Abstract

KRISHNAN, Gokul S.  and  KAMATH S., Sowmya. Ontology-driven Text Feature Modeling for Disease Prediction using Unstructured Radiological Notes. Comp. y Sist. [online]. 2019, vol.23, n.3, pp.915-922.  Epub Aug 09, 2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-23-3-3238.

Clinical 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.

Keywords : Healthcare informatics; unstructured text; disease prediction; ontologies; natural language processing.

        · text in English     · English ( pdf )