基于常规实验室检验结果的多发性骨髓瘤早期诊断数据集（表4）

数据集获取（Figshare）

• 源站标题：Table_4_Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma.docx
• DOI：10.3389/fonc.2021.608191.s007
• 发布日期：2021-03-29
• 文件数：1 个
• 文件总体积：16.81 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Employment_of_Artificial_Intelligence_Based_on_Routine_Laboratory_Results_for_the_Early_Diagnosis_of_Multiple_Myeloma_docx/14332256

命令行下载

curl -L -o "Table_4_Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma.docx" "https://ndownloader.figshare.com/files/27338015"

文件清单

• Table_4_Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma.docx（16.81 KB）

文件总体积 16.81 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

ObjectiveIn order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed.
Methods4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve.
ResultsBy designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF.
ConclusionThe model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2021.608191.s007（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Employment_of_Artificial_Intelligence_Based_on_Routine_Laboratory_Results_for_the_Early_Diagnosis_of_Multiple_Myeloma_docx/14332256）。