| Texto completo | |
| Autor(es): |
Rodrigues-, Jr., Jose F.
[1]
;
Gutierrez, Marco A.
[2]
;
Spadon, Gabriel
[1]
;
Brandoli, Bruno
[3]
;
Amer-Yahia, Sihem
[4]
Número total de Autores: 5
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Math Sci & Comp, Av Trab Sao Carlense 400, Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Heart Inst, Av Dr Eneas C Aguiar 44, Sao Paulo, SP - Brazil
[3] Dalhousie Univ, Inst Big Data Analyt, 6050 Univ Ave, Halifax, NS - Canada
[4] Univ Grenoble Alpes, IMAG, CNRS, 700 Av Cent, St Martin Dheres - France
Número total de Afiliações: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INFORMATION SCIENCES; v. 545, p. 813-827, FEB 4 2021. |
| Citações Web of Science: | 0 |
| Resumo | |
The interest for patient trajectory prediction, a sort of computer-aided medicine, has steadily increased with the pace of artificial intelligence innovation. Notwithstanding, the design of effective systems able to predict clinical outcomes based on the history of a patient is far from trivial. Works so far are based on neural architectures with low performance, especially when using low-cardinality datasets; alternatively, complex inference approaches are hard to reproduce and/or extrapolate as they are designed for very specific circumstances. We introduce LIG-Doctor, an artificial neural network architecture based on two Minimal Gated Recurrent Unit networks functioning in a bidirectional parallel manner, benefiting from temporal events both forward and backward. In comparison to state-of-the-art works, consistent improvements were achieved in prognosis prediction, as assessed with metrics Recall@k, Precision@k, F1-score, and AUC-ROC. Besides the detailed delineation of our architecture, a sequence of experiments is reported with insights that progressively guided design decisions to inspire future works on similar problems. Our results shall contribute to the improvement of computer-aided medicine and, more generally, to processes related to the design of neural network architectures. (C) 2020 Elsevier Inc. All rights reserved. (AU) | |
| 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: | 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: | 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 |