Computing and Information Systems - Research Publications
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ItemProcess Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients( 2021-12-08)Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining tools in different contexts, including healthcare. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we report the analysis of an event log recording more than 30 million events capturing the general practice healthcare processes of more than one million patients in Victoria–Australia–over five years. Our analysis allowed us to understand benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable processes and large data-sets. While we provide solutions to the identified limitations, the overarching goal of this study was to detect differences between the patients‘ health services utilization pattern observed in 2020– during the COVID-19 pandemic and mandatory lock-downs –and the one observed in the prior four years, 2016 to 2019. By using a combination of process mining techniques and traditional data mining, we were able to demonstrate that vaccinations in Victoria did not drop drastically–as other interactions did. On the contrary, we observed a surge of influenza and pneumococcus vaccinations in 2020, contradicting research findings of similar studies conducted in different geographical areas.
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ItemA comparative analysis of sepsis digital phenotyping methods(ACM, 2021-02)Health data captured in Electronic health records (EHRs) have enabled the development of computational approaches to improve patient management and treatment, including early diagnosis of severe conditions such as sepsis. The validity of these efforts, however, largely relies on which sepsis definition is used and the quality of the underlying data. Here we tested different sepsis definitions to better understand how phenotyping approaches may impact the classification accuracy of sepsis prediction algorithms. To assess the extent to which sepsis definitions (dis)agree with each other, we have analised a large cohort of patients admitted to the ICU (over 22,000) from MIMIC-IV. Cases were classified as septic and non-septic using the Sepsis-3 definition as a standard and compared with different ICD-10-based sepsis phenotyping criteria. Most of administrative sepsis definitions agreed with each other when identifying positive sepsis cases. At the same time, we identified considerable disagreement between Sepsis-3 and administrative definitions. This discrepancy affected machine learning algorithms’ predictive performance. Two algorithms out of three built on Sepsis-3 outperformed models based on other phenotypes. Experiments demonstrate that phenotype definitions can significantly influence a predictive model performance. This highlights the importance of consistent and validated digital phenotyping criteria.