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Status: Bibliographieeintrag
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 Online-Ressource
Verfasst von:Dudchenko, Aleksei [VerfasserIn]   i
 Knaup-Gregori, Petra [VerfasserIn]   i
 Ganzinger, Matthias [VerfasserIn]   i
Titel:A predictive model for patient similarity
Titelzusatz:classes based on secondary data and simple measurements as predictors
Verf.angabe:Aleksei Dudchenko, Georgy Kopanitsa, Petra Knaup, Matthias Ganzinger
Jahr:2018
Umfang:6 S.
Fussnoten:Gesehen am 24.06.2019
Titel Quelle:Enthalten in: pHealth (Veranstaltung : 15. : 2018 : Gjøvik)pHealth 2018
Ort Quelle:Amsterdam, Netherlands : IOS Press, 2018
Jahr Quelle:2018
Band/Heft Quelle:(2018), Seite 167-172
ISBN Quelle:1-61499-868-X
 978-1-61499-868-6
Abstract:Predictive models optimized for average cases might work not perfect for cases deviating from average because they are based on a cohort of all patients. Models could be more personalized if they were built on a sub-cohort of patients similar to a current one and to train models on data collected from those similar patients. In this paper, we consider patient similarity as a classification task. We suppose that data such as diagnoses and treatment obtained by physicians (secondary data) are more relevant for similarity than tests and measurements (primary data). We defined several classes based on diagnoses and outcomes and apply a predictive model for classification. We used five commonly used and easy to obtain measurements as predictors for the model. All measurements were collected during the first 24 hours after admission. We have shown that classes of similar patients can be defined on the basis of a previous patient's secondary data and new patients can be classified into these classes.
DOI:doi:10.3233/978-1-61499-868-6-167
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.3233/978-1-61499-868-6-167
 DOI: https://doi.org/10.3233/978-1-61499-868-6-167
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Forecasting
 Humans
 Models, Theoretical
 Patient Admission
 Patient classification
 Patient similarity
 Patients
 Predictive model
K10plus-PPN:1667823973
Verknüpfungen:→ Sammelwerk

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