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Digital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital
Abstract Whilst digital health data provides great benefits for improved and effective patient care and organisational outcomes, the quality of digital health data can sometimes be a significant issue. Healthcare providers are known to spend a significant amount of time on assessing and cleaning data. To address this situation, this paper presents six Digital Health…
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Collaborative and Interactive Detection and Repair of Activity Labels in Process Event Logs
Abstract Process mining uses computational techniques for process-oriented data analysis. The use of poor quality input data will lead to unreliable analysis outcomes (garbage in – garbage out), as it does for other types of data analysis. Among the key inputs to process mining analyses are activity labels in event logs which represent tasks that…
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A Contextual Approach to Detecting Synonymous and Polluted Activity Labels in Process Event Logs
Abstract Process mining, as a well-established research area, uses algorithms for process-oriented data analysis. Similar to other types of data analysis, the existence of quality issues in input data will lead to unreliable analysis results (garbage in – garbage out). An important input for process mining is an event log, which is a record of…