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The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.

Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents&rsquo, health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident&rsquo, s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community? This research received no external funding

[EN] There are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.

Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining. ispartof: Artificial Intelligence In Medicine vol:109 ispartof: location:Netherlands status: Published online

Clinical Guidelines, medical protocols, and other healthcare indications, cover a significant slice of physicians daily routine, as they are used to support clinical choices also with relevant legal implications. On the one hand, informatics have proved to be a valuable mean for providing formalisms, methods, and approaches to extend clinical guidelines for better supporting the work performed in the healthcare domain. On the other hand, due to the different perspectives that can be considered for addressing similar problems, it lead to an undeniable fragmentation of the field. It may be argued that such fragmentation did not help to propose a practical, accepted, and extensively adopted solutions to assist physicians. As in Process Mining as a general field, Process Mining for Healthcare inherits the requirement of Conformance Checking. Conformance Checking aims to measure the adherence of a particular (discovered or known) process with a given set of data, or vice-versa. Due to the intuitive similarities in terms of challenges and problems to be faced between conformance checking and clinical guidelines, one may be tempted to expect that the fragmentation issue will naturally arise also in the conformance checking field. This position paper is a first step on the direction to embrace experience, lessons learnt, paradigms, and formalisms globally derived from the clinical guidelines challenge. We argue that such new focus, joint with the even growing notoriety and interest in PM4HC, might allow more physicians to make the big jump from user to protagonist becoming more motivated and proactive in building a strong multidisciplinary community.

[EN] The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care., This research was funded by Hospital General de Valencia thanks to the LOPEZ TRIGO 2017 AWARD and by the CONICYT grant REDI 170136 Project. The APC was funded by the APE/2019/007 (D.O.G.V. 8355/06.08.2018 Annex XIII).

[EN] Background Expressing anthropometric parameters (height, weight, BMI) as z-score is a key principle in the clinical assessment of children and adolescents. The Centre for Disease Control and Prevention (CDC) growth charts and the CDC-LMS method for z-score calculation are widely used to assess growth and nutritional status, though they can be imprecise in some percentiles. Objective To improve the accuracy of z-score calculation by revising the statistical method using the original data used to develop current z-score calculators. Design A Gaussian Process Regressions (GPR) was designed and internally validated. Z-scores for weight-for-age (WFA), height-for-age (HFA) and BMI-for-age (BMIFA) were compared with WHO and CDC-LMS methods in 1) standard z-score cut-off points, 2) simulated population of 3000 children and 3) real observations 212 children aged 2 to 18 yo. Results GPR yielded more accurate calculation of z-scores for standard cut-off points (p<, The study presented in this paper was developed in the context of the MyCyFAPP Project, funded by the European Union under the Grant Agreement number 643806. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Source at https://doi.org/10.2196/12237. Background: Research in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored. Objective: To study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes. Methods: The methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market. Results: The literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (P=.42, F=.81) nor the type of feature (P=.20, χ2=11.7). Apps were classified according to the number of level 1 features and sorted into quartiles. First quartile apps had a regular distribution of the ten features in the taxonomy whereas the other 3 quartiles had an irregular distribution. Conclusions: There are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the disease.

Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given realworld\ud data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can\ud be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare.\ud In this paper we introduce pMineR, an R library specifically designed for performing Process Mining in the medical domain, and supporting\ud human experts by presenting processes in a human-readable way.

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. In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare.

Source at https://doi.org/10.1155/2017/8194940 . Health consumers are increasingly using the Internet to search for health information. The existence of overloaded, inaccurate, obsolete, or simply incorrect health information available on the Internet is a serious obstacle for finding relevant and good-quality data that actually helps patients. Search engines of multimedia Internet platforms are thought to help users to find relevant information according to their search. But, is the information recovered by those search engines from quality sources? Is the health information uploaded from reliable sources, such as hospitals and health organizations, easily available to patients? The availability of videos is directly related to the ranking position in YouTube search. The higher the ranking of the information is, the more accessible it is. The aim of this study is to analyze the ranking evolution of diabetes health videos on YouTube in order to discover how videos from reliable channels, such as hospitals and health organizations, are evolving in the ranking. The analysis was done by tracking the ranking of 2372 videos on a daily basis during a 30-day period using 20 diabetes-related queries. Our conclusions are that the current YouTube algorithm favors the presence of reliable videos in upper rank positions in diabetes-related searches.

[EN] BACKGROUND: Social media is changing the way in which citizens and health professionals communicate. Previous studies have assessed the use of Health 2.0 by hospitals, showing clear evidence of growth in recent years. In order to understand if this happens in Spain, it is necessary to assess the performance of health care institutions on the Internet social media using quantitative indicators. OBJECTIVES: The study aimed to analyze how hospitals in Spain perform on the Internet and social media networks by determining quantitative indicators in 3 different dimensions: presence, use, and impact and assess these indicators on the 3 most commonly used social media - Facebook, Twitter, YouTube. Further, we aimed to find out if there was a difference between private and public hospitals in their use of the aforementioned social networks. METHODS: The evolution of presence, use, and impact metrics is studied over the period 2011- 2015. The population studied accounts for all the hospitals listed in the National Hospitals Catalog (NHC). The percentage of hospitals having Facebook, Twitter, and YouTube profiles has been used to show the presence and evolution of hospitals on social media during this time. Usage was assessed by analyzing the content published on each social network. Impact evaluation was measured by analyzing the trend of subscribers for each social network. Statistical analysis was performed using a lognormal transformation and also using a nonparametric distribution, with the aim of comparing t student and Wilcoxon independence tests for the observed variables. RESULTS: From the 787 hospitals identified, 69.9% (550/787) had an institutional webpage and 34.2% (269/787) had at least one profile in one of the social networks (Facebook, Twitter, and YouTube) in December 2015. Hospitals' Internet presence has increased by more than 450.0% (787/172) and social media presence has increased ten times since 2011. Twitter is the preferred social network for public hospitals, whereas private hospitals showed better performance on Facebook and YouTube. The two-sided Wilcoxon test and t student test at a CI of 95% show that the use of Twitter distribution is higher (P, The authors wish to acknowledge the ITACA Institute (Universitat Politècnica de València) for making possible the publication of this paper through the Excellence Support program for the publication in high-impact international journals.

[EN] Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice.This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientificc progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized., The authors want to acknowledge the European Commision for their suport via MOSAIC (ICT-FP7-600914) and HEARTWAYS (ICT-SME-315659) EU-Projects

Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/ doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single serviceoriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environment, The authors wish to acknowledge the consortium of the METABO project (funded by the European Commission, Grant nr. 216270) for their commitment during concept development and trial execution.

Type 2 Diabetes Mellitus (T2DM) is the most common form of diabetes. Early identification of people at risk of developing T2DM is extremely important, but the effectiveness of existing model is not clear as it is not clear the relative importance of the needs that such systems should satisfy.

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection, The authors want to acknowledge the Spanish Government, the eMotiva Project (TSI-020110-2009-219) partners, Health Institute Carlos III through the RETICSCombiomed (RD07/0067/2001) and Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ05-02-03386), for their support and the professionals and residents of Centro Residencial San Sebastian en la Pobla De Vallbona and MySphera Enterprise for their active participation in the project.

Detection of abnormal cardiac events during clinical examination is a matter of chances, as such events may not happen at that precise moment. We therefore propose the implementation and evaluation of a mobile based system that allows a real-time detection of cardiovascular problems related to heart-rate variability. Our approach is to integrate an Internet of Things eHealth kit based on Arduino and validated algorithms for heart rate variability to build a low-cost, reliable and scalable solution. 12 healthy users have evaluated the system in different scenarios to assess the best performing algorithm and the best windowing interval. Finally, a mobile system based on an Android application which integrated the Pan and Tompkins algorithm with a 20 seconds windowing and a module to retrieve real-time electrocardiography through a Bluetooth interface was implemented and assessed.

[EN] Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass., Authors would like to acknowledge the Laboratory for the Analysis for Human Behavior (www.sabien.upv.es/lach) and the Operative Program FEDER 2007/2013, for providing the necessary materials to undertake the presented research. The work done by A.L. was funded by the Ministry of Economy and Competitiveness: Promoting Youth Employment Program and Implementation of the (PEJ-2014-A-06813) Youth Guarantee 2014. The subsidized activity is part of the National System of Youth Guarantee and are co-financed under the Operational Program for Youth Employment, with financial resources from the Initiative Youth Employment (IYE) and the European Social Fund (ESF) for the period 2014-2020.