Lig-Doctor: real-world clinical prognosis using a bi-directional neural network

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

• 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