Simulating the behavior of the human brain on GPUS

The simulation of the behavior of the Human Brain is one of the most important challenges in computing today. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons’ morphology. This is carried out using the Hines Algorithm and, although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. Two different approaches are studied, one for mono-morphology simulations (batch of neurons with the same shape) and one for multi-morphology simulations (batch of neurons where every neuron has a different shape). In mono-morphology simulations we obtain a good performance using just a single kernel to compute all the neurons. However this turns out to be inefficient on multi-morphology simulations. Unlike the previous scenario, in multi-morphology simulations a much more complex implementation is necessary to obtain a good performance. In this case, we must execute more than one single GPU kernel. In every execution (kernel call) one specific part of the batch of the neurons is solved. These parts can be seen as multiple and independent tridiagonal systems. Although the present paper is focused on the simulation of the behavior of the Human Brain, some of these techniques, in particular those related to the solving of tridiagonal systems, can be also used for multiple oil and gas simulations. Our studies have proven that the optimizations proposed in the present work can achieve high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two NVIDIA K80 GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling, even when dealing with a very high number of neurons.This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316-P), the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Parallels (2014-SGR-1051). We thank the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence, and the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement No. 749516.Peer ReviewedPostprint (published version

Synthesis of Tri- and Tetrasubstituted Alkenyl Boronates from Alkynes

The synthesis of organoboron compounds have attracted the attention of the synthetic community. In particular, molecules with C(sp(2))-B bonds enable the transformation to new C-C or C-heteroatom bonds by well-established methodologies. Alkenyl boronates have the possibility for further conversion of the boron moiety or functionalization of the double bond. This review gives an overview on the recent methodologies for the selective preparation of the challenging highly substituted alkenyl boronates from alkynes.Ministerio de Ciencia e InnovaciónComunidad de MadridUniversidad de Alcal

A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards

[EN] The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.This research received no external funding. The APC was funded by Research group Information and Communication Technologies against Climate Change (!CTCC) of the Universitat Politecnica de Valencia, Spain.Lemus Zúñiga, LG.; Félix, JM.; Fides Valero, Á.; Benlloch-Dualde, J.; Martinez-Millana, A. (2022). A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards. Sensors. 22(4):1-17. https://doi.org/10.3390/s2204164611722

cuHinesBatch: solving multiple hines systems on GPUs Human Brain Project

The simulation of the behavior of the Human Brain is one of the most important challenges today in computing. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons’ morphology. This is carried out using the Hines Algorithm. Although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on NVIDIA GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. To evaluate the impact of the optimizations on real inputs, we have used 6 different morphologies in terms of size and branches. Our studies have proven that the optimizations proposed in the present work can achieve a high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two K80 NVIDIA GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling even when dealing with number of neurons.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Paral·lels (2014-SGR-1051). We thank the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence. Antonio J. Peña is cofinanced by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva fellowship number IJCI-2015-23266.Peer ReviewedPostprint (published version

Influencia de la obesidad en la salud sexual y reproductiva

Resumen Introducción. La epidemia mundial del sobrepeso y la obesidad se está convirtiendo, dado su crecimiento, en un importante problema de salud pública en muchas partes del mundo; afecta aproximadamente a la mitad de la población y por lo tanto resulta un problema común entre la franja de edad fértil. Exposición. La obesidad y el sobrepeso son condiciones comunes que tienen consecuencias no sólo en la salud en general, sino también en gran medida sobre la salud reproductiva. Existe una alta prevalencia de mujeres obesas en la población estéril y numerosos estudios han puesto de relieve el vínculo entre la obesidad y la infertilidad. También aumenta los riesgos de atención obstétrica y complicaciones neonatales, disminuye la fecundidad, incluso en mujeres que ovulan, y está asociada a trastornos menstruales y mayor riesgo de abortos espontáneos de repetición. Conclusiones. Cambios en el estilo de vida, la realización de una dieta y un programa de ejercicio, constituyen la primera línea de tratamiento para la obesidad

A Novel, Quick, and Reliable Smartphone-Based Method for Serum PSA Quantification: Original Design of a Portable Microfluidic Immunosensor-Based System

We describe a versatile, portable, and simple platform that includes a microfluidic electrochemical immunosensor for prostate-specific antigen (PSA) detection. It is based on the covalent immobilization of the anti-PSA monoclonal antibody on magnetic microbeads retained in the central channel of a microfluidic device. Image flow cytometry and scanning electron microscopy were used to characterize the magnetic microbeads. A direct sandwich immunoassay (with horseradish peroxidase-conjugated PSA antibody) served to quantify the cancer biomarker in serum samples. The enzymatic product was detected at -100 mV by amperometry on sputtered thin-film electrodes. Electrochemical reaction produced a current proportional to the PSA level, with a linear range from 10 pg mL(-1) to 1500 pg mL(-1). The sensitivity was demonstrated by a detection limit of 2 pg mL(-1) and the reproducibility by a coefficient of variation of 6.16%. The clinical performance of this platform was tested in serum samples from patients with prostate cancer (PCa), observing high specificity and full correlation with gold standard determinations. In conclusion, this analytical platform is a promising tool for measuring PSA levels in patients with PCa, offering a high sensitivity and reduced variability. The small platform size and low cost of this quantitative methodology support its suitability for the fast and sensitive analysis of PSA and other circulating biomarkers in patients. Further research is warranted to verify these findings and explore its potential application at all healthcare levels.Universidad Nacional de San Luis PROICO 22/Q241ANPCyT PICT 2018-04443 PICT-2015-2246 PICT-2015-1575 PICT-2014-1184 PICT-2014-0375 PICT-2018-04443Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 11220150100004COGENYOCentre for Genomics and Oncological Research: Pfizer-University of GranadaAndalusian Regional Government (Granada, Spain)ISCIII Health Research Institute P17/00989La Caixa FoundationHealth and Family Secretariat of the Andalusian Regional GovernmentSpanish GovernmentH2020-MSCA-IF-2019-89566

Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case

[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).Ibáñez Sánchez, G.; Fernández Llatas, C.; Martinez-Millana, A.; Celda, A.; Mandingorra, J.; Aparici-Tortajada, L.; Valero Ramon, Z.... (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):1-22. https://doi.org/10.3390/ijerph16101783S1221610Berwick, D. M., Nolan, T. W., & Whittington, J. (2008). The Triple Aim: Care, Health, And Cost. Health Affairs, 27(3), 759-769. doi:10.1377/hlthaff.27.3.759Porter, M. E. (2010). What Is Value in Health Care? New England Journal of Medicine, 363(26), 2477-2481. doi:10.1056/nejmp1011024Mamlin, B. W., & Tierney, W. M. (2016). The Promise of Information and Communication Technology in Healthcare: Extracting Value From the Chaos. The American Journal of the Medical Sciences, 351(1), 59-68. doi:10.1016/j.amjms.2015.10.015Murdoch, T. B., & Detsky, A. S. (2013). The Inevitable Application of Big Data to Health Care. JAMA, 309(13), 1351. doi:10.1001/jama.2013.393Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs, 33(7), 1123-1131. doi:10.1377/hlthaff.2014.0041Fernández-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/ijerph10115671Rojas, E., Sepúlveda, M., Munoz-Gama, J., Capurro, D., Traver, V., & Fernandez-Llatas, C. (2017). Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Applied Sciences, 7(3), 302. doi:10.3390/app7030302Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: what it is and what it isn’t. BMJ, 312(7023), 71-72. doi:10.1136/bmj.312.7023.71Is Evidence-Based Medicine Patient-Centered and Is Patient-Centered Care Evidence-Based? (2006). Health Services Research, 41(1), 1-8. doi:10.1111/j.1475-6773.2006.00504.xGoldberger, J. J., & Buxton, A. E. (2013). Personalized Medicine vs Guideline-Based Medicine. JAMA, 309(24), 2559. doi:10.1001/jama.2013.6629Kelly, M. P., Heath, I., Howick, J., & Greenhalgh, T. (2015). The importance of values in evidence-based medicine. BMC Medical Ethics, 16(1). doi:10.1186/s12910-015-0063-3Gonzalez-Ferrer, A., Seara, G., Cháfer, J., & Mayol, J. (2018). Generating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare. International Journal of Interactive Multimedia and Artificial Intelligence, 4(7), 42. doi:10.9781/ijimai.2017.03.006Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176), 1203-1205. doi:10.1126/science.1248506Rojas, 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.2016.04.007Fernandez-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/s151229769Baker, K., Dunwoodie, E., Jones, R. G., Newsham, A., Johnson, O., Price, C. P., … Hall, G. (2017). Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy. International Journal of Medical Informatics, 103, 32-41. doi:10.1016/j.ijmedinf.2017.03.011Rebuge, 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.2011.01.003Partington, 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:10.1145/2629446Storm-Versloot, M. N., Ubbink, D. T., Kappelhof, J., & Luitse, J. S. K. (2011). Comparison of an Informally Structured Triage System, the Emergency Severity Index, and the Manchester Triage System to Distinguish Patient Priority in the Emergency Department. Academic Emergency Medicine, 18(8), 822-829. doi:10.1111/j.1553-2712.2011.01122.xFeigin, V. L., Roth, G. A., Naghavi, M., Parmar, P., Krishnamurthi, R., Chugh, S., … Forouzanfar, M. H. (2016). Global burden of stroke and risk factors in 188 countries, during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet Neurology, 15(9), 913-924. doi:10.1016/s1474-4422(16)30073-4Howard, G., & Goff, D. C. (2012). Population shifts and the future of stroke: forecasts of the future burden of stroke. Annals of the New York Academy of Sciences, 1268(1), 14-20. doi:10.1111/j.1749-6632.2012.06665.xGustavsson, A., Svensson, M., Jacobi, F., Allgulander, C., Alonso, J., Beghi, E., … Olesen, J. (2011). Cost of disorders of the brain in Europe 2010. European Neuropsychopharmacology, 21(10), 718-779. doi:10.1016/j.euroneuro.2011.08.008Alberts, M. J. (2000). Recommendations for the Establishment of Primary Stroke Centers. JAMA, 283(23), 3102. doi:10.1001/jama.283.23.3102Conca, 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.8884Chavalarias, D., Wallach, J. D., Li, A. H. T., & Ioannidis, J. P. A. (2016). Evolution of ReportingPValues in the Biomedical Literature, 1990-2015. JAMA, 315(11), 1141. doi:10.1001/jama.2016.195

First results of archaeological research in the Colorado-Negro interfluvium (Pichi Mahuida department, Río Negro province, Argentina)

En este trabajo se presentan los resultados de las primeras investigaciones arqueológicas realizadas en el interfluvio ubicado entre los cursos medios de los ríos Colorado y Negro (Provincia de Río Negro, Argentina). En esta extensa planicie árida se encuentran lagunas temporarias en las que se concentra el material arqueológico, principalmente en superficie. Con el fin de comprender las estrategias humanas de uso del espacio y la intensidad de las ocupaciones se llevaron a cabo estudios distribucionales y, en menor medida, excavaciones arqueológicas. La información generada fue discutida a la luz de las expectativas arqueológicas derivadas del modelo de uso del espacio propuesto por Borrero y colaboradores (2008). Los resultados obtenidos indican que el registro arqueológico localizado en torno a estas lagunas efímeras es principalmente el efecto de un uso del espacio planificado y redundante. Asimismo, algunos sectores de estas lagunas fueron utilizados más intensa y repetidamente, sugiriendo casos de redundancia específica. Si bien no se cuenta con fechados radiocarbónicos, la presencia de cerámica permite vincular las ocupaciones humanas al menos al Holoceno Tardío. La evidencia discutida revela la importancia de este espacio interfluvial en los circuitos de asentamiento y movilidad de las sociedades cazadoras-recolectoras en torno a dos de los principales ríos norpatagónicos.This paper presents the first results of the archaeological investigations carried out in the interfluvium located between the middle courses of the Colorado and Negro rivers (Río Negro province, Argentina). This is an extensive arid plain with many temporary lagoons in which archaeological material is concentrated, mainly on the surface. Our analysis included distributional studies and, to a lesser extent, archaeological excavations, in order to understand the human strategies of space use and occupational intensity. The generated information is discussed in the light of archaeological expectations derived from the space use model proposed by Borrero and colleagues (2008). The results obtained indicate that the archaeological record located around these ephemeral lagoons is mainly the result of a planned and redundant use of space. Furthermore, some sectors of these lagoons were used more intensively and repeatedly, suggesting cases of specific redundancy. Although radiocarbon dates could not be obtained, the presence of pottery relates human occupations at least to the Late Holocene. The evidence discussed reveals the importance of this interfluvial space in the settlement and mobility strategies of the hunter-gatherer societies that inhabited two of the main northern Patagonian rivers.Fil: Martinez, Gustavo Adolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; ArgentinaFil: Santos Valero, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; ArgentinaFil: Alcaraz, Ana Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; ArgentinaFil: Borges Vaz, Erika. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; ArgentinaFil: Stoessel, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; ArgentinaFil: Flensborg, Gustavo Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; ArgentinaFil: Ramos Martínez, Gustavo Antonio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Geología de Costas y del Cuaternario. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Geología de Costas y del Cuaternario; ArgentinaFil: Rafuse, Daniel Joseph. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano; Argentin

Process mining for healthcare: Characteristics and challenges

[EN] 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.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] 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?Gatta, R.; Vallati, M.; Fernández Llatas, C.; Martinez-Millana, A.; Orini, S.; Sacchi, L.; Lenkowicz, J.... (2020). What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper. International Journal of Environmental research and Public Health (Online). 17(18):1-19. https://doi.org/10.3390/ijerph17186616S1191718Guyatt, G. (1992). Evidence-Based Medicine. JAMA, 268(17), 2420. doi:10.1001/jama.1992.03490170092032Hripcsak, G., Ludemann, P., Pryor, T. A., Wigertz, O. B., & Clayton, P. D. (1994). Rationale for the Arden Syntax. Computers and Biomedical Research, 27(4), 291-324. doi:10.1006/cbmr.1994.1023Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. 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