Smartphones enable to collect large data streams about phone calls that, once combined with Computational Intelligence techniques, bring great potential for improving the monitoring of patients with mental illnesses. However, the acoustic data streams recorded in uncontrolled environments are dynamically changing due to various sources of uncertainty. In addition, such acoustic data are usually difficult to interpret by psychiatrists. Within this study, we propose an approach based on Linguistic Summaries with Fuzzy Clustering (LS-FC) aiming at the development of human-consistent and easily interpretable summaries about relations between acoustic data and mental state of a patient affected by Bipolar Disorder, e.g., Most calls in the state of hypomania have low loudness compared to the state of euthymia [T = 1]. To capture the dynamics of acoustic data streams, we apply a dynamic incremental semi-supervised fuzzy clustering that synthesizes data into clusters. These clusters are represented by prototypes which are used for the construction of the membership functions describing linguistic terms e.g., low loudness, and then, linguistic summaries. The main contribution of this paper is the incorporation of information about clusters’ prototypes in the generation of linguistic summaries. The primary goal of this research is explainability. The semi-supervised learning algorithm is used mainly for deriving clusters and building improved linguistic summaries. Numerical results indicate that linguistic summaries provide intuitive and clear information about voice features in a patient’s affective state and they are consistent with clinical observation. In particular, during most calls in hypomania/mania both the quality of the patient’s voice and the dynamics of change in the spectrum signal reflected in spectral flux are low compared to euthymia. The proposed approach enables to summarize large data streams into meaningful descriptions that, although relatively simple, offer information granules that are very intuitive for clinicians and are promising to support the smartphone-based monitoring of bipolar disorder patients to inform about the potential change of mental state.

We introduce an approach called PLENARY (exPlaining bLack-box modEls in Natural lAnguage thRough fuzzY linguistic summaries), which is an explainable classifier based on a data-driven predictive model. Neural learning is exploited to derive a predictive model based on two levels of labels associated with the data. Then, model explanations are derived through the popular SHapley Additive exPlanations (SHAP) tool and conveyed in a linguistic form via fuzzy linguistic summaries. The linguistic summarization allows translating the explanations of the model outputs provided by SHAP into statements expressed in natural language. PLENARY accounts for the imprecision related to model outputs by summarizing them into simple linguistic statements and for the imprecision related to the data labeling process by including additional domain knowledge in the form of middle-layer labels. PLENARY is validated on preprocessed speech signals collected from smartphones from patients with bipolar disorder and on publicly available mental health survey data. The experiments confirm that fuzzy linguistic summarization is an effective technique to support meta-analyses of the outputs of AI models. Also, PLENARY improves explainability by aggregating low-level attributes into high-level information granules, and by incorporating vague domain knowledge into a multi-task sequential and compositional multilayer perceptron. SHAP explanations translated into fuzzy linguistic summaries significantly improve understanding of the predictive modelling process and its outputs.

Predictive process monitoring (PPM) techniques have become a key element in both public and private organizations by enabling crucial operational support of their business processes. Thanks to the availability of large amounts of data, different solutions based on machine and deep learning have been proposed in the literature for the monitoring of process instances. These state-of-the-art approaches leverage accuracy as main objective of the predictive modeling, while they often neglect the interpretability of the model. Recent studies have addressed the problem of interpretability of predictive models leading to the emerging area of Explainable AI (XAI). In an attempt to bring XAI in PPM, in this paper we propose a fully interpretable model for outcome prediction. The proposed method is based on a set of fuzzy rules acquired from event data via the training of a neuro-fuzzy network. This solution provides a good trade-off between accuracy and interpretability of the predictive model. Experimental results on different benchmark event logs are encouraging and motivate the importance to develop explainable models for predictive process analytics.

Predictive process monitoring has recently become one of the main enablers of data-driven insights in process mining. As an application of predictive analytics, process prediction is mainly concerned with predicting the evolution of running traces based on models extracted from historical event logs. This paper presents a process mining approach, which uses convolutional neural networks to equip the execution scenario of a business process with a means to predict the next activity in a running trace. The basic idea is to convert the temporal data enclosed in the historical event log of a business process into spatial data so as to treat them as images. To this purpose, every trace of the event log is first transformed into the set of its prefix traces (i.e. sequences of events that represent the prefix of a trace). These prefix traces are mapped into 2D image-like data structures. Created spatial data are finally used to train a Convolutional Neural Network, in order to learn a deep learning model capable to predict the next activity (i.e. the activity associated to the event occurring after the last event in the considered prefix trace). This predictive deep model can be employed as a powerful service to support participants in performing business processes since it guarantees a higher utilization by acting proactively in anticipation. Preliminary tests with two benchmark logs are carried out to investigate the viability of the proposed approach.