马尔可夫模型预测南非私营与公共诊所抗逆转录病毒治疗成本和结果
基于马尔可夫模型,比较南非私营与公共诊所ART治疗的成本与结果,包含模型参数、生存和成本估计。

基本信息
资源简介
马尔可夫模型预测南非私营与公共诊所抗逆转录病毒治疗成本和结果
【数据集背景】
本数据集源站标题为《A Novel Markov Model Projecting Costs and Outcomes of Providing Antiretroviral Therapy to Public Patients in Private Practices versus Public Clinics in South Africa》。配套论文《A Novel Markov Model Projecting Costs and Outcomes of Providing Antiretroviral Therapy to Public Patients in Private Practices versus Public Clinics in South Africa》发表于 PLoS ONE(2013),DOI:10.1371/journal.pone.0053570。源站首次发布:2013-02-06。
【数据内容】
源站原始描述:Introduction Providing private antiretroviral therapy (ART) care for public sector patients could increase access to ART in low- and middle-income countries. We compared the costs and outcomes of a private-care and a public-care ART program in South Africa. Methods A novel Markov model was developed from the public-care program. Patients were first tunneled for 6 months in their baseline CD4 category before being distributed into a dynamic CD4 and viral load model. Patients were allowed to retur…
【数据结构与技术规格】
文件数 7 个,合计 10.33 MB。Figure_S1.tif:image/tiff,3.70 MB。Figure_S2.tif:image/tiff,3.87 MB。Figure_S3.tif:image/tiff,563.76 KB。数据集 DOI:10.1371/journal.pone.0053570。
【主题与分类】
源站学科分类:Biotechnology、Mathematics、Medicine、Infectious Diseases。主题标签:markov、projecting、costs、outcomes、providing、antiretroviral、patients、practices、clinics、africa。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面:https://plos.figshare.com/articles/dataset/A_Novel_Markov_Model_Projecting_Costs_and_Outcomes_of_Providing_Antiretroviral_Therapy_to_Public_Patients_in_Private_Practices_versus_Public_Clinics_in_South_Africa__/155898
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Leisegang, Rory; Maartens, Gary; Hislop, Michael; Sargent, John; Darkoh, Ernest; Cleary, Susan (2016). A Novel Markov Model Projecting Costs and Outcomes of Providing Antiretroviral Therapy to Public Patients in Private Practices versus Public Clinics in South Africa. PLOS ONE. Dataset
【使用注意】
· 源站最后修改于 2023-06-01,引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:A Novel Markov Model Projecting Costs and Outcomes of Providing Antiretroviral Therapy to Public Patients in Private Practices versus Public Clinics in South Africa
- DOI:10.1371/journal.pone.0053570
- 发布日期:2013-02-06
- 文件数:7 个
- 文件总体积:10.33 MB
- 源站页面:https://plos.figshare.com/articles/dataset/A_Novel_Markov_Model_Projecting_Costs_and_Outcomes_of_Providing_Antiretroviral_Therapy_to_Public_Patients_in_Private_Practices_versus_Public_Clinics_in_South_Africa__/155898
命令行下载
curl -L -o "Figure_S1.tif" "https://ndownloader.figshare.com/files/482668"
curl -L -o "Figure_S2.tif" "https://ndownloader.figshare.com/files/482670"
curl -L -o "Figure_S3.tif" "https://ndownloader.figshare.com/files/482673"
curl -L -o "Table_S1.xls" "https://ndownloader.figshare.com/files/482674"
curl -L -o "Table_S2.xls" "https://ndownloader.figshare.com/files/482676"
curl -L -o "Table_S3.tif" "https://ndownloader.figshare.com/files/482677"
curl -L -o "Table_S4.xls" "https://ndownloader.figshare.com/files/482679"
文件清单
Figure_S1.tif(3.70 MB)Figure_S2.tif(3.87 MB)Figure_S3.tif(563.76 KB)Table_S1.xls(98.82 KB)Table_S2.xls(86.02 KB)Table_S3.tif(1.88 MB)Table_S4.xls(135.68 KB)
文件总体积 10.33 MB(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
Introduction
Providing private antiretroviral therapy (ART) care for public sector patients could increase access to ART in low- and middle-income countries. We compared the costs and outcomes of a private-care and a public-care ART program in South Africa.
Methods
A novel Markov model was developed from the public-care program. Patients were first tunneled for 6 months in their baseline CD4 category before being distributed into a dynamic CD4 and viral load model. Patients were allowed to return to ART care from loss to follow up (LTFU). We then populated this modeling framework with estimates derived from the private-care program to externally validate the model.
Results
Baseline characteristics were similar in the two programs. Clinic visit utilization was higher and death rates were lower in the first few years on ART in the public-care program. After 10 years on ART we estimated the following outcomes in the public-care and private-care programs respectively: viral load 500 cells/μl 33% and 37%, LTFU 14% and 14%, and death 27% and 32%. Lifetime undiscounted survival estimates were 14.1 (95%CI 13.2–14.9) and (95%CI 12.7–14.5) years with costs of 18,734 (95%CI 12,588–14,022) and 13,062 (95%CI 12,077–14,047) USD in the private-care and public-care programs respectively. When clinic visit utilization in the public-care program was reduced by two thirds after the initial 6 months on ART, which is similar to their current practice, the costs were comparable between the programs.
Conclusions
Using a novel Markov model, we determined that the private-care program had similar outcomes but lower costs than the public-care program, largely due to lower visit frequencies. These findings have important implications for increasing and sustaining coverage of patients in need of ART care in resource-limited settings.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0053570(source: https://plos.figshare.com/articles/dataset/A_Novel_Markov_Model_Projecting_Costs_and_Outcomes_of_Providing_Antiretroviral_Therapy_to_Public_Patients_in_Private_Practices_versus_Public_Clinics_in_South_Africa__/155898)。
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
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