Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier

Journal of Biomedical Informatics
Raheleh Davoodi, Mohammad Hassan Moradi

Abstract

Electronic health records (EHRs) contain critical information useful for clinical studies. Early assessment of patients' mortality in intensive care units is of great importance. In this paper, a Deep Rule-Based Fuzzy System (DRBFS) was proposed to develop an accurate in-hospital mortality prediction in the intensive care unit (ICU) patients employing a large number of input variables. Our main contribution is proposing a system, which is capable of dealing with big data with heterogeneous mixed categorical and numeric attributes. In DRBFS, the hidden layer in each unit is represented by interpretable fuzzy rules. Benefiting the strength of soft partitioning, a modified supervised fuzzy k-prototype clustering has been employed for fuzzy rule generation. According to the stacked approach, the same input space is kept in every base building unit of DRBFS. The training set in addition to random shifts, obtained from random projections of prediction results of the current base building unit is presented as the input of the next base building unit. A cohort of 10,972 adult admissions was selected from Medical Information Mart for Intensive Care (MIMIC-III) data set, where 9.31% of patients have died in the hospital. A heterogeneous ...Continue Reading

Citations

Mar 17, 2019·American Journal of Epidemiology·Susan M ShortreedJennifer C Nelson
May 18, 2020·Journal of the American Medical Informatics Association : JAMIA·Seyedeh Neelufar PayrovnaziriZhe He
Jul 22, 2020·Medical & Biological Engineering & Computing·Guang ZhangFeng Chen
Jul 28, 2019·Health Informatics Journal·Aya AwadYasser El-Sonbaty
Mar 4, 2021·Personal and Ubiquitous Computing·Naghmeh KhajehaliMohammad Jafar Tarokh
Jun 16, 2021·Artificial Intelligence in Medicine·Francisco ValenteJoão Morais
Jul 27, 2021·Medical Journal, Armed Forces India·Rashmi Datta, Shalendra Singh
Nov 30, 2021·BMC Medical Informatics and Decision Making·Ying WuXiangyu Chang

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