Health data sciencePub Date : 2023-12-16eCollection Date: 2024-01-01DOI: 10.34133/hds.0216
Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro
{"title":"Erratum to \"Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study\".","authors":"Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro","doi":"10.34133/hds.0216","DOIUrl":"10.34133/hds.0216","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.34133/hds.0099.].</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0216"},"PeriodicalIF":0.0,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11649199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840458","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}
Adeolu Z Ogunleye, Chayanit Piyawajanusorn, G. Ghislat, Pedro Ballester
{"title":"Large-scale machine learning analysis reveals DNA-methylation and gene-expression response signatures for gemcitabine-treated pancreatic cancer","authors":"Adeolu Z Ogunleye, Chayanit Piyawajanusorn, G. Ghislat, Pedro Ballester","doi":"10.34133/hds.0108","DOIUrl":"https://doi.org/10.34133/hds.0108","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using mobile-phone data to assess socio-economic disparities in unhealthy food reliance during the COVID-19 pandemic","authors":"Charles Alba, Ruopeng An","doi":"10.34133/hds.0101","DOIUrl":"https://doi.org/10.34133/hds.0101","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transforming health care through a learning health system approach in the digital era: Chronic kidney disease management in China","authors":"Guilan Kong, Jinwei Wang, Hongbo Lin, Beiyan Bao, Charles Friedman, Luxia Zhang","doi":"10.34133/hds.0102","DOIUrl":"https://doi.org/10.34133/hds.0102","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139201723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro
{"title":"Detection of Patients at Risk of Multi-Drug Resistant Enterobacteriaceae Infection using Graph Neural Networks: a Retrospective Study","authors":"Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, Douglas Teodoro","doi":"10.34133/hds.0099","DOIUrl":"https://doi.org/10.34133/hds.0099","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"23 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135273078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Recent progress in wearable brain-computer interface (BCI) devices based on electroencephalogram (EEG) for medical applications: A review","authors":"Jiayan Zhang, Junshi Li, Zhe Huang, Dong Huang, Huaiqiang Yu, Zhihong Li","doi":"10.34133/hds.0096","DOIUrl":"https://doi.org/10.34133/hds.0096","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Health data sciencePub Date : 2023-10-02eCollection Date: 2023-01-01DOI: 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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","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}
Health data sciencePub Date : 2023-06-07eCollection Date: 2023-01-01DOI: 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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","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}