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Naslov:Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
Avtorji:Povalej, Petra (Avtor)
Obradović, Zoran (Avtor)
Štiglic, Gregor (Avtor)
Datoteke:.pdf PeerJ_2017_Povalej_Brzan,_Obradovic,_Stiglic_Contribution_of_temporal_data_to_predictive_performance_in_30-day_readmission_of_morbidly_o.pdf (1,10 MB)
 
URL https://peerj.com/articles/3230
 
Jezik:Angleški jezik
Vrsta gradiva:Znanstveno delo (r2)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FZV - Fakulteta za zdravstvene vede
Opis:Background: Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. Methods: The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results. Results: Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation. Discussion: The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.
Ključne besede:readmission prediction, predictive modelling, temporal data
Leto izida:2017
Št. strani:str. 1-14
Številčenje:Letn. 5
ISSN:2167-8359
UDK:614.2:004.6
COBISS_ID:2321060 Povezava se odpre v novem oknu
DOI:10.7717/peerj.3230 Povezava se odpre v novem oknu
ISSN pri članku:2167-8359
Licenca:CC BY 4.0
To delo je dosegljivo pod licenco Creative Commons Priznanje avtorstva 4.0 Mednarodna
Število ogledov:81
Število prenosov:2
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
Področja:Ostalo
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Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Gradivo je del revije

Naslov:PeerJ
Založnik:PeerJ Inc.
ISSN:2167-8359
COBISS.SI-ID:31891929 Novo okno

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:napoved ponovnega sprejema, napovedno modeliranje, časovni podatki


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