An Explainable AI Model for ICU admission prediction of COVID­19 patients

Δαζέα, Ελένη; Dazea, Eleni

dc.contributor.author	Δαζέα, Ελένη	el
dc.contributor.author	Dazea, Eleni	en
dc.date.accessioned	2021-11-29T10:36:52Z
dc.date.available	2021-11-29T10:36:52Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54114
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21812
dc.rights	Default License
dc.subject	Explainable AI	en
dc.subject	Machine Learning	el
dc.subject	Logic Programming without Negation as Failure	en
dc.subject	Gorgias	en
dc.subject	COVID 19	en
dc.subject	Επεξηγήσιμη τεχνητή νοημοσύνη	el
dc.subject	Μηχανική Μάθηση	el
dc.subject	Λογικός προγραμματισμός χωρίς άρνηση ως αποτυχία	el
dc.subject	Γοργίας	el
dc.title	An Explainable AI Model for ICU admission prediction of COVID19 patients	en
dc.title	Κατασκευή μοντέλου επεξηγήσιμης τεχνητής νοημοσύνης για την πρόβλεψη εισαγωγής σε ΜΕΘ ασθενών με COVID-19	el
heal.type	bachelorThesis
heal.classification	Επιστήμη Υπολογιστών	el
heal.language	el
heal.language	en
heal.access	campus
heal.recordProvider	ntua	el
heal.publicationDate	2021-07-09
heal.abstract	Machine Learning is a field that is widely used in all aspects of our lives. However, despite of the benefits of its use, its function and the process that every model follows to produce a result can not be easily comprehended by a human. Especially in medical applications and self driving cars, it is very important for the user to understand the steps the model takes to reach the solution and the importance of the features of the model, in order to avoid mistakes and improve the model’s functionality. In this thesis, we created an explainable AI model that can predict the ICU admission of COVID19 patients, based on their symptoms and lab results. An important feature of our model is the interpretation and justification of the prediction to the user. This would allow for example a doctor that uses the program to know in advance which patients are more likely to be admitted to the ICU and monitor them and also assess the validity of such prediction. For the creation of the program, we first trained models with a variety of different algorithms in Python, using COVID19 patients’ data from the Sirio Libanes hospital in Sao Paolo, Brazil. Then, we took the model with the highest accuracy, which in this case was used an adaboost algorithm with a random forest weak learner and transferred it in the R language, where we used the InTrees library to create a sum of the most important rules, which we can use to get a good ICU admission prediction. Finally, with the above rules, we created a Prolog program, using the Gorgias framework, which takes as an input some important patient lab results and returns a prediction on whether the patient will be admitted and the key symptoms based on which the program produced that result. The framework for the user’s communication with Gorgias was written in Java.	en
heal.advisorName	Στεφανέας, Πέτρος	el
heal.committeeMemberName	Στεφανέας, Πέτρος	el
heal.committeeMemberName	Παπασπύρου, Νικόλαος	el
heal.committeeMemberName	Παγουρτζής, Αριστείδης	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false