XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs

Prescriptive Business Process Monitoring for Recommending Next Best Actions

Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN`s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique`s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

A factorial randomized controlled trial to evaluate the effect of micronutrients supplementation and regular aerobic exercise on maternal endothelium-dependent vasodilatation and oxidative stress of the newborn

<p>Abstract</p> <p>Background</p> <p>Many studies have suggested a relationship between metabolic abnormalities and impaired fetal growth with the development of non-transmissible chronic diseases in the adulthood. Moreover, it has been proposed that maternal factors such as endothelial function and oxidative stress are key mechanisms of both fetal metabolic alterations and subsequent development of non-transmissible chronic diseases. The objective of this project is to evaluate the effect of micronutrient supplementation and regular aerobic exercise on endothelium-dependent vasodilation maternal and stress oxidative of the newborn.</p> <p>Methods and design</p> <p>320 pregnant women attending to usual prenatal care in Cali, Colombia will be included in a factorial randomized controlled trial. Women will be assigned to the following intervention groups: <it>1. Control group: </it>usual prenatal care (PC) and placebo (maltodextrine). <it>2. Exercise group: </it>PC, placebo and aerobic physical exercise. <it>3. Micronutrients group: </it>PC and a micronutrients capsule consisting of zinc (30 mg), selenium (70 μg), vitamin A (400 μg), alphatocopherol (30 mg), vitamin C (200 mg), and niacin (100 mg)<it>. 4. Combined interventions Group: </it>PC, supplementation of micronutrients, and aerobic physical exercise. Anthropometric measures will be taken at the start and at the end of the interventions.</p> <p>Discussion</p> <p>Since in previous studies has been showed that the maternal endothelial function and oxidative stress are related to oxidative stress of the newborn, this study proposes that complementation with micronutrients during pregnancy and/or regular physical exercise can be an early and innovative alternative to strengthen the prevention of chronic diseases in the population.</p> <p>Trial registration</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT00872365">NCT00872365</a>.</p

Altered Cerebellar-Cerebral Functional Connectivity in Geriatric Depression

Although volumetric and activation changes in the cerebellum have frequently been reported in studies on major depression, its role in the neural mechanism of depression remains unclear. To understand how the cerebellum may relate to affective and cognitive dysfunction in depression, we investigated the resting-state functional connectivity between cerebellar regions and the cerebral cortex in samples of patients with geriatric depression (n = 11) and healthy controls (n = 18). Seed-based connectivity analyses were conducted using seeds from cerebellum regions previously identified as being involved in the executive, default-mode, affective-limbic, and motor networks. The results revealed that, compared with controls, individuals with depression show reduced functional connectivity between several cerebellum seed regions, specifically those in the executive and affective-limbic networks with the ventromedial prefrontal cortex (vmPFC) and increased functional connectivity between the motor-related cerebellum seed regions with the putamen and motor cortex. We further investigated whether the altered functional connectivity in depressed patients was associated with cognitive function and severity of depression. A positive correlation was found between the Crus II–vmPFC connectivity and performance on the Hopkins Verbal Learning Test-Revised delayed memory recall. Additionally, the vermis–posterior cinglate cortex (PCC) connectivity was positively correlated with depression severity. Our results suggest that cerebellum–vmPFC coupling may be related to cognitive function whereas cerebellum–PCC coupling may be related to emotion processing in geriatric depression

Explainability in Predictive Process Monitoring: When Understanding Helps Improving

none3Predictive business process monitoring techniques aim at making predictions about the future state of the executions of a business process, as for instance the remaining execution time, the next activity that will be executed, or the final outcome with respect to a set of possible outcomes. However, in general, the accuracy of a predictive model is not optimal so that, in some cases, the predictions provided by the model are wrong. In addition, state-of-the-art techniques for predictive process monitoring do not give an explanation about what features induced the predictive model to provide wrong predictions, so that it is difficult to understand why the predictive model was mistaken. In this paper, we propose a novel approach to explain why a predictive model for outcome-oriented predictions provides wrong predictions, and eventually improve its accuracy. The approach leverages post-hoc explainers and different encodings for identifying the most common features that induce a predictor to make mistakes. By reducing the impact of those features, the accuracy of the predictive model is increased. The approach has been validated on both synthetic and real-life logs.noneWilliams Rizzi; Chiara Di Francescomarino; Fabrizio Maria MaggiRizzi, Williams; Di Francescomarino, Chiara; Maria Maggi, Fabrizi