癫痫孕妇癫痫发作预测模型数据集

数据集获取（Figshare）

• 源站标题：Predicting seizures in pregnant women with epilepsy: Development and external validation of a prognostic model
• DOI：10.1371/journal.pmed.1002802
• 发布日期：2019-05-13
• 文件数：3 个
• 文件总体积：655.95 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Predicting_seizures_in_pregnant_women_with_epilepsy_Development_and_external_validation_of_a_prognostic_model/8119256

命令行下载

curl -L -o "pmed.1002802.s001.tif" "https://ndownloader.figshare.com/files/15136892"
curl -L -o "pmed.1002802.s002.docx" "https://ndownloader.figshare.com/files/15136895"
curl -L -o "pmed.1002802.s003.docx" "https://ndownloader.figshare.com/files/15136898"

文件清单

• pmed.1002802.s001.tif（482.30 KB）
• pmed.1002802.s002.docx（78.48 KB）
• pmed.1002802.s003.docx（95.17 KB）

文件总体积 655.95 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

Background
Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs.
Methods and findings
We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011–17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0–1, values > 0.5 show discrimination) and calibration slope (scale 0–1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pmed.1002802（source: https://plos.figshare.com/articles/dataset/Predicting_seizures_in_pregnant_women_with_epilepsy_Development_and_external_validation_of_a_prognostic_model/8119256）。