Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults

Simple item page
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorAndrulli, Martina
dc.contributor.authorÅkerlund Larsson, Markus
dc.contributor.authorKelly, Daniel
dc.contributor.authorTimmons, Suzanne
dc.contributor.authorAlamäki, Antti
dc.contributor.authorBarton, John
dc.contributor.authorCondell, Joan
dc.contributor.authorO'Flynn, Brendan
dc.contributor.authorNordström, Anna
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderEuropean Commissionen
dc.contributor.funderInterregen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderDepartment of Business, Enterprise and Innovation, Irelanden
dc.date.accessioned2021-11-11T16:05:00Z
dc.date.available2021-11-11T16:05:00Z
dc.date.issued2021-11-01
dc.date.updated2021-11-11T15:55:01Z
dc.description.abstractCancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults.en
dc.description.sponsorshipEuropean Regional Development Fund (ERDF under Ireland’s European Structural and Investment Funds Programmes 2014-2020); European Commission ( INTERREG NPA funded project SenDOC); Science Foundation Ireland (Grant number 12/RC/2289-P2 INSIGHT-2 which is co-funded under the ERDF); Enterprise Ireland and the Department of Business, Enterprise and Innovation (under the DTIF project HOLISTICS)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTedesco, S., Andrulli, M., Åkerlund Larsson, M., Kelly, D., Timmons, S., Alamäki,, A., Barton, J., Condell, J., O'Flynn, B. and Nordström, A. (2021) 'Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults', EMBC 2021, 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Virtual Event, 01-05 Nov. pp. 1849-1851. doi: 10.1109/EMBC46164.2021.9630370en
dc.identifier.doi10.1109/EMBC46164.2021.9630370
dc.identifier.endpage1851en
dc.identifier.isbn978-1-7281-1179-7/21/
dc.identifier.startpage1849en
dc.identifier.urihttps://hdl.handle.net/10468/12202
dc.language.isoenen
dc.publisherIEEEen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://ieeexplore.ieee.org/document/9630370
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen
dc.subjectCanceren
dc.subjectElectronic health recordsen
dc.subjectMortalityen
dc.subjectOlder Adultsen
dc.subjectPredictionen
dc.subjectWearablesen
dc.titleInvestigation of the analysis of wearable data for cancer-specific mortality prediction in older adultsen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
EMBC21_0693_FI.pdf
Size:
274.25 KB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tyndall National Institute - Conference Items
Insight Centre for Data Analytics - Conference Items