A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity

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dc.authorid0000-0002-6624-6148en_US
dc.authorid0000-0003-2371-8173en_US
dc.authorid0000-0003-1840-9958
dc.contributor.authorNour, Majid
dc.contributor.authorSindi, Hatem
dc.contributor.authorAbozinadah, Ehab
dc.contributor.authorÖztürk, Şaban
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:53:49Z
dc.date.available2021-06-23T19:53:49Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractObjective: Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process. Method: As a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured. Results: As a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model. Conclusion: Finally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.en_US
dc.identifier.doi10.1016/j.ijmedinf.2020.104300
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.pmid33069058en_US
dc.identifier.scopus2-s2.0-85092436354en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijmedinf.2020.104300
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10271
dc.identifier.volume144en_US
dc.identifier.wosWOS:000600413300002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofInternational Journal Of Medical Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHealthcare System Evaluationen_US
dc.subjectHospital Indicatorsen_US
dc.subjectRandom Forest Regressionen_US
dc.subjectWeighted Indicatorsen_US
dc.subjectCross-indicatorsen_US
dc.titleA healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurityen_US
dc.typeArticleen_US

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