Gregor Štiglic (Avtor), Petra Povalej (Avtor), Nino Fijačko (Avtor), Fei Wang (Avtor), Alexandros Kalousis (Avtor), Boris Delibašić (Avtor), Zoran Obradović (Avtor)

Povzetek

Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755%0.771) to 0.769 (95% CI: 0.761%0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.

Ključne besede

predictive models;logistic regression;readmission classification;comorbidities;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UM FZV - Fakulteta za zdravstvene vede
UDK: 004.6:61
COBISS: 2183076 Povezava se bo odprla v novem oknu
ISSN: 1932-6203
Št. ogledov: 882
Št. prenosov: 314
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: napovedovalni modeli;logistična regresija;klasifikacija ponovnega sprejema;pridružene motnje;
URN: URN:SI:UM:
Vrsta dela (COBISS): Znanstveno delo
Strani: str. 1-6
Letnik: ǂVol. ǂ10
Zvezek: ǂno. ǂ12
Čas izdaje: 2015
DOI: 10.1371/journal.pone.0144439
ID: 9142110