从住院记录到监测：使用本地患者画像描述印度韦洛尔的霍乱特征

数据集获取（Figshare）

• 源站标题：From hospitalization records to surveillance: The use of local patient profiles to characterize cholera in Vellore, India
• DOI：10.1371/journal.pone.0182642
• 发布日期：2017-08-18
• 文件数：5 个
• 文件总体积：524.76 KB
• 源站页面：https://plos.figshare.com/articles/dataset/From_hospitalization_records_to_surveillance_The_use_of_local_patient_profiles_to_characterize_cholera_in_Vellore_India/5325784

命令行下载

curl -L -o "S1 Dataset.xlsx" "https://ndownloader.figshare.com/files/9139930"
curl -L -o "S1 Fig.pdf" "https://ndownloader.figshare.com/files/9139936"
curl -L -o "S2 Fig.pdf" "https://ndownloader.figshare.com/files/9139951"
curl -L -o "S1 Table.pdf" "https://ndownloader.figshare.com/files/9139954"
curl -L -o "S2 Table.pdf" "https://ndownloader.figshare.com/files/9139960"

文件清单

• S1 Dataset.xlsx（184.28 KB）
• S1 Fig.pdf（112.61 KB）
• S2 Fig.pdf（148.16 KB）
• S1 Table.pdf（14.43 KB）
• S2 Table.pdf（65.28 KB）

文件总体积 524.76 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

Despite availability of high quality medical records, health care systems often do not have the resources or tools to utilize these data efficiently. Yet, hospital-based, laboratory-confirmed records may pave the way for building reliable surveillance systems capable of monitoring temporal trends of emerging infections. In this communication, we present a new tool to compress and visualize medical records with a local population profile (LPP) approach, which transforms information into statistically comparable patterns. We provide a step-by-step tutorial on how to build, interpret, and expand the use of LPP using hospitalization records of laboratory-confirmed cholera. We abstracted case information from the databases maintained by the Department of Clinical Microbiology at Christian Medical College in Vellore, India. We used a single-year age distribution to construct LPPs for O1, O139, and non O1/O139 serotypes of Vibrio cholerae. Disease counts and hospitalization rates were converted into fitted kernel-based probability densities. We formally compared LPPs with the Kolmogorov-Smirnov test, and created multi-panel visuals to depict temporal trend, age distribution, and hospitalization rates simultaneously. Our first implementation of LPPs revealed information that is typically gathered from surveillance systems such as: i) estimates of the demographic distribution of diseases and identification of a population at risk, ii) changes in the dominant pathogen presence; and iii) trends in disease occurrence. The LPP demonstrated the benefit of increased resolution in pattern detection of disease for different Vibrio cholerae serotypes and two demographic categories by showing patterns and anomalies that would be obscured by traditional methods of analysis and visualization. LPP can be used effectively to compile basic patient information such as age, sex, diagnosis, location, and time into compact visuals. Future development of the proposed approach will allow public health researchers and practitioners to broadly utilize and efficiently compress large volumes of medical records without loss of information.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0182642（source: https://plos.figshare.com/articles/dataset/From_hospitalization_records_to_surveillance_The_use_of_local_patient_profiles_to_characterize_cholera_in_Vellore_India/5325784）。