Process mining is the discipline of analyzing and improving processes which are known as an event log. The real-life event log contains noise, infrequent behaviors, and numerous concurrency, in effect the generated process model through process discovery algorithms will be inefficient and complex. Shortcomings in an event log result in current process discovery algorithms failing to pre-process data and describe real-life phenomena. Existing process mining algorithms are limited based on the algorithm's filtering, parameters, and pre-defined features. It is critical to use a high-quality event log to generate a robust process model. However, pre-processing of the event log is mostly cumbersome and is a challenging procedure. In this paper, we propose a novel pre-processing step aimed to obtain superior quality event log from a set of raw data, consequently a better performing process model. The proposed approach concatenates events which hold concurrent relations based on a probability algorithm, producing simpler and accurate process models. This proposed pre-processing step is based on the probability of the frequency of concurrent events. The performance of the pre-processing approach is evaluated on 18 real-life benchmark datasets that are publicly available. We show that the proposed pre-processing framework significantly reduces the complexity of the process model and improves the model's F-Measure.INDEX TERMS Process mining, concurrent relations, concatenation, pre-processing methods, probability.
Prediction of the next event is important for organizations to improve and optimize their system process to achieve organizational goals. Existing predictive models are limited since they use discovery algorithms that might not be able to conserve the sequences of events as reported in the event log. Discovery algorithms alter the sequence of events in two ways, either the algorithms generate an additional sequence of events not found in the event logs or remove the order of events. Since prediction relies on these process algorithms, the prediction model can suffer and produce underperforming results. Models that do not use discovery algorithms, such as deep learning models, ignore completely the sequence of events. To overcome these limitations, we propose a new algorithm called AXDP (Adjacency Matrix Deep Learning Prediction Model). AXDP predicts the next event of a process using graph theory techniques, specifically adjacency matrices and predicts using the power of deep learning models. AXDP has a major advantage, in that sequence of events is conserved, resulting in better prediction of the next event. When testing AXDP on eight publicly available datasets, AXDP outperforms what we believe to be the most recent and best predictive models that exist for the prediction of the next event for six of the eight datasets.INDEX TERMS Deep learning, process mining, event logs, adjacency matrix, max eigenvalues.
Background: Paralytic Ileus (PI) patients in the Intensive Care Unit (ICU) are at significant risk of death. Prediction of at-risk patients for mortality after 24 hours of admission of ICU PI patients is important to increase the life expectancy of PI patients. Methods and Results: The proposed framework, DLMP(Deep Learning Model for Mortality Prediction of ICU Patients with PI) is a powerful deep learning model consisting of six total unique clinical lab items and two demographics as inputs to a Neural Network(NN) of only two neuron layers. Using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 1,017 ICU PI patients, the DLMP resulted in the best prediction performance with an AUC score of 0.866. Conclusion: The proposed approach is capable of modeling the mortality of ICU patients after 24 hours admission using only six unique total clinical data and two demographics with a simple NN architecture. DLMP framework significantly improves the outcome prediction compared to the process mining and machine learning models. The proposed DLMP has the potential of allowing clinicians to create targeted interventions that reduce mortality for PI patients in an ICU setting.
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