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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12710/25126
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dc.contributor.authorIapăscurtă, Victor
dc.date.accessioned2023-07-04T12:53:51Z
dc.date.available2023-07-04T12:53:51Z
dc.date.issued2023
dc.identifier.citationIAPĂSCURTĂ, Victor. Early prediction of sepsis using a proprietary application developed based on machine learning (artificial intelligence): abstract of the Ph.D. thesis in medical sciences: 321.19 – Anaesthesiology and intensive care. Chișinău, 2023, 36 p.
dc.identifier.urihttp://repository.usmf.md/handle/20.500.12710/25126
dc.description.abstractThe topicality and importance of the problem addressed. Two important aspects can be highlighted that outlines the research direction reflected in the paper: (a) The problem of sepsis as a variety of critical conditions, often difficult to diagnose in time, and the results of treatment depend closely on the time when treatment is started (with antibiotics) and the direct influence of these factors on mortality [1], which over time has decreased insignificantly [2] and (b) The emergence of a new player - the so-called artificial intelligence (AI) technologies, which often claim to be panaceas for many problems, including medical ones. The current research attempts to assess the possibility of using these technologies to address important issues in the management of this group of patients through early prediction (a few hours before onset) of sepsis, which would mitigate the delay in starting the treatment. [...]en_US
dc.language.isoenen_US
dc.subjectsepsisen_US
dc.subjectmodelen_US
dc.subjectartificial intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectalgorithmic complexityen_US
dc.subjectblock decomposition methoden_US
dc.subjectdecision support systemsen_US
dc.subjectprediction systemsen_US
dc.subject.ddcUDC: 616.94-037:004.8(043.2)en_US
dc.subject.meshSepsisen_US
dc.subject.meshSepsis--diagnosisen_US
dc.subject.meshArtificial Intelligenceen_US
dc.subject.meshMachine Learningen_US
dc.subject.meshAnesthesiology--educationen_US
dc.subject.meshCritical Careen_US
dc.subject.meshData Analysisen_US
dc.subject.meshModels, Educationalen_US
dc.titleEarly prediction of sepsis using a proprietary application developed based on machine learning (artificial intelligence) : Abstract of the Ph.D. thesis in medical sciences: 321.19 – Anaesthesiology and intensive careen_US
dc.typeOtheren_US
Appears in Collections:REZUMATELE TEZELOR DE DOCTOR, DOCTOR HABILITAT

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