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dc.contributor.authorKrishnan, G.S.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-31T08:39:08Z-
dc.date.available2020-03-31T08:39:08Z-
dc.date.issued2019-
dc.identifier.citationComputacion y Sistemas, 2019, Vol.23, 3, pp.915-922en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/12392-
dc.description.abstractClinical Decision Support Systems (CDSSs) support medical personnel by offering aid in decision-making and timely interventions in patient care. Typically such systems are built on structured Electronic Health Records (EHRs), which, unfortunately have a very low adoption rate in developing countries at present. In such situations, clinical notes recorded by medical personnel, though unstructured, can be a significant source for rich patient related information. However, conversion of unstructured clinical notes to a structured EHR form is a manual and time consuming task, underscoring a critical need for more efficient, automated methods. In this paper, a generic disease prediction CDSS built on unstructured radiology text reports is proposed. We incorporate word embeddings and clinical ontologies to model the textual features of the patient data for training a feed-forward neural network for ICD9 disease group prediction. The proposed model built on unstructured text outperformed the state-of-the-art model built on structured data by 9% in terms of AUROC and 23% in terms of AUPRC, thus eliminating the dependency on the availability of structured clinical data. 2019 Instituto Politecnico Nacional. All rights reserved.en_US
dc.titleOntology-driven Text Feature Modeling for Disease Prediction using Unstructured Radiological Notesen_US
dc.typeArticleen_US
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