DYNAMITE, an acronym for DYNamic Archetypal analysis for MIning disease TrajEctories, is a new methodology developed specifically to model disease progression by exploiting information available in longitudinal clinical datasets. First, archetypal analysis is applied to data organised in matrix form, with the aim of finding extreme and representative disease states (archetypes) linked to the original data through convex coefficients. Then, each original observation is associated with a single archetype based on their similarity; finally, an event log is created encoding the progression of disease states for each patient in terms of archetype states. In the last stage of the procedure, archetypal analysis is coupled with process mining, which allows the event log archetypes to be visualised graphically as sequences of disease states, allowing the clinical trajectories of patients to be extracted and examined. As a proof of concept, we applied the proposed method to data from a cohort of amyotrophic lateral sclerosis patients whose progression was monitored using the 12-item ALSFRS-R questionnaire. Without any a priori knowledge, DYNAMITE identified six archetypes clearly describing different types and severity of impairment and provided reliable clinical trajectories consistent with the prognosis of amyotrophic lateral sclerosis patients. DYNAMITE offers high interpretability at every stage of the analysis, which makes it particularly suitable for use in healthcare where explainability is paramount, and enables analysis of clinical trajectories at both individual and population levels.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Process Mining is of growing importance in the healthcare domain, where the quality of delivered services depends on the suitable and efficient execution of processes encoding the vast amount of clinical knowledge gained via the evidence-based medicine paradigm. In particular, to assess and measure the quality of delivered treatments, there is a strong interest in tools able to perform conformance checking. In process mining for the healthcare domain, a number of major challenges are posed by: (i) the complexity of involved data, that refers to patients' aspects such as disease, behaviour, clinical history, psychology, etc; (ii) the availability of data, that come from the heterogeneous, fragmented and scant connected healthcare system; and (iii) the wide range of available standards for communication (DICOM, IHE, etc.) or data representation (ICD9, SNOMED, etc.) purposes. To effectively perform process mining in the healthcare domain, it is crucial to build event logs capturing all the steps of running processes, which have to be derived by the knowledge stored in the Electronic Health Records. It is therefore crucial to cope with aforementioned data-related challenges. In this paper, we aim at supporting the exploitation of process mining in the healthcare domain, particularly with regards to conformance checking. We therefore introduce a set of specifically-designed techniques, provided as a suite of software packages written in R. In particular, the suite provides a flexible and agile way to automatically and reliably build Event Log from clinical data sources, and to effectively perform conformance checking.

Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated.