Building Process-Oriented Data Science Solutions for Real-World Healthcare

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dc.catalogadorjwg
dc.contributor.authorFernandez-Llatas, Carlos
dc.contributor.authorMartin, Niels
dc.contributor.authorJohnson, Owen
dc.contributor.authorSepúlveda, Marcos
dc.contributor.authorHelm, Emmanuel
dc.contributor.authorMuñoz Gama, Jorge
dc.date.accessioned2024-05-30T17:18:03Z
dc.date.available2024-05-30T17:18:03Z
dc.date.issued2022
dc.description.abstractThe 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.
dc.fuente.origenWOS
dc.identifier.doi10.3390/ijerph19148427
dc.identifier.eissn1660-4601
dc.identifier.urihttp://doi.org/10.3390/ijerph19148427
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86092
dc.identifier.wosidWOS:000834448400001
dc.information.autorucEscuela de Ingeniería; Munoz Gama, Jorge; 0000-0002-6908-3911; 1013863
dc.issue.numero14
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherMDPI
dc.revistaInternational Journal of Environmental Research and Public Health
dc.rightsacceso abierto
dc.subjectprocess-oriented data science
dc.subjectprocess mining
dc.subjecthealthcare
dc.subjectartificial intelligence
dc.subjectCOVID-19
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.titleBuilding Process-Oriented Data Science Solutions for Real-World Healthcare
dc.typeartículo
dc.volumen19
sipa.codpersvinculados1013863
sipa.trazabilidadORCID;2024-05-30
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