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Personalised need of care in an ageing society: The making of a prediction tool based on register data

  1. Wright, Marvin ORCID logo
  2. Kusumastuti, Sasmita
  3. Mortensen, Laust H.
  4. Westendorp, Rudi G. J.
  5. Gerds, Thomas A.

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Wright-et-al_2021_Personalised need of care in an ageing society.pdf 967,71KB
WeightNameValue
1000 Titel
  • Personalised need of care in an ageing society: The making of a prediction tool based on register data
1000 Autor/in
  1. Wright, Marvin |
  2. Kusumastuti, Sasmita |
  3. Mortensen, Laust H. |
  4. Westendorp, Rudi G. J. |
  5. Gerds, Thomas A. |
1000 Erscheinungsjahr 2021
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-01-16
1000 Erschienen in
1000 Quellenangabe
  • 184(4):1199-1219
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1111/rssa.12644 |
1000 Ergänzendes Material
  • https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12644#support-information-section |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Danish municipalities monitor older persons who are at high risk of declining health and would later need home care services. However, there is no established strategy yet on how to accurately identify those who are at high risk. Therefore, there is great potential to optimise the municipalities’ prevention strategies. Denmark’s comprehensive set of electronic population registers provide longitudinal data that cover individual and household socio-demographics and medical history. Using these data, we developed and applied recurrent neural networks to predict the risk of a need of care services in the future and thus identify individuals who would benefit the most from the municipalities’ prevention strategies. We compared our recurrent neural network model to prediction models based on Cox regression and Fine–Gray regression in terms of calibration and discrimination. Challenges for the prediction modelling were the competing risk of death and the longitudinal information on the registered life course data.
1000 Sacherschließung
lokal Survival analysis
lokal Register data
lokal Competing risks
lokal Recurrent neural networks
lokal Deep learning
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-8542-6291|https://frl.publisso.de/adhoc/uri/S3VzdW1hc3R1dGksIFNhc21pdGE=|https://frl.publisso.de/adhoc/uri/TW9ydGVuc2VuLCBMYXVzdCBILg==|https://frl.publisso.de/adhoc/uri/V2VzdGVuZG9ycCwgUnVkaSBHLiBKLg==|https://frl.publisso.de/adhoc/uri/R2VyZHMsIFRob21hcyBBLg==
1000 Label
1000 Förderer
  1. Novo Nordisk Fonden |
  2. Projekt DEAL |
1000 Fördernummer
  1. NNF17OC0027812
  2. -
1000 Förderprogramm
  1. Harnessing the Power of Big Data to Address the Societal Challenge of Aging
  2. Open Access funding
1000 Dateien
  1. Personalised need of care in an ageing society: The making of a prediction tool based on register data
    Personalised need of care in an ageing society: The making of a prediction tool based on register data
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Novo Nordisk Fonden |
    1000 Förderprogramm Harnessing the Power of Big Data to Address the Societal Challenge of Aging
    1000 Fördernummer NNF17OC0027812
  2. 1000 joinedFunding-child
    1000 Förderer Projekt DEAL |
    1000 Förderprogramm Open Access funding
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6430752.rdf
1000 Erstellt am 2021-12-17T12:50:21.028+0100
1000 Erstellt von 266
1000 beschreibt frl:6430752
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet Fri Jan 21 14:48:47 CET 2022
1000 Objekt bearb. Fri Dec 17 12:52:09 CET 2021
1000 Vgl. frl:6430752
1000 Oai Id
  1. oai:frl.publisso.de:frl:6430752 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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