Exploring the application of process mining: process model discovery, data generation, and event prediction
Creators: name: Li, Keyi
Publisher: name: No Publisher Supplied
Year: 2024

description: Process mining is a prominent research field that has emerged in recent years. It offers substantial benefits in decision-making, efficiency enhancement, and error reduction across various real-world domains such as healthcare, business, and education. Process mining field mainly analyzes process data, typically stored in event logs, which capture case IDs, activities, and other chronological context. Processes exhibit variability based on different use cases or contexts. Researchers apply process mining techniques to extract non-trivial knowledge from the process data. However, a prevalent issue in this field is in the limited publicly available datasets that lack the complexity and diversity of real-world scenarios, which limited the development of generalized process mining methods and their practical applicability to complex problems. This dissertation aims to address this gap by exploring process mining applications across datasets of varying complexity. We proposed novel methods to enhance the representation of complex process data, mitigate challenges associated with limited data, and support human decision-making. Specifically, the dissertation presents three key contributions: (1) a method for discovering interpretable process model; (2) a deep generative model for augmenting process dataset; and (3) deep learning-based models for event prediction based on process data. The effectiveness of these methods is demonstrated through experiments conducted in real-world medical settings, such as trauma resuscitation, where they substantially improved medical providers' decision-making processes. Although the focus of this dissertation is on medical processes, the approaches developed are applicable to similar domains that using process datasets. First, we introduced a process model discovery method for complex medical processes. To develop a medical process model that improves patient outcomes, understanding the actual work (i.e., “work-as-done”) rather than theorized work (i.e., “work-as-imagined”) is critical. When applied to complex process data, traditional process model discovery methods often omits critical steps or produces cluttered and unreadable models. To address these limitations, we introduced a Trace Alignment-based Process Discovery method called TAD Miner to build interpretable process models. TAD Miner creates simple linear process models using a threshold metric that optimizes the consensus sequence to represent the backbone process, and identifies both concurrent activities and uncommon-but-critical activities to represent the side branches. TAD Miner also identifies the locations of repeated activities, which is an essential feature for representing medical treatment steps. We used the TAD process models to identify (1) the errors and (2) the best locations for the tentative steps in knowledge-driven expert models. The knowledge-driven models were revised based on the modifications suggested by the discovered models. The improved modeling using TAD Miner may enhance understanding of complex medical processes. Second, we introduced a data augmentation model for synthetic process data generation. Because process data with confidential information cannot be shared, research is limited using process data and analytics in the process mining domain. We introduced a generative adversarial network, called ProcessGAN, to address the limitation of shareable process data. Our model generates process data that consists of activity sequences and corresponding timestamps. ProcessGAN consists of a transformer-based network as the generator, and a time-aware self-attention network as the discriminator. It can generate privacy-preserving process data from random noise. ProcessGAN considers the duration of the process and time intervals between activities to generate realistic activity sequences with timestamps. To evaluate the synthetic data, we proposed statistical metrics and trained a supervised model to score the synthetic processes. We also applied our TAD Miner to discover process models (i.e., workflow diagrams) for synthetic medical processes and had domain experts evaluate the clinical applicability of the synthetic diagrams. ProcessGAN outperformed the existing generative models in generating complex processes with valid parallel pathways, and better represented the long-range dependencies between activities. The timestamps generated by the ProcessGAN model showed similar distributions and trends with the authentic timestamps. ProcessGAN can generate shareable synthetic process data indistinguishable from authentic data. Last, we introduced deep learning-based event prediction models specifically to assist the decision-making in the medical process. We introduced (1) a treatment activity recommendation model, and (2) a goal recognition model. Both models have the potential to improve workflow, reduce team errors, and improve patient outcomes. The treatment activity recommendation model aggregates patient context (e.g., injury features and physiological values) and process context (e.g., already performed activities) as input, and continuously predicts the activities to be performed. Our model is built on a tower-structured model of multi-layer perceptrons. It achieved a 0.89 F1-score on the sepsis activity predictions and 0.65 F1-score on the trauma resuscitation activity predictions, showing the feasibility of predicting activities using context and process data. The goal recognition model aims to track whether certain goals are being pursued by the medical providers throughout a medical process. Our experiments focused on recognizing the providers' goal of (1) airway stabilization and (2) circulatory support, which are two critical goals in trauma resuscitation. We designed a dual-gated recurrent unit (GRU) architecture to learn the contributions from timestamp and activity types for goal recognition. Our model achieved an average AUC score of 0.84 for airway stabilization and 0.83 for circulatory support. The results are explainable by identifying the significant time windows and the most contributory activities in deciding whether the goal is being pursuit. Our model outperformed existing medical event predictive models in both accuracy and interpretability. Integrating our model into a decision support system would automate the tracking of providers’ actions, optimizing workflow to ensure timely delivery of patient care.