Representación, interpretación y aprendizaje de flujos de trabajo basado en actividades para la estandarización de vías clínicas

Describir los mejores procesos para ejecutar correctamente una estrategia de una forma eficiente y con calidad no es siempre una tarea fácil. La estandarizaci ón de procesos en general y de Vías Clínicas en particular requiere de potentes herramientas de especificación e implementación que apoyen a los expertos en diseño. La utilización de modelos de Flujos de Trabajo (del inglés Work ows) facilita a los expertos en diseño la creación las reglas de ejecución de sus sistemas como si fueran programadores. Aún así debido a la gran mutabilidad de los procesos reales, es muy difícil conocer como los procesos se están ejecutando en la realidad. La utilización de técnicas de reconocimiento de formas pueden ayudar a los expertos en procesos a inferir, a partir de muestras de ejecución pasadas, modelos que expliquen la forma en la que estos procesos están efectivamente ejecutándose. Este paradigma es conocido como Aprendizaje de Flujos de Trabajo (del inglés Work ow Mining). Los cambios de estado en los procesos de cuidado existentes en las Vías Clínicas se basan en los resultados de las acciones. Los modelos actuales de Aprendizaje de Flujos de Trabajo no recogen esta información en sus corpus. Por eso, los actuales sistemas de aprendizaje no cubren las necesidades de problemas complejos como es el caso de las Vías Clínicas. En esta Tesis se van a estudiar los modelos de representación, interpretación y aprendizaje de Flujos de Trabajo con la intención de proponer un modelo adecuado para resolver los problemas que impiden a los diseñadores de procesos complejos, como Vías Clínicas, utilizar técnicas de Aprendizaje de Flujos de Trabajo. Para ello se va a definir un nuevo paradigma adecuado para el apoyo al diseño de Vías Clínicas, además de proporcionar herramientas para su uso. Por esto en esta Tesis se presenta además un modelo de representación de Flujos de Trabajo con una alta expresividad y legibilidad, una herramienta software capaz de ejecutar y simular Flujos de TrabFernández Llatas, C. (2009). Representación, interpretación y aprendizaje de flujos de trabajo basado en actividades para la estandarización de vías clínicas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4562Palanci

Morphologic matrix application as a tool to spring on creativity

[EN] Morphological analysis methodology has a broad spectrum of application: from technological management to the design of new products and services. Among the techniques applied for spurring creativity in “Managerial Skills to Engineers”, Morphological Analysis has been that has had more success along the years. Traditionally has the feature of being a technique very structured that easily can be applied by the students for resolving different kind of problems, and actually the results confirm this asseveration. Our group has applied this methodology since many years ago. First on doctorate courses of “Technology Management” and afterwards on training in different companies, degree and master subjects, and on Continuous Improvement actions (Kaizen blitz) in Almusaffes Ford Factory and first line suppliers. On this paper we discuss the experience applying it as a tool for developing new products combining with other techniques for spurring creativity as brain storming, lateral thinking, de Bono’s hats, nominal group, etc. In this communication are resumed some of the technique application results and the most interesting answers to the final questionnaire each year is passed for knowing directly student’s real opinion.Dema Pérez, CM.; Estelles Miguel, S.; Fernández Llatas, C. (2022). Morphologic matrix application as a tool to spring on creativity. En Proceedings INNODOCT/21. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 207-214. https://doi.org/10.4995/INN2021.2021.1393720721

Collaborative learning and pandemic situation with online teaching. The experience in Management Skills for Engineers

[EN] In the subject of Management Skills for Engineers in the last 2020/21 academic year, it was necessary to modify radically the full teaching scheme. Given that the number of students was small and that they already knew the conceptual management basis, this subject had traditionally been developed through reverse teaching and carrying out cases and group works in a collaborative learning context. COVID and the need of structuring teaching and tutorials based on TEAMS, which the UPV has standardized, raised an important turning point and decision: moving to a traditional teaching methodology or keeping the one used until now adapting it to take advantage of the potential of TEAMS and the rest of the UPV's applications. In this communication the theoretical foundations, the planning of the subject and the most significant results are summarized.Dema Pérez, CM.; Estelles Miguel, S.; Fernández Llatas, C. (2022). Collaborative learning and pandemic situation with online teaching. The experience in Management Skills for Engineers. En Proceedings INNODOCT/21. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 197-205. https://doi.org/10.4995/INN2021.2021.13936OCS19720

Managerial Skills to Engineers, an optative subject on last course of grade in the ETSIIV. Results of applying new methodologies for developing managerial skills

[EN] Managerial Skills to Engineers is an optative subject placed on last degree course. Students have passed on second course “Organization Foundations” as only subject about management contents. So, it was proposed with the aim of developing main managerial skills thinking of their professional future, highlighting among them communication skills clearly. On fact, during quotidian engineer’s work on plant they need leading groups, participating on meetings, negotiating with clients, suppliers, etc. Communication constitutes a basic pillar for personnel and professional engineer’s future success. In this paper most relevant results reached during three last years have been gathered up, all of it considering actual constraints have advised us to improve each year step by step consolidating each one before moving forward.Dema Pérez, CM.; Fernández Llatas, C.; Martinez-Miñana, A.; Estelles Miguel, S. (2019). Managerial Skills to Engineers, an optative subject on last course of grade in the ETSIIV. Results of applying new methodologies for developing managerial skills. En INNODOCT/18. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 185-193. https://doi.org/10.4995/INN2018.2018.8864OCS18519

Morphologic matrix application as a tool to spring on creativity. Results in a design master in the U.P.V

[EN] Some years ago our group had the challenge of collaborating on a design master teaching an optative subject of Technology Management Foundations. The challenge was to develop it in an attractive way capable of interesting really students and generating a motivated behaviour in class. Now, seven years later, it is possible to have a complete landscape of this experience. Designers profile was very different from the profile of mechanical, electric, electronics, chemical, ... engineers we usually had in class, and this reality was a problem at the beginning of first edition when we had to resolve it and to define the basis to thenew master editions. Main tool taken from technological forecasting to apply it as a design tool was Morphology. Our group had applied this methodology since many years ago. First on doctorate courses of “Technology Management” and afterwards on postgraduate courses and masters. On this paper we discuss the experience on a design master where this methodology was applied as a tool for developing new products combining with other techniques for spurring creativity as brain storming, lateral thinking, de Bono’s hats, nominal group, etc. Other forecasting methodologies were gap analysis and analogies. Examples of final works have not been included so it is not possible contacting students to ask for permission in order to include them on the paper.Dema Pérez, CM.; Fernández Llatas, C.; Martinez-Miñana, A.; Estelles Miguel, S. (2019). Morphologic matrix application as a tool to spring on creativity. Results in a design master in the U.P.V. En INNODOCT/18. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 195-202. https://doi.org/10.4995/INN2018.2018.8865OCS19520

Performance assessment of a closed-loop system for diabetes management

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 environmentThe 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.Martínez Millana, A.; Fico, G.; Fernández Llatas, C.; Traver Salcedo, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical and Biological Engineering and Computing. 53(12):1295-1303. doi:10.1007/s11517-015-1245-3S129513035312Bellazzi R, Larizza C, Montani A et al (2002) A telemedicine support dor diabetes management: the T-IDDM project. Comput Methods Programs Biomed 69:147–161Boloor K, Chirkova R, Salo T, Viniotis Y (2011) Analysis of response time percentile service level agreements in soa-based applications. IEEE global telecommunications conference (GLOBECOM 2011), dec. 2011, pp 1–6Cartwright M et al (2013) Effect of telehealth on quality of life and psychological outcomes over 12 months: nested study of patient reported outcomes in a pragmatic, cluster randomised controlled trial. BMJ 346:f653Chen I-Y et al (2008) Pervasive digital monitoring and transmission of pre-care patient biostatics with an OSGi, MOM and SOA based remote health care system. In: Proceedings of the 6th annual IEEE international conference on PerCom. Hong KongFico G, Fioravanti A, Arredondo MT, Leuteritz JP, Guillén A, Fernandez D (2011) A user centered design approach for patient interfaces to a diabetes IT platform. Conf Proc IEEE Eng Med Biol Soc 2011:1169–1172Fioravanti A, Fico G, Arredondo MT, Salvi D, Villalar JL (2010) Integration of heterogeneous biomedical sensors into an ISO/IEEE 11073 compliant application. In: Engineering in medicine and biology society (EMBC), 2010 Annual international conference of the IEEE, pp 1049–1052García Saez G et al (2009) Architecture of a wireless personal assistant for telemedical diabetes care. Int J Med Inform 9(78):391–403Gómez EJ, Hernando ME et al (2008) The INCA system: a further step towards a telemedical artificial pancreas. IEEE Trans Inf Technol Biomed 12(4):470–479Harrison’s Principles of Internal Medicine (2011) McGraw-Hill. ISBN:978-0071748896. Ed. July 2011Ke X, Li W et al (2010) WCDMA KPI framework definition methods and applications. ICCET proceedings V4-471–V4-475Klonof D (2013) Twelve modern digital technologies that are transforming decision making for diabetes and all areas of health care. J Diabetes Sci Technol 7(2):291–295Lanzola G et al (2007) Going mobile with a multiaccess service for the management of diabetic patients. J Diabetes Sci Technol 1(5):730–737Ma C et al (2006) Empowering patients with essential information and communication support in the context of diabetes. Int J Med Inform 75(8):577–596Müller AJ, Knuth M, Nikolaus KS, Krivánek R, Küster F, Hasslacher C (2013) First clinical evaluation of a new percutaneous optical fiber glucose sensor for continuous glucose monitoring in diabetes. J Diabetes Sci Technol 7:13Nundy S et al (2012) Using mobile health to support chronic care model: developing an institutional model. Int J Telemed Appl 2012, Art Id 871925. doi: 10.1155/2012/871925Obstfelder A, Engeseth KH, Wynn R (2007) Characteristic of succesfully implemented telemedical applications. Implement Sci 2:25Pravin P et al (2012) A framework for the comparison of mobile patient monitoring systems. J Biomed Inf 45:544–556Reichel A, Rietzsch H, Ludwig B, Röthig K, Moritz A, Bornstein S (2013) Self-adjustment of insulin dose using graphically depicted self-monitoring of blood glucose measurements in patients with type 1 diabetes mellitus. J Diabetes Sci Technol 7(1):156–162Ryan D et al (2012) Clinical and cost effectiveness of mobile phone supported self-monitoring of asthma: multicenter randomized controlled trial. BMJ 344:e1756Schade DS et al (2005) To pump or not to pump. Diabetes Technol Therapeutics 7:845–848Stravroula G, Bartsocas CS et al (2010) SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients. IEEE Trans Inf Technol Biomed 14(3):622–633The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. 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Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

[EN] Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients¿ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients¿ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560.Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330S1252018Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0Brennan, P., Perola, M., van Ommen, G.-J., & Riboli, E. (2017). Chronic disease research in Europe and the need for integrated population cohorts. European Journal of Epidemiology, 32(9), 741-749. doi:10.1007/s10654-017-0315-2Raghupathi, W., & Raghupathi, V. (2018). An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health. International Journal of Environmental Research and Public Health, 15(3), 431. doi:10.3390/ijerph15030431Forouzanfar, M. H., Afshin, A., Alexander, L. T., Anderson, H. R., Bhutta, Z. A., Biryukov, S., … Charlson, F. J. (2016). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1659-1724. doi:10.1016/s0140-6736(16)31679-8Gómez, J., Oviedo, B., & Zhuma, E. (2016). Patient Monitoring System Based on Internet of Things. Procedia Computer Science, 83, 90-97. doi:10.1016/j.procs.2016.04.103Harvey, A., Brand, A., Holgate, S. T., Kristiansen, L. V., Lehrach, H., Palotie, A., & Prainsack, B. (2012). The future of technologies for personalised medicine. New Biotechnology, 29(6), 625-633. doi:10.1016/j.nbt.2012.03.009Larry Jameson, J., & Longo, D. L. (2015). Precision Medicine—Personalized, Problematic, and Promising. Obstetrical & Gynecological Survey, 70(10), 612-614. doi:10.1097/01.ogx.0000472121.21647.38Collins, F. S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795. doi:10.1056/nejmp1500523Glasgow, R. E., Kwan, B. M., & Matlock, D. D. (2018). Realizing the full potential of precision health: The need to include patient-reported health behavior, mental health, social determinants, and patient preferences data. Journal of Clinical and Translational Science, 2(3), 183-185. doi:10.1017/cts.2018.31Whittemore, A. S. (2010). Evaluating health risk models. Statistics in Medicine, 29(23), 2438-2452. doi:10.1002/sim.3991Reynolds, B. C., Roem, J. L., Ng, D. K. S., Matsuda-Abedini, M., Flynn, J. T., Furth, S. L., … Parekh, R. S. (2020). Association of Time-Varying Blood Pressure With Chronic Kidney Disease Progression in Children. JAMA Network Open, 3(2), e1921213. doi:10.1001/jamanetworkopen.2019.21213Campbell, H., Hotchkiss, R., Bradshaw, N., & Porteous, M. (1998). Integrated care pathways. BMJ, 316(7125), 133-137. doi:10.1136/bmj.316.7125.133Schienkiewitz, A., Mensink, G. B. M., & Scheidt-Nave, C. (2012). Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18-79 years. BMC Public Health, 12(1). doi:10.1186/1471-2458-12-658Must, A. (1999). The Disease Burden Associated With Overweight and Obesity. JAMA, 282(16), 1523. doi:10.1001/jama.282.16.1523Audureau, E., Pouchot, J., & Coste, J. (2016). Gender-Related Differential Effects of Obesity on Health-Related Quality of Life via Obesity-Related Comorbidities. Circulation: Cardiovascular Quality and Outcomes, 9(3), 246-256. doi:10.1161/circoutcomes.115.002127Everhart, J. E., Pettitt, D. J., Bennett, P. H., & Knowler, W. C. (1992). Duration of Obesity Increases the Incidence of NIDDM. Diabetes, 41(2), 235-240. doi:10.2337/diab.41.2.235Wannamethee, S. G. (2005). Overweight and obesity and weight change in middle aged men: impact on cardiovascular disease and diabetes. Journal of Epidemiology & Community Health, 59(2), 134-139. doi:10.1136/jech.2003.015651Ziegelstein, R. C. (2018). Perspectives in Primary Care: Knowing the Patient as a Person in the Precision Medicine Era. The Annals of Family Medicine, 16(1), 4-5. doi:10.1370/afm.2169Tricoli, A., Nasiri, N., & De, S. (2017). Wearable and Miniaturized Sensor Technologies for Personalized and Preventive Medicine. Advanced Functional Materials, 27(15), 1605271. doi:10.1002/adfm.201605271Saponara, S., Donati, M., Fanucci, L., & Celli, A. (2016). An Embedded Sensing and Communication Platform, and a Healthcare Model for Remote Monitoring of Chronic Diseases. Electronics, 5(4), 47. doi:10.3390/electronics5030047Alvarez, C., Rojas, E., Arias, M., Munoz-Gama, J., Sepúlveda, M., Herskovic, V., & Capurro, D. (2018). Discovering role interaction models in the Emergency Room using Process Mining. Journal of Biomedical Informatics, 78, 60-77. doi:10.1016/j.jbi.2017.12.015Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Orphanou, K., Stassopoulou, A., & Keravnou, E. (2016). DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis. IEEE Journal of Biomedical and Health Informatics, 20(3), 944-952. doi:10.1109/jbhi.2015.2420534Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., … Pavel, M. (2015). Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translational Behavioral Medicine, 5(3), 335-346. doi:10.1007/s13142-015-0324-1Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Ibanez-Sanchez, G., Fernandez-Llatas, C., Martinez-Millana, A., Celda, A., Mandingorra, J., Aparici-Tortajada, L., … Traver, V. (2019). Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case. International Journal of Environmental Research and Public Health, 16(10), 1783. doi:10.3390/ijerph16101783Chambers, D. A., Feero, W. G., & Khoury, M. J. (2016). Convergence of Implementation Science, Precision Medicine, and the Learning Health Care System. JAMA, 315(18), 1941. doi:10.1001/jama.2016.3867Cameranesi, M., Diamantini, C., Mircoli, A., Potena, D., & Storti, E. (2020). Extraction of User Daily Behavior From Home Sensors Through Process Discovery. IEEE Internet of Things Journal, 7(9), 8440-8450. doi:10.1109/jiot.2020.2990537Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Conca, T., Saint-Pierre, C., Herskovic, V., Sepúlveda, M., Capurro, D., Prieto, F., & Fernandez-Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. Journal of Medical Internet Research, 20(4), e127. doi:10.2196/jmir.8884Makaroff, L. E. (2017). The need for international consensus on prediabetes. The Lancet Diabetes & Endocrinology, 5(1), 5-7. doi:10.1016/s2213-8587(16)30328-xShiue, I., McMeekin, P., & Price, C. (2017). Retrospective observational study of emergency admission, readmission and the ‘weekend effect’. BMJ Open, 7(3), e012493. doi:10.1136/bmjopen-2016-01249

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s Flow Analysis in Physical Retail Store. Procedia Manufacturing, 3, 3506-3513. doi:10.1016/j.promfg.2015.07.672Dogan, O., & Öztaysi, B. (2018). In-store behavioral analytics technology selection using fuzzy decision making. Journal of Enterprise Information Management, 31(4), 612-630. doi:10.1108/jeim-02-2018-0035Hwang, I., & Jang, Y. J. (2017). Process Mining to Discover Shoppers’ Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System. IEEE Transactions on Automation Science and Engineering, 14(4), 1786-1792. doi:10.1109/tase.2017.2692961Abedi, N., Bhaskar, A., Chung, E., & Miska, M. (2015). Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses. Transportation Research Part C: Emerging Technologies, 60, 124-141. doi:10.1016/j.trc.2015.08.010Mou, S., Robb, D. J., & DeHoratius, N. (2018). Retail store operations: Literature review and research directions. European Journal of Operational Research, 265(2), 399-422. doi:10.1016/j.ejor.2017.07.003Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003). Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237-267. doi:10.1016/s0169-023x(03)00066-1Ou-Yang, C., & Winarjo, H. (2011). Petri-net integration – An approach to support multi-agent process mining. Expert Systems with Applications, 38(4), 4039-4051. doi:10.1016/j.eswa.2010.09.066Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Funkner, A. A., Yakovlev, A. N., & Kovalchuk, S. V. (2017). Towards evolutionary discovery of typical clinical pathways in electronic health records. Procedia Computer Science, 119, 234-244. doi:10.1016/j.procs.2017.11.181Jans, M., Alles, M., & Vasarhelyi, M. (2013). The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems, 14(1), 1-20. doi:10.1016/j.accinf.2012.06.015Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J. P., Blat, J., & Sinatra, R. (2014). An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data. Environment and Planning B: Planning and Design, 41(6), 1113-1131. doi:10.1068/b130047pDe Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235-257. doi:10.1016/j.is.2015.07.003Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Arroyo, R., Yebes, J. J., Bergasa, L. M., Daza, I. G., & Almazán, J. (2015). Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Systems with Applications, 42(21), 7991-8005. doi:10.1016/j.eswa.2015.06.016Popa, M. C., Rothkrantz, L. J. M., Shan, C., Gritti, T., & Wiggers, P. (2013). 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Analysing spatiotemporal sequences in Bluetooth tracking data. Applied Geography, 34, 659-668. doi:10.1016/j.apgeog.2012.04.003Frisby, J., Smith, V., Traub, S., & Patel, V. L. (2017). Contextual Computing : A Bluetooth based approach for tracking healthcare providers in the emergency room. Journal of Biomedical Informatics, 65, 97-104. doi:10.1016/j.jbi.2016.11.008Yoshimura, Y., Krebs, A., & Ratti, C. (2017). Noninvasive Bluetooth Monitoring of Visitors’ Length of Stay at the Louvre. IEEE Pervasive Computing, 16(2), 26-34. doi:10.1109/mprv.2017.33Cao, Q., Jones, D. R., & Sheng, H. (2014). Contained nomadic information environments: Technology, organization, and environment influences on adoption of hospital RFID patient tracking. Information & Management, 51(2), 225-239. doi:10.1016/j.im.2013.11.007Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. 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Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] 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' 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'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.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. 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Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. 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Evaluating the Social Media Performance of Hospitals in Spain: A Longitudinal and Comparative Study

[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<.001) for private and public hospitals in Spain, whereas other variables show a nonsignificant different distribution. CONCLUSIONS: The Internet presence of Spanish hospitals is high; however, their presence on the 3 main social networks is still not as high compared to that of hospitals in the United States and Western Europe. Public hospitals are found to be more active on Twitter, whereas private hospitals show better performance on Facebook and YouTube. This study suggests that hospitals, both public and private, should devote more effort to and be more aware of social media, with a clear strategy as to how they can foment new relationships with patients and citizens.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.S11119