基于机器学习提高骨科围手术期患者不合理处方精准预测的数据集

数据集获取（Figshare）

• 源站标题：Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients
• DOI：10.6084/m9.figshare.25745082.v1
• 发布日期：2024-05-03
• 文件数：2 个
• 文件总体积：730.27 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Development_and_validation_of_a_machine_learning_model_to_improve_precision_prediction_for_irrational_prescriptions_in_orthopedic_perioperative_patients/25745082

命令行下载

curl -L -o "ieds_a_2348569_sm4948.docx" "https://ndownloader.figshare.com/files/46065855"
curl -L -o "ieds_a_2348569_sm4947.docx" "https://ndownloader.figshare.com/files/46065858"

文件清单

• ieds_a_2348569_sm4948.docx（215.70 KB）
• ieds_a_2348569_sm4947.docx（514.57 KB）

文件总体积 730.27 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

Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients.
A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated.
Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs.
The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.25745082.v1（source: https://tandf.figshare.com/articles/dataset/Development_and_validation_of_a_machine_learning_model_to_improve_precision_prediction_for_irrational_prescriptions_in_orthopedic_perioperative_patients/25745082）。