20.500.12556/DKUM-59294 Comprehensible predictive modeling using regularized logistic regression and comorbidity based features 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. predictive models logistic regression readmission classification comorbidities napovedovalni modeli logistična regresija klasifikacija ponovnega sprejema pridružene motnje true false true Angleški jezik Slovenski jezik Znanstveno delo 2016-05-11 03:54:36 2017-06-19 08:40:45 2022-06-20 03:06:16 2019-07-11 11:09:05 2015 0 0 str. 1-6 št. 12 Letn. 10 2015 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1932-6203 004.6:61 2005896 2183076 10.1371/journal.pone.0144439 1932-6203 URN:SI:UM:DK:PX1JLWNK PLOS_ONE_2015_Stiglic_et_al._Comprehensible_Predictive_Modeling_Using_Regularized_Logistic_Regression_and_Comorbidity_Based_Features.PDF PLOS_ONE_2015_Stiglic_et_al._Comprehensible_Predictive_Modeling_Using_Regularized_Logistic_Regression_and_Comorbidity_Based_Features.PDF 1 15E50895E2B1843B80D606A3B252707B ea7594c40c4887c4c8578822ebf04ff64ffb2256f6e2894f62971e86daa537a0 421195a5-7c0e-11eb-bb7a-00155d0001ca https://dk.um.si/Dokument.php?lang=slv&id=112715 http://dx.plos.org/10.1371/journal.pone.0144439 1 https://dk.um.si/Dokument.php?lang=slv&id=91363 Fakulteta za zdravstvene vede 0 0 0