| Texto completo | |
| Autor(es): |
Rodrigues-Jr, Jose F.
;
Spadon, Gabriel
;
Brandoli, Bruno
;
Amer-Yahia, Sihem
;
DeHerrera, AGS
;
Gonzalez, AR
;
Santosh, KC
;
Temesgen, Z
;
Kane, B
;
Soda, P
Número total de Autores: 10
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020); v. N/A, p. 4-pg., 2020-01-01. |
| Resumo | |
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, can predict probable future conditions. Previous works have tackled the problem by using artificial neural network architectures that do not benefit from bi-directional temporal processing, or by utilizing non-generalizable inference approaches. Differently, we introduce a Deep Learning architecture whose design results from an intensive experimental process; our final architecture is based on two parallel Minimal Gated Recurrent Unit networks working in bi-directional manner, which was extensively tested with two real-world datasets. Our results demonstrate significant improvements in automated medical prognosis, as measured with metrics Precision@, Recall@, F1-Score, and AUC-ROC. We contribute with an architecture and with insights for the design of Deep Learning architectures. (AU) | |
| Processo FAPESP: | 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD) |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 18/17620-5 - Medicina preventiva por meio de técnicas de deep learning aplicadas ao prognóstico de saúde |
| Beneficiário: | José Fernando Rodrigues Júnior |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |
| Processo FAPESP: | 17/08376-0 - Análise e aperfeiçoamento de sistemas urbanos por meio de mapas digitais representados por redes complexas |
| Beneficiário: | Gabriel Spadon de Souza |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |