Abstract
It is evident that a huge amount of data is currently being generated. Across the world, 2.5 quintillion bytes of data is being recorded currently. It is almost equivalent to 0.5 Million TB or it is enough to occupy 10 Million Blue-ray disks. The amount of data is expected to surpass 44 trillion gigabytes at the end of 2020 (as compared to 4.4 trillion gigabytes during the end of 2013). The lion’s share of the data being recorded in the information systems is basically related to healthcare activities. Extracting useful information/insights from a large quantity of data is very important. Visualizing data could yield wonderful results, and summaries hidden in data, especially, visualization could do a wonderful job in health care. Data visualization saves time and conveys information more meaningfully. It is a powerful way to summarize which assists all stakeholders. This chapter presents an attempt to summarize healthcare data through exploratory data analysis and process mining control-flow discovery techniques. Exploratory data analysis of healthcare data presents a way to explore healthcare data meaningfully, and process mining based control flow visualization presents the way to extract the causal relationships between the activities of the process. Process mining way of visualizing healthcare helps in identifying the discrepancies between planned and actual healthcare processes. Final sections of this chapter present Process Mining based control flow visualizations on real-time event log detailed in healthcare information systems.
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Notes
- 1.
Fuzzy net is used to construct the process mode.
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Manoj Kumar, M.V., Prashanth, B.S., Shastry, A., Sanjay, H.A., Sneha, H.R. (2021). Healthcare Data Visualization. In: Srinivasa, K.G., G. M., S., Sekhar, S.R.M. (eds) Artificial Intelligence for Information Management: A Healthcare Perspective. Studies in Big Data, vol 88. Springer, Singapore. https://doi.org/10.1007/978-981-16-0415-7_9
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