急性心力衰竭诊断中血液学指标分析的数据集

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.xlsx
• DOI：10.3389/fmed.2024.1285067.s001
• 发布日期：2024-03-27
• 文件数：1 个
• 文件总体积：112.83 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Analysis_of_hematological_indicators_via_explainable_artificial_intelligence_in_the_diagnosis_of_acute_heart_failure_a_retrospective_study_xlsx/25485145

命令行下载

curl -L -o "Data_Sheet_1_Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective stu.xlsx" "https://ndownloader.figshare.com/files/45291472"

文件清单

• Data_Sheet_1_Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective stu.xlsx（112.83 KB）

文件总体积 112.83 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

IntroductionAcute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF.
MethodsIn this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients’ demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model’s efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model’s incorporated risk factors.
ResultsWhite blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2024.1285067.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Analysis_of_hematological_indicators_via_explainable_artificial_intelligence_in_the_diagnosis_of_acute_heart_failure_a_retrospective_study_xlsx/25485145）。