Integrated Declarative Process and Decision Discovery of the Emergency Care Process

Mertens, Steven; Gailly, Frederik; Sassenbroeck, Diederik Van; Poels, Geert

doi:10.1007/s10796-020-10078-5

Cited by 8 publications

(4 citation statements)

References 81 publications

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“…EmergencyDepartment: a real-life event log containing 41657 traces (625758 activity and 1350465 data events using 116 unique activities and 951 unique data values to form traces of up to 128 activity events) about the registration, triage, diagnosis and treatment of patients in the medical emergency department of the Belgian AZ Maria Middelares hospital [18]. This dataset was not filtered by specific pathologies and therefore contains the full complexity that an emergency medicine physician must face daily, including multiple iterations of tests and examinations to reach a satisfactory diagnosis and treatment.…”

Section: A Datasetsmentioning

confidence: 99%

“…First, we developed a new declarative process and decision modeling language that could capture the rules, decision criteria and practical limitations that constitute the daily reality for physicians when treating patients (e.g., medical guidelines and available resources) [3]. We subsequently developed a corresponding discovery technique [17] and application method [18] to automate the creation of such models. The method was evaluated in a real-life emergency department and differs from the imperative approaches proposed in similar work [19]- [22] with its declarative and dataaware approach.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supporting Physicians Through Prescriptive Process Monitoring

Mertens,

Gailly

2023

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

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“…We received data from the emergency care department of a hospital, which consisted of electronic health records and all other available data (e.g., lab results and sensor data) corresponding to patients in the study. We already mined a satisfactory process & decision model from this data using a mining method centered around DeciClareMiner [17]. This model and data will be used for the evaluation of software tools proposed here.…”

Section: Conclusion and Current Statusmentioning

confidence: 99%

Personalized Process and Decision Support in Emergency Care by Mining Electronic Health Records and Sensor Data

Mertens

2020

Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

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Physicians are constantly confronted with decisions on what should be done next during the diagnosis and treatment of patients. Nonroutine medical treatment processes, for example in emergency care, are the most extreme group of such processes due to the large and diverse set of options at each decision point. Such decision making can be very demanding and even overwhelming, especially for less experienced medical personnel. The current IT infrastructure in healthcare remains lacking in support for decision making. In this research project, we lay the foundation process & decision support system that externalizes the knowledge and experience from historic data to offer personalized and process-aware decision support to physicians. The systems uses two separate but complementary components: a process engine and a prediction generator. CCS CONCEPTS• Information systems → Information retrieval • Computer systems organization → Real-time systems • Computing methodologies → Artificial intelligence → Knowledge representation and reasoning

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“…We present a method for calculating and analyzing stakeholder utilities of processes that arise in, but are not limited to, the social sciences. These areas include business process analysis [18], healthcare workflow analysis [3], [10], [13] and policy process analysis [11], [1], [6], [7]. This method is quite general and applicable to any situation in which declarativetype constraints play a part.…”

Section: Introductionmentioning

confidence: 99%

Stakeholder Utility Measures for Declarative Processes and Their Use in Process Comparisons

Dukes

2022

IEEE Trans. Comput. Soc. Syst.

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We present a method for calculating and analyzing stakeholder utilities of processes that arise in, but are not limited to, social sciences. These areas include business process analysis, healthcare workflow analysis, and policy process analysis. This method is quite general and applicable to any situation in which declarative-type constraints of a modal and/or temporal nature play a part. A declarative process is a process in which activities may freely happen while respecting a set of constraints. For such a process, anything may happen so long as it is not explicitly forbidden. Declarative processes have been used and studied as models of business and healthcare workflows by several authors. In considering a declarative process as a model of some system, it is natural to consider how the process behaves with respect to stakeholders. We derive a measure for stakeholder utility that can be applied in a very general setting. This derivation is achieved by listing a collection of properties that we argue such a stakeholder utility function ought to satisfy and then using these to show that a very specific form must hold for such a utility. The utility measure depends on the set of unique traces of the declarative process, and calculating this set requires a combinatorial analysis of the declarative graph that represents the process. This builds on previous work of Dukes and Casey (2021) in which the combinatorial diversity metrics for declarative processes were derived for use in policy process analysis. The collection of stakeholder utilities can themselves then be used to form a metric with which we can compare different declarative processes to one another. These are illustrated using several examples of declarative processes that already exist in the literature.

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Integrated Declarative Process and Decision Discovery of the Emergency Care Process

Cited by 8 publications

References 81 publications

Supporting Physicians Through Prescriptive Process Monitoring

Supporting Physicians Through Prescriptive Process Monitoring

Personalized Process and Decision Support in Emergency Care by Mining Electronic Health Records and Sensor Data

Stakeholder Utility Measures for Declarative Processes and Their Use in Process Comparisons

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