The widespread use of information technology in healthcare leads to extensive data
collection, which can be utilised to enhance patient care and manage chronic illnesses. Our …

This perspective article explores how process mining can extract clinical insights from
mobile health data and complement data-driven techniques like machine learning. Despite …

Despite the creation of thousands of machine learning (ML) models, the promise of
improving patient care with ML remains largely unrealized. Adoption into clinical practice is …

Recent advances in the field of artificial intelligence (AI) could yield new insights into the
potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI …

Abstract Background Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease
whose spreading and progression mechanisms are still unclear. The ability to predict ALS …

Most process mining techniques are primarily automated, meaning that process analysts
input information and receive output. As a result, process mining techniques function like …

Process mining is a well-established discipline with applications in many industry sectors,
including healthcare. To date, few publications have considered the context in which …

This chapter introduces a specific application domain of process mining: healthcare.
Healthcare is a very promising domain for process mining given the significant societal …

Process mining can help acquire insightful knowledge and heighten the system's
performance. In this study, we surveyed the trajectories of 1050 sepsis patients in a regional …

Business Process Management (BPM) has been increasingly used in recent years in the
healthcare domain to analyze, optimize, harmonize and compare clinical and healthcare …