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- W4319318988 abstract "In this study, we introduce a novel framework for Deep Fuzzy Rule-Based System (DFRBS) and its extensions and demonstrate its capability in the medical prognostic, brain connectivity estimation and depression severity staging tasks. This model benefits from a hierarchical fuzzy rule-based system and is capable of fusing heterogeneous decisions in an interpretable manner utilizing a stacked approach. The hidden layer in each unit is represented by interpretable fuzzy rules and fuzzy rules are generated by means of the soft partitioning approach, i.e., a modified FCM clustering. According to the stacked method, the same input space is kept in all layers of DFRBS. The inputs of the middle units are constructed from the training set and additive random shifts, obtained from random projections of predictions of the previous layer. As a result, DFRBS can properly handle heterogeneous, imbalanced multivariate inputs. DFRBS is applied to Electronic Health Records (EHRs) for in-hospital mortality prediction. In addition, its modified version with first-order TSK fuzzy rules as the cores of the network is applied to EEG signals for calculation of brain effective connectivity in four-fold consciousness states. Another extended version of DFRBS is employed for multiclass classification of depression severity staging. In the cases of mortality prediction and depression severity staging, our results are demonstrated that DFRBS outperforms various common methods while maintaining interpretable rule bases and our results in case of connectivity estimation demonstrate that the proposed non-linear connectivity estimator is capable of detecting novel correlates of consciousness." @default.
- W4319318988 created "2023-02-08" @default.
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- W4319318988 date "2023-01-01" @default.
- W4319318988 modified "2023-09-29" @default.
- W4319318988 title "A Novel Approach for Non-linear Deep Fuzzy Rule-Based Model and Its Applications in Biomedical Analyses" @default.
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- W4319318988 doi "https://doi.org/10.1007/978-3-031-22371-6_4" @default.
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