Revista: | Computación y sistemas |
Base de datos: | |
Número de sistema: | 000560435 |
ISSN: | 1405-5546 |
Autors: | Krishnan, Gokul S1 Kamath S., Sowmya1 |
Institucions: | 1National Institute of Technology Karnataka, Department of Information Technology, Surathkal. India |
Any: | 2019 |
Període: | Jul-Sep |
Volum: | 23 |
Número: | 3 |
Paginació: | 915-922 |
País: | México |
Idioma: | Inglés |
Tipo de documento: | Artículo |
Resumen en inglés | 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. |
Disciplines | Ciencias de la computación |
Paraules clau: | Inteligencia artificial |
Keyword: | Healthcare informatics, Unstructured text, Disease prediction, Ontologies, Natural language processing, Artificial intelligence |
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