Počet záznamů: 1
The Effect of Missing Data when Predicting Readmission in Heart Failure Patients
- 1.
SYSNO ASEP 0582863 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název The Effect of Missing Data when Predicting Readmission in Heart Failure Patients Tvůrce(i) Plešinger, Filip (UPT-D) RID, ORCID, SAI
Koščová, Zuzana (UPT-D)
Vargová, Enikö (UPT-D)
Pavlus, Ján (UPT-D)
Smíšek, Radovan (UPT-D) RID, ORCID, SAI
Viščor, Ivo (UPT-D) RID, ORCID, SAI
Bulková, V. (CZ)Celkový počet autorů 7 Zdroj.dok. 2023 Computing in Cardiology (CinC). - New York : IEEE, 2023 - ISSN 2325-8861 - ISBN 979-8-3503-8252-5 Rozsah stran (2023) Poč.str. 4 s. Forma vydání Online - E Akce Computing in Cardiology 2023 /50./ Datum konání 01.10.2023 - 04.10.2023 Místo konání Atlanta Země US - Spojené státy americké Typ akce WRD Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova heart failure ; modelling ; readmission Vědní obor RIV FS - Lékařská zařízení, přístroje a vybavení Obor OECD Medical engineering CEP FW06010766 GA TA ČR - Technologická agentura ČR Institucionální podpora UPT-D - RVO:68081731 EID SCOPUS 85182328903 DOI 10.22489/CinC.2023.265 Anotace Background: The discharge of patients from hospital care is regulated by guidelines. Still, readmission of heart failure (HF) patients is a common issue, and several calculators have been published to predict it. Aims: We elaborate on how the prediction performance decreases when features become missing. We also elaborate on which features should a user include every time to reach acceptable prediction performance. Method: We prepared a balanced dataset from HF patients in the MIMIC-III database (N=2,204) with 16 features. Using training data (80%) in a four-fold cross-validation manner, we evaluated all feature combinations (N=Z^{16}-1) and found the optimal feature set for the logistic regression model. We also evaluated feature presence in top-performing models (N=655) and identified mandatory features. Finally, we trained the resultant model using all training data and evaluated the effect of missing features (N=2^{8} combinations) using separate test data (20%). Results: We identified three mandatory features (age, blood urea nitrogen, and systolic blood pressure) and eight optional. This led to a resultant model with eleven features. The hazard ratio (HR) using test data showed a value of 2.08 (95%CI 1.66-2.61) when all eleven features were present. It also showed an HR of 1.73 (95%CI1.39-2.17) when only three mandatory features were present, and others were missing (i.e., replaced by zeros). Pracoviště Ústav přístrojové techniky Kontakt Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Rok sběru 2024 Elektronická adresa https://ieeexplore.ieee.org/document/10364025
Počet záznamů: 1