Health data science最新文献_第3页

See your stories: Visualisation for Narrative Medicine 看看你的故事叙事医学的可视化

Health data science Pub Date : 2023-12-04 DOI: 10.34133/hds.0103

Hua Ma, Xiaoru Yuan, Xu Sun, Glyn Lawson, Qingfeng Wang

引用次数: 0

Using mobile-phone data to assess socio-economic disparities in unhealthy food reliance during the COVID-19 pandemic 利用手机数据评估 COVID-19 大流行期间不健康食品依赖的社会经济差异

Health data science Pub Date : 2023-11-30 DOI: 10.34133/hds.0101

Charles Alba, Ruopeng An

引用次数: 0

Transforming health care through a learning health system approach in the digital era: Chronic kidney disease management in China 在数字时代，通过学习型医疗系统方法实现医疗保健转型：中国的慢性肾病管理

Health data science Pub Date : 2023-11-30 DOI: 10.34133/hds.0102

Guilan Kong, Jinwei Wang, Hongbo Lin, Beiyan Bao, Charles Friedman, Luxia Zhang

引用次数: 0

Detection of Patients at Risk of Multi-Drug Resistant Enterobacteriaceae Infection using Graph Neural Networks: a Retrospective Study 使用图神经网络检测多重耐药肠杆菌科感染风险患者:一项回顾性研究

Health data science Pub Date : 2023-10-24 DOI: 10.34133/hds.0099

Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro

引用次数: 0

Recent progress in wearable brain-computer interface (BCI) devices based on electroencephalogram (EEG) for medical applications: A review 基于脑电图(EEG)的可穿戴脑机接口(BCI)设备在医学上的应用进展综述

Health data science Pub Date : 2023-10-23 DOI: 10.34133/hds.0096

Jiayan Zhang, Junshi Li, Zhe Huang, Dong Huang, Huaiqiang Yu, Zhihong Li

引用次数: 0

A Structure-based Allosteric Modulator Design Paradigm 基于结构的变构调制器设计范式

Health data science Pub Date : 2023-10-15 DOI: 10.34133/hds.0094

Mingyu Li, Xiaobin Lan, Xun Lu, Jian Zhang

引用次数: 0

A Machine Learning Approach to Predict HIV Viral Load Hotspots in Kenya Using Real-World Data. 利用真实世界数据预测肯尼亚HIV病毒载量热点的机器学习方法

Health data science Pub Date : 2023-10-02 eCollection Date: 2023-01-01 DOI: 10.34133/hds.0019

Nancy Kagendi, Matilu Mwau

{"title":"A Machine Learning Approach to Predict HIV Viral Load Hotspots in Kenya Using Real-World Data.","authors":"Nancy Kagendi, Matilu Mwau","doi":"10.34133/hds.0019","DOIUrl":"10.34133/hds.0019","url":null,"abstract":"Background: Machine learning models are not in routine use for predicting HIV status. Our objective is to describe the development of a machine learning model to predict HIV viral load (VL) hotspots as an early warning system in Kenya, based on routinely collected data by affiliate entities of the Ministry of Health. Based on World Health Organization's recommendations, hotspots are health facilities with ≥20% people living with HIV whose VL is not suppressed. Prediction of VL hotspots provides an early warning system to health administrators to optimize treatment and resources distribution.Methods: A random forest model was built to predict the hotspot status of a health facility in the upcoming month, starting from 2016. Prior to model building, the datasets were cleaned and checked for outliers and multicollinearity at the patient level. The patient-level data were aggregated up to the facility level before model building. We analyzed data from 4 million tests and 4,265 facilities. The dataset at the health facility level was divided into train (75%) and test (25%) datasets.Results: The model discriminates hotspots from non-hotspots with an accuracy of 78%. The F1 score of the model is 69% and the Brier score is 0.139. In December 2019, our model correctly predicted 434 VL hotspots in addition to the observed 446 VL hotspots.Conclusion: The hotspot mapping model can be essential to antiretroviral therapy programs. This model can provide support to decision-makers to identify VL hotspots ahead in time using cost-efficient routinely collected data.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"0019"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48874541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models. simpleNomo:为逻辑回归模型的可视化计算制作nomogram的Python包

Health data science Pub Date : 2023-06-07 eCollection Date: 2023-01-01 DOI: 10.34133/hds.0023

Haoyang Hong, Shenda Hong

{"title":"simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models.","authors":"Haoyang Hong, Shenda Hong","doi":"10.34133/hds.0023","DOIUrl":"10.34133/hds.0023","url":null,"abstract":"Background: Logistic regression models are widely used in clinical prediction, but their application in resource-poor settings or areas without internet access can be challenging. Nomograms can serve as a useful visualization tool to speed up the calculation procedure, but existing nomogram generators often require the input of raw data, inhibiting the transformation of established logistic regression models that only provide coefficients. Developing a tool that can generate nomograms directly from logistic regression coefficients would greatly increase usability and facilitate the translation of research findings into patient care.Methods: We designed and developed simpleNomo, an open-source Python toolbox that enables the construction of nomograms for logistic regression models. Uniquely, simpleNomo allows for the creation of nomograms using only the coefficients of the model. Further, we also devoloped an online website for nomogram generation.Results: simpleNomo properly maintains the predictive ability of the original logistic regression model and easy to follow. simpleNomo is compatible with Python 3 and can be installed through Python Package Index (PyPI) or https://github.com/Hhy096/nomogram.Conclusion: This paper presents simpleNomo, an open-source Python toolbox for generating nomograms for logistic regression models. It facilitates the process of transferring established logistic regression models to nomograms and can further convert more existing works into practical use.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"0023"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44189861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping Chinese Medical Entities to the Unified Medical Language System. 中文医学实体到统一医学语言系统的映射

Health data science Pub Date : 2023-03-30 eCollection Date: 2023-01-01 DOI: 10.34133/hds.0011

Luming Chen, Yifan Qi, Aiping Wu, Lizong Deng, Taijiao Jiang

{"title":"Mapping Chinese Medical Entities to the Unified Medical Language System.","authors":"Luming Chen, Yifan Qi, Aiping Wu, Lizong Deng, Taijiao Jiang","doi":"10.34133/hds.0011","DOIUrl":"10.34133/hds.0011","url":null,"abstract":"Background: Chinese medical entities have not been organized comprehensively due to the lack of well-developed terminology systems, which poses a challenge to processing Chinese medical texts for fine-grained medical knowledge representation. To unify Chinese medical terminologies, mapping Chinese medical entities to their English counterparts in the Unified Medical Language System (UMLS) is an efficient solution. However, their mappings have not been investigated sufficiently in former research. In this study, we explore strategies for mapping Chinese medical entities to the UMLS and systematically evaluate the mapping performance.Methods: First, Chinese medical entities are translated to English using multiple web-based translation engines. Then, 3 mapping strategies are investigated: (a) string-based, (b) semantic-based, and (c) string and semantic similarity combined. In addition, cross-lingual pretrained language models are applied to map Chinese medical entities to UMLS concepts without translation. All of these strategies are evaluated on the ICD10-CN, Chinese Human Phenotype Ontology (CHPO), and RealWorld datasets.Results: The linear combination method based on the SapBERT and term frequency-inverse document frequency bag-of-words models perform the best on all evaluation datasets, with 91.85%, 82.44%, and 78.43% of the top 5 accuracies on the ICD10-CN, CHPO, and RealWorld datasets, respectively.Conclusions: In our study, we explore strategies for mapping Chinese medical entities to the UMLS and identify a satisfactory linear combination method. Our investigation will facilitate Chinese medical entity normalization and inspire research that focuses on Chinese medical ontology development.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"1 1","pages":"0011"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41855781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Incidence of Moderate and Severe Ovarian Hyperstimulation Syndrome in Hospitalized Patients in China. 2013-2017年中国住院患者中重度卵巢过度刺激综合征的发病率

Health data science Pub Date : 2023-03-15 eCollection Date: 2023-01-01 DOI: 10.34133/hds.0009

Danni Zheng, Ying Shi, Yuanyuan Wang, Rong Li, Xiaoyu Long, Jie Qiao

{"title":"The Incidence of Moderate and Severe Ovarian Hyperstimulation Syndrome in Hospitalized Patients in China.","authors":"Danni Zheng, Ying Shi, Yuanyuan Wang, Rong Li, Xiaoyu Long, Jie Qiao","doi":"10.34133/hds.0009","DOIUrl":"10.34133/hds.0009","url":null,"abstract":"Background: Ovarian hyperstimulation syndrome (OHSS) occurs in women receiving fertility treatments. Moderate and severe OHSS cases are required to be admitted to hospital for treatment. The incidence of moderate and severe OHSS and the characteristics of these cases are unknown in China. We aimed to assess the incidence of moderate and severe OHSS in national databases from China between 2013 and 2017.Methods: We extracted moderate and severe OHSS cases from the Hospital Quality Monitoring System, the nationwide inpatient data collection system. We used ovum pick-up (OPUbaidu) cycle data from the annual report of China's National Health Commission, developed on the basis of OPU data collected by National ART Management Information System. Overall incidence of moderate and severe OHSS (women aged 20 to 50 years) and year-specific incidence by each calendar year in China were calculated. We also investigated the age distribution in OHSS and OHSS with different comorbidities.Results: We extracted 18,022 eligible patients with moderate or severe OHSS and 1,581,703 OPU cycles. The overall incidence of moderate and severe OHSS between 2013 and 2017 was 1.14%. The year-specific moderate and severe OHSS incidence was 1.1% in 2013, 1.4% in 2014, 1.4% in 2015, 1.1% in 2016, 0.9% in 2017, respectively. Women aged 26 to 30 years accounted for 48.4% of OHSS cases, followed by women aged 31 to 35 years (30%) and 20 to 25 years (14.2%). The age distribution pattern was consistent across OHSS with different comorbidities.Conclusions: This study reported the incidence of moderate and severe OHSS in China using nationwide data for the first time. Our findings support that women aged under 35 years receiving assisted reproductive technology need more attention than other age groups in terms of OHSS risk control.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"0009"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44991011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0