Abstract Process mining encompasses a range of methods designed to analyze event logs. Among these methods, control-flow discovery algorithms are particularly significant, as they enable the identification of real-world process models, known as in-vivo processes, in contrast to anticipated models. An obstacle faced by control-flow discovery algorithms is their limited ability to recognize duplicated activities, which are activities that occur in multiple locations within a process. This issue is particularly relevant in the healthcare sector, where numerous instances of duplicated activities exist in processes but remain undetected by conventional algorithms. This article introduces a novel concept for a control-flow discovery algorithm capable of effectively revealing duplicated activities. The effectiveness of this technique is demonstrated through experimentation on a synthetic dataset. Moreover, the algorithm has been implemented and its source code is available as open-source software, accessible both as a ProM plugin and a Java Maven dependency.

OBJECTIVE: Process Mining (PM) is an established discipline with increasing adoption in the clinical domain. In this context, PM seeks to infer clinical processes from healthcare data collected in the Electronic Health Record. However, the particularities of clinical practice cause that, in most cases, the processes obtained result in an intricate network that hardly corresponds to clinical algorithms and, thus, are difficult to understand for clinical and IT personnel. To address these problems, our aim is to incorporate specialized clinical knowledge into the PM discovery algorithm. METHODS: We propose a declarative approach to interactive process discovery in the clinical domain. Concretely, we present a set of declarative techniques that allows clinicians to incorporate their knowledge in the process, based on the Declare formalism. RESULTS: The results of this work encompass both the declarative interactive approach and its implementation in the I-PALIA PM discovery algorithm, as well as an application to a use case for the treatment of prostate cancer. This application demonstrates that the implemented techniques are useful in managing typical problems that arise when applying PM methods to the clinical domain. CONCLUSION: This work proposes a novel approach with techniques for interactive process discovery in the clinical domain. This approach not only allows the clinical expert to interactively incorporate specialized knowledge into the PM algorithm, but also serves to obtain process models that are more comprehensible and better resemble treatment procedures.

Although Internet of Things (IoT) systems are widely used in various industries, they are prone to data collection errors due to device limitations and environmental factors. These errors can significantly degrade the quality of collected data and the event log extracted from raw sensor readings, impact data analysis and lead to inaccurate or distorted results. This article emphasizes the importance of evaluating data quality and errors before proceeding with analysis. The effectiveness of three error correction methods, a rule-based method and a Process Mining (PM)-based method which are adjusted for a smart home use case, and their combination was also investigated in resolving log errors. The study found that understanding different types and sources of errors, and adapting the error correction algorithm based on this knowledge of error sources, can greatly improve the algorithm’s efficiency in addressing various error types.

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 objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

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.

Obesity is a growing problem that has reached a pandemic dimension. Diagnosis of obesity is based on the body mass index, regardless other important indicators related to metabolic impairments. One of the main problems is the difficult identification of obese subjects or in risk of developing obesity. New health information systems supporting massive amounts of physiological, treatments and lifestyle data have been proposed elsewhere, however these have also introduced a significant data and decision overload problem. In this paper we present a comparative study on data base management engines for supporting big data analytics for the identification of obese subjects based on Electronic Health Records. We compared relational and non-relational approaches to address scalability and performance in a tertiary hospital. The experiments have evaluated data from five different hospital services on a data-mart containing 20,706,947 records from the University Hospital La Fe of Valencia (Spain). Experiments where based on data load and query with different configurations and restrictions. NoSQL approach yielded better results when compared to relational engines for all the proposed experiments.

An adequate nutritional status is a major determinant of health in the elderly population. Malnutrition or risk of malnutrition are situations linked to an increase of complications, which often affects people over 60 years old. The Mini Nutritional Assessment questionnaire allows to obtain in a fast, easy and non-invasive way information related to the nutritional status. However, the solely act of assessment does not lead to an improvement in the nutritional status. In this paper we describe the implementation and evaluation of an Android based app containing the Mini Nutritional Assessment questionnaire and a set of recommended nutritional interventions based on the questionnaire score. The app was evaluated in a single-cohort study involving 154 subjects during a a period of 6 months in a nursing home. Our results confirm the effectiveness of the screening and intervention program based on the use of the app, as per an improvement in the nutritional status of subjects who where primarily malnourished or at risk of malnutrition. The usability, perceived usefulness and perceived savings of the app was evaluated by means of an adapted System Usability Score questionnaire on ten professionals participating in the study, showing high levels of acceptance and potential savings.

The CrowdHEALTH project aims at delivering an integrated platform that provides decision support to public health authorities for policy creation through the exploitation of collective knowledge that emerges from multiple information sources. The latter will be realized through proposed Social Holistic Health Records - SHHRs. CrowdHEALTH will provide policy makers with the means of processing large amount of healthcare information (including diseases, root causes, risk factors and patient data across different geographical areas and timescales) from a single-entry point.

Malnutrition is one of the major geriatric syndromes and frailty factor, this joint with the fact of elderly population growing, will situate malnutrition as a front end problem in the upcoming years. Therefore, it is important that health professionals can assess and follow up nutritional status in a proper way, using all available data related to patients. Process mining can be used to extract knowledge from information in order to understand health care processes. A classic approach to assess malnutrition usually comprises anthropometric measures as static variables, with no information about patients evolution and pathways. The aim of this work was to examine anthropometric measures from a dynamic perspective thanks to process mining tools, in order to obtain dynamic behaviour models. This paper proposes a method based on the use of process mining to discover and identify weight changes behaviour. Clustering is used as part of the pre-processing of data to manage variability, and then process mining is used to identify patterns of patients' behaviour. The method is applied through different experiments to data from 96 patients. Results grouped almost all individuals in different models based on common behaviours. Main finding shows different behaviour groups seem to have different results regarding malnutrition status for same interventions. By discovering patterns of dynamic weight change and their relation with malnutrition, nursing homes and health care professional can promote more successful intervention among patients based on their behaviour, moreover they can compare interventions' results analysing changes in behaviour between before and after the intervention.

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.