Browsing by Author "Martinez-Millana, Antonio"

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    Analyzing medical emergency processes with process mining: the stroke case
    (2019) Fernandez-Llatas, Carlos; Ibanez-Sanchez, Gema; Celda, Angeles; Mandingorra, Jesus; Aparici-Tortajada, Lucia; Martinez-Millana, Antonio; Munoz-Gama, Jorge; Sepúlveda, Marcos; Rojas, Eric; Gálvez, Víctor; Capurro, Daniel; Traver, Vicente
    Medical emergencies are one of the most critical processes that occurs in a hospital. The creation of adequate and timely triage protocols, can make the difference between the life and death of the patient. One of the most critical emergency care protocols is the stroke case. This disease demands an accurate and quick diagnosis for ensuring an immediate treatment in order to limit or even, avoid, the undesired cognitive decline. The aim of this paper is perform an analysis of how Process Mining techniques can support health professionals in the interactive analysis of emergency processes considering critical timing of Stroke, using a Question Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we have used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January of 2010 to June of 2017. Our results demonstrate how Process Mining technology can highlight the differences of the stroke patient flow in emergency, supporting professionals in the better understanding and improvement of quality of care.
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    Individual Behavior Modeling with Sensors Using Process Mining
    (2019) Dogan, Onur; Martinez-Millana, Antonio; Rojas, Eric; Sepúlveda Fernández, Marcos Ernesto; Muñoz Gama, Jorge; Traver, Vicente; Fernandez-Llatas, Carlos
    Understanding human behavior can assist in the adoption of satisfactory healthinterventions and improved care. One of the main problems relies on the definition of humanbehaviors, as human activities depend on multiple variables and are of dynamic nature. Althoughsmart homes have advanced in the latest years and contributed to unobtrusive human behaviortracking, artificial intelligence has not coped yet with the problem of variability and dynamismof these behaviors. Process mining is an emerging discipline capable of adapting to the nature ofhigh-variate data and extract knowledge to define behavior patterns. In this study, we analyze datafrom 25 in-house residents acquired with indoor location sensors by means of process miningclustering techniques, which allows obtaining workflows of the human behavior inside the house.Data are clustered by adjusting two variables: the similarity index and the Euclidean distancebetween workflows. Thereafter, two main models are created: (1) a workflow view to analyze thecharacteristics of the discovered clusters and the information they reveal about human behaviorand (2) a calendar view, in which common behaviors are rendered in the way of a calendarallowing to detect relevant patterns depending on the day of the week and the season of the year.Three representative patients who performed three different behaviors: stable, unstable, and complexbehaviors according to the proposed approach are investigated. This approach provides humanbehavior details in the manner of a workflow model, discovering user paths, frequent transitionsbetween rooms, and the time the user was in each room, in addition to showing the results into thecalendar view increases readability and visual attraction of human behaviors, allowing to us detectpatterns happening on special days.