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dc.contributor.advisorHoare, Terrien
dc.contributor.authorBhat, Akshata
dc.date.accessioned2021-04-28T20:55:51Z
dc.date.available2021-04-28T20:55:51Z
dc.date.issued2021
dc.identifier.citationBhat, A. (2021). Comparison of machine learning V/S deep learning model to predict ICD9 code using text mining techniques. Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4259
dc.description.abstractHealthcare information is usually collected and stored in form of numbers, texts or images. This data consists of important details such as their visits, symptoms, prescriptions, notes or vital statistics of the patients. Most of these documents are huge in amounts and difficult to maintain or access, hence most of the health institutions maintain such details in the form of Electronic Health Records (EHR) in order to avoid manual error and avoid redundancy. This dissertation uses text mining techniques on textual notes from a real time EHR database (MIMIC – III); to identify the most effective vectorization technique to retrieve meaningful information. A comparison among machine learning models alongside of deep learning model is made using the novel H2O framework and Rapid Miner to predict the ICD9 code based on the extracted data.en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.en
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.subjectMachine learningen
dc.subjectDeep Learningen
dc.subjectText data miningen
dc.titleComparison of machine learning V/S deep learning model to predict ICD9 code using text mining techniquesen
dc.typeThesisen
dc.rights.holderCopyright: The publisheren
dc.type.degreenameMSc in Data Analyticsen
dc.type.degreelevelMScen


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