22674985.v1
• 发布日期：2023-04-21
• 文件数：2 个
• 文件总体积：203.25 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Impact_of_multimorbidity_patterns_in_hospital_admissions_the_case_study_of_asthma/22674985

命令行下载

curl -L -o "ijas_a_2185154_sm0430.docx" "https://ndownloader.figshare.com/files/40244799"
curl -L -o "ijas_a_2185154_sm0428.docx" "https://ndownloader.figshare.com/files/40244803"

文件清单

• ijas_a_2185154_sm0430.docx（37.61 KB）
• ijas_a_2185154_sm0428.docx（165.64 KB）

文件总体积 203.25 KB（源站 API 实测）。

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

Background: Most previous studies assessing multimorbidity in asthma assessed the frequency of individual comorbid diseases. Objective: We aimed to assess the frequency and clinical and economic impact of co-occurring groups of comorbidities (comorbidity patterns using the Charlson Comorbidity Index) on asthma hospitalizations. Methods: We assessed the dataset containing a registration of all Portuguese hospitalizations between 2011–2015. We applied three different approaches (regression models, association rule mining, and decision trees) to assess both the frequency and impact of comorbidities patterns in the length-of-stay, in-hospital mortality and hospital charges. For each approach, separate analyses were performed for episodes with asthma as main and as secondary diagnosis. Separate analyses were performed by participants’ age group. Results: We assessed 198340 hospitalizations in patients >18 years old. Both in hospitalizations with asthma as main or secondary diagnosis, combinations of diseases involving cancer, metastasis, cerebrovascular disease, hemiplegia/paraplegia, and liver disease displayed a relevant clinical and economic burden. In hospitalizations having asthma as a secondary diagnosis, we identified several comorbidity patterns involving asthma and associated with increased length-of-stay (average impact of 1.3 [95%CI=0.6–2.0]–3.2 [95%CI=1.8-4.6] additional days), in-hospital mortality (OR range=1.4 [95%CI=1.0–2.0]–7.9 [95%CI=2.6–23.5]) and hospital charges (average additional charges of 351.0 [95%CI=219.1–482.8] to 1470.8 [95%CI=1004.6–1937.0]) Euro compared with hospitalizations without any registered Charlson comorbidity). Consistent results were observed with association rules mining and decision tree approaches. Conclusions: Our findings highlight the importance not only of a complete assessment of patients with asthma, but also of considering the presence of asthma in patients admitted by other diseases, as it may have a relevant impact on clinical and health services outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.22674985.v1（source: https://tandf.figshare.com/articles/dataset/Impact_of_multimorbidity_patterns_in_hospital_admissions_the_case_study_of_asthma/22674985）。