利用机器学习预测成人噬血细胞性淋巴组织细胞增多症六个月死亡率：基于实验室数据的预后方法

数据集获取（Figshare）

• 源站标题：Predicting six-month mortality in adult hemophagocytic lymphohistiocytosis with machine learning: a prognostic approach utilizing laboratory data
• DOI：10.6084/m9.figshare.30273821.v1
• 发布日期：2025-10-03
• 文件数：3 个
• 文件总体积：553.55 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Predicting_six-month_mortality_in_adult_hemophagocytic_lymphohistiocytosis_with_machine_learning_a_prognostic_approach_utilizing_laboratory_data/30273821

命令行下载

curl -L -o "iann_a_2566869_sm0470.docx" "https://ndownloader.figshare.com/files/58483442"
curl -L -o "iann_a_2566869_sm3657.jpg" "https://ndownloader.figshare.com/files/58483445"
curl -L -o "iann_a_2566869_sm3656.jpg" "https://ndownloader.figshare.com/files/58483448"

文件清单

• iann_a_2566869_sm0470.docx（11.86 KB）
• iann_a_2566869_sm3657.jpg（155.19 KB）
• iann_a_2566869_sm3656.jpg（386.50 KB）

文件总体积 553.55 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

Hemophagocytic lymphohistiocytosis (HLH) is associated with high mortality rates. This study was conducted to develop and validate a predictive model for adult HLH patients at high risk of six months mortality using machine learning (ML) algorithms.
The study utilized univariate analysis and LASSO regression, incorporating eleven ML algorithms, to perform a comprehensive analysis of both admission and discharge variables. Model performance was assessed using metrics such as AUC, F1 score, Kaplan Meier (KM) curves, calibration curves and decision curve analysis (DCA).
A total of 136 patients meeting the HLH-2004 criteria were included in this study. Among them, 59 patients received chemotherapy or other cytotoxic drugs, while the remaining 77 patients underwent symptomatic treatment. The median age was 52 years among nonsurvivors and 48 years among survivors, with 47 (42.6%) males in the nonsurvivor group and 31 (47.6%) in the survivor group. Age and nine discharge variables were identified as the most significant features for model construction. Random forest (RF) algorithm demonstrated superior predictive capabilities, achieving an AUC of 1.00, accuracy of 0.98 and F1 score of 0.98 in the training cohort, and an AUC of 0.89, accuracy of 0.85 and F1 score of 0.85 in the validation cohort. The top five predictors were all discharge variables (ferritin, white blood cell (WBC), albumin (ALB), platelet (PLT) and direct bilirubin (DB)).
The predictive model developed in this study provides a valuable tool for clinicians to early identify high-risk HLH patients, thereby enabling more targeted and effective interventions.

Hemophagocytic lymphohistiocytosis (HLH) is associated with high mortality rates.
Discharge variables offer more reliable prognostic information for adult HLH.
This study provides a valuable tool for clinicians to early identify high-risk HLH patients, thereby enabling more targeted and effective interventions

Hemophagocytic lymphohistiocytosis (HLH) is associated with high mortality rates.
Discharge variables offer more reliable prognostic information for adult HLH.
This study provides a valuable tool for clinicians to early identify high-ri

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30273821.v1（source: https://tandf.figshare.com/articles/dataset/Predicting_six-month_mortality_in_adult_hemophagocytic_lymphohistiocytosis_with_machine_learning_a_prognostic_approach_utilizing_laboratory_data/30273821）。