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Titlebook: Interactive Process Mining in Healthcare; Carlos Fernandez-Llatas Book 2021 Springer Nature Switzerland AG 2021 Machine Learning.Clinical

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樓主: industrious
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發(fā)表于 2025-3-23 09:53:11 | 只看該作者
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發(fā)表于 2025-3-23 16:15:32 | 只看該作者
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發(fā)表于 2025-3-23 18:59:42 | 只看該作者
Process Mining in Healthcarein literature [., ., .]. Data-driven paradigms are named to be a feasible solution in this field that can support medical experts in their daily decisions [.]. Behind this paradigm, there are frameworks specifically designed for dealing with process-oriented problems. This is the case of process min
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發(fā)表于 2025-3-24 01:12:19 | 只看該作者
Data Quality in Process Miningnderstand their processes in order to improve them. In this respect, process mining has the unique potential to retrieve process-related insights from process execution data. Despite the wide range of algorithms that have been developed over the past decade, the reliability of process mining outcome
15#
發(fā)表于 2025-3-24 04:55:46 | 只看該作者
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發(fā)表于 2025-3-24 10:29:03 | 只看該作者
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發(fā)表于 2025-3-24 14:32:53 | 只看該作者
Bringing Interactive Process Mining to Health Professionals: Interactive Data Rodeosot only leverage the presence of the expert in the loop but also, achieve the engagement of the expert by providing process views that can be human readable in the easiest possible way. In this chapter, we analyze some available frameworks, techniques, and algorithms in the process mining literature
18#
發(fā)表于 2025-3-24 15:31:37 | 只看該作者
Interactive Process Mining in Practice: Interactive Process Indicators them to understand their processes. Experts usually used number-based Key Performance Indicators to have an idea of the behavior of the deployed protocols. In this chapter, we propose Interactive Process Indicators (IPIs) as a solution to the problem of the exaggerated reduction of a process to a n
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發(fā)表于 2025-3-24 19:01:55 | 只看該作者
20#
發(fā)表于 2025-3-25 01:50:54 | 只看該作者
Interactive Process Mining in Surgery with Real Time Location Systems: Interactive Trace Correctionlyzing models in hospitals. However, noisy environments and building structures can interfere with RTLS signals and can affect their accuracy. This noise produces an undesirable effect on Process Mining algorithms called .. This effect dramatically decreases the human understandability of Workflows
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