表2_医学信息学平台（MIP）：跨意大利临床队列的试点研究.xlsx

数据集获取（Figshare）

• 源站标题：Table_2_Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.xlsx
• DOI：10.3389/fneur.2020.01021.s006
• 发布日期：2020-09-23
• 文件数：1 个
• 文件总体积：16.43 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Medical_Informatics_Platform_MIP_A_Pilot_Study_Across_Clinical_Italian_Cohorts_xlsx/12992819

命令行下载

curl -L -o "Table_2_Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.xlsx" "https://ndownloader.figshare.com/files/24755444"

文件清单

• Table_2_Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts.xlsx（16.43 KB）

文件总体积 16.43 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

Introduction: With the shift of research focus to personalized medicine in Alzheimer’‘s Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient’‘s risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup.
Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools.
Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians’’ DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases.
Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-u

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2020.01021.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Medical_Informatics_Platform_MIP_A_Pilot_Study_Across_Clinical_Italian_Cohorts_xlsx/12992819）。