Time prediction on multi-perspective declarative business processes

Ramirez, Andres J.; Barba, Irene; Fernández-Olivares, Juan; Valle, Carmelo Del; Weber, Barbara

doi:10.1007/s10115-018-1180-3

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…In addition, we will extend the proposed approach by enhancing it with the data perspective as well. Finally, we plan to improve the presented tool in order to (1) enable features like optimization [11], recommendations [3], and predictions [12], and (2) make it stable enough to share it as an open source project.…”

Section: Discussionmentioning

confidence: 99%

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

Ramirez

Barba

Reichert

et al. 2018

Advanced Information Systems Engineering

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For more than a decade, the interest in aligning information systems in a process-oriented way has been increasing. To enable operational support for business processes, the latter are usually specified in an imperative way. The resulting process models, however, tend to be too rigid to meet the flexibility demands of the actors involved. Declarative process modeling languages, in turn, provide a promising alternative in scenarios in which a high level of flexibility is demanded. In the scientific literature, declarative languages have been used for modeling rather simple processes or synthetic examples. However, to the best of our knowledge, they have not been used to model complex, real-world scenarios that comprise constraints going beyond control-flow. In this paper, we propose the use of a declarative language for modeling a sophisticated healthcare process scenario from the real world. The scenario is subject to complex temporal constraints and entails the need for coordinating the constraint-based interactions among the processes related to a patient treatment process. As demonstrated in this work, the selected real process scenario can be suitably modeled through a declarative approach.

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Section: Discussionmentioning

confidence: 99%

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

Ramirez

Barba

Reichert

et al. 2018

Advanced Information Systems Engineering

Self Cite

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“…However, some papers also include some additional data as input. We identified three additional inputs based on the previously executed activities: the time between them [43], the resources utilized [34], [35], [45]- [47] and the contextual data generated during [30], [34], [38]- [40], [45], [47]- [49]. When focusing solely on the next-activity prediction subdomain, the use of these additional forms of input is scattered and the impact of different representations (e.g., one-hot, timeseries, embeddings, etc.)…”

Section: B Input Encodingmentioning

confidence: 99%

Supporting Physicians Through Prescriptive Process Monitoring

Mertens,

Gailly

2023

2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)

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Physicians face increasing pressure to provide effective medical treatment. The complexity of the healthcare system, which is dynamic, multi-disciplinary, flexible, humancentered, and knowledge-intensive, makes it difficult for physicians to handle this pressure. Existing support systems for physicians often only provide basic documentation capabilities. Predictive process monitoring, a new area of research, offers techniques that can predict the future behavior of ongoing medical processes. This paper goes one step further by exploring the use of these predictions to make recommendations to physicians. We compare different neural network types, topologies, and input encodings with relevant benchmark techniques to identify the best options for next-activity recommendations in healthcare, based on accuracy, consistency, training speed, and prediction speed. We use data from three healthcare processes, including a real-life emergency medicine department, to evaluate the performance of these techniques.

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“…More broadly, the problem of ensuring that systems fulfill a given set of specifications has been widely addressed in model checking and process mining research. The systems under consideration can range from electronic circuits and communication protocols [33,29], to business processes [23,15] and even biological systems [18].…”

Section: Related Workmentioning

confidence: 99%

Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow

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Manataki

Papanastasiou

et al. 2021

Biomedical Engineering Systems and Technologies

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Intelligent systems play an increasingly central role in healthcare systems worldwide. Nonetheless, operational friction represents an obstacle to full utilization of scarce resources and improvement of service standards. In this paper we address the challenge of developing data-driven models of complex workflow systems -a prerequisite for harnessing intelligent technologies for workflow improvement. We present a proof-of-concept model parametrized using real-world data and constructed based on domain knowledge from the Royal Infirmary of Edinburgh, demonstrating how off-the-shelf process mining, machine learning and stochastic process modeling tools can be combined to build predictive models that capture complex control flow, constraints, policies and guidelines.

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Time prediction on multi-perspective declarative business processes

Cited by 10 publications

References 30 publications

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

Supporting Physicians Through Prescriptive Process Monitoring

Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow

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