H. Kondylakis, L. Koumakis, Dimitrios G. Katehakis, A. Kouroubali, K. Marias, M. Tsiknakis, P. Simos, E. Karademas
{"title":"Developing a Data Infrastructure for Enabling Breast Cancer Women to BOUNCE Back","authors":"H. Kondylakis, L. Koumakis, Dimitrios G. Katehakis, A. Kouroubali, K. Marias, M. Tsiknakis, P. Simos, E. Karademas","doi":"10.1109/CBMS.2019.00134","DOIUrl":null,"url":null,"abstract":"Breast cancer is the most common cancer disease in women and is rapidly becoming a chronic illness due recent advances in treatment methods. As such, coping with cancer has become a major socio-economic challenge leading to an increasing need for predicting resilience of women to the variety of stressful experiences and practical challenges they face. In this paper, we present the data infrastructure developed for this purpose, demonstrating the various components that will contribute to the developing the resilience trajectory predictor. Special emphasis is given to the semantic tier, presenting the project solution already implemented for effectively collecting, ingesting, cleaning, modelling and processing data that will be used throughout the lifetime of the project.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
Breast cancer is the most common cancer disease in women and is rapidly becoming a chronic illness due recent advances in treatment methods. As such, coping with cancer has become a major socio-economic challenge leading to an increasing need for predicting resilience of women to the variety of stressful experiences and practical challenges they face. In this paper, we present the data infrastructure developed for this purpose, demonstrating the various components that will contribute to the developing the resilience trajectory predictor. Special emphasis is given to the semantic tier, presenting the project solution already implemented for effectively collecting, ingesting, cleaning, modelling and processing data that will be used throughout the lifetime of the project.