Umeå universitets logga

umu.sePublikationer
Ändra sökning
Avgränsa sökresultatet
1 - 3 av 3
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Kelly, D.
    et al.
    Ulster University, Northern Ireland, United Kingdom.
    Condell, J.
    Ulster University, Northern Ireland, United Kingdom.
    Gillespie, J.
    Ulster University, Northern Ireland, United Kingdom.
    Munoz Esquivel, K.
    Ulster University, Northern Ireland, United Kingdom.
    Barton, J.
    Tyndall National Institute, University College Cork, Ireland.
    Tedesco, S.
    Tyndall National Institute, University College Cork, Ireland.
    Nordström, Anna
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för hållbar hälsa. Umeå universitet, Samhällsvetenskapliga fakulteten, Idrottshögskolan vid Umeå universitet. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering, Geriatrik.
    Larsson, Markus Åkerlund
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för hållbar hälsa.
    Alamäki, A.
    Karelia University of Applied Sciences, Finland.
    Improved screening of fall risk using free-living based accelerometer data2022Ingår i: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 131, artikel-id 104116Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.

  • 2.
    Tedesco, Salvatore
    et al.
    Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, Ireland.
    Andrulli, Martina
    Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, Ireland.
    Larsson, Markus Åkerlund
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för hållbar hälsa.
    Kelly, Daniel
    School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom.
    Alamäki, Antti
    Department of Physiotherapy, Karelia University of Applied Sciences, Tikkarinne 9, Joensuu, Finland.
    Timmons, Suzanne
    Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland.
    Barton, John
    Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, Ireland.
    Condell, Joan
    School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom.
    O’flynn, Brendan
    Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, Ireland.
    Nordström, Anna
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för hållbar hälsa. School of Sport Sciences, UiT the Arctic University of Norway, Tromsø, Norway.
    Comparison of machine learning techniques for mortality prediction in a prospective cohort of older adults2021Ingår i: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, Vol. 18, nr 23, artikel-id 12806Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all‐cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all‐cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free‐living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random Under‐ Sampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data‐driven and disease‐agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

    Ladda ner fulltext (pdf)
    fulltext
  • 3.
    Tedesco, Salvatore
    et al.
    University College Cork, Tyndall National Institute, Cork, Ireland.
    Andrulli, Martina
    University College Cork, Tyndall National Institute, Cork, Ireland.
    Åkerlund Larsson, Markus
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin.
    Kelly, Daniel
    School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom.
    Timmons, Suzanne
    Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland.
    Alamäki, Antti
    Department of Physiotherapy, Karelia University of Applied Sciences, Joensuu, Finland.
    Barton, John
    University College Cork, Tyndall National Institute, Cork, Ireland.
    Condell, Joan
    School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom.
    O'Flynn, Brendan
    University College Cork, Tyndall National Institute, Cork, Ireland.
    Nordström, Anna
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin. School of Sport Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
    Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults2021Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, IEEE, 2021, s. 1848-1851Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cancer 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.

1 - 3 av 3
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf