| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560435 |
| ISSN: | 1405-5546 |
| Autores: | Krishnan, Gokul S1 Kamath S., Sowmya1 |
| Instituciones: | 1National Institute of Technology Karnataka, Department of Information Technology, Surathkal. India |
| Año: | 2019 |
| Periodo: | Jul-Sep |
| Volumen: | 23 |
| Número: | 3 |
| Paginación: | 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. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Healthcare informatics, Unstructured text, Disease prediction, Ontologies, Natural language processing, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
