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

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 feature set of first 48 h from ICU admissions, were extracted for in-hospital mortality rate. Required preprocessing and appropriate feature extraction were applied. To avoid biased assessments, performance indexes were calculated using holdout validation. We have evaluated our proposed method with several common classifiers including naïve Bayes (NB), decision trees (DT), Gradient Boosting (GB), Deep Belief Networks (DBN) and D-TSK-FC. The area under the receiver operating characteristics curve (AUROC) for NB, DT, GB, DBN, D-TSK-FC and our proposed method were 73.51%, 61.81%, 72.98%, 70.07%, 66.74% and 73.90% respectively. Our results have demonstrated that DRBFS outperforms various methods, while maintaining interpretable rule bases. Besides, benefiting from specific clustering methods, DRBFS can be well scaled up for large heterogeneous data sets.