| Full text | |
| Author(s): |
Rodrigues-, Jr., Jose F.
[1]
;
Gutierrez, Marco A.
[2]
;
Spadon, Gabriel
[1]
;
Brandoli, Bruno
[3]
;
Amer-Yahia, Sihem
[4]
Total Authors: 5
|
| Affiliation: | [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
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | INFORMATION SCIENCES; v. 545, p. 813-827, FEB 4 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 18/17620-5 - Preventive medicine by means of deep learning techniques applied in healthcare prognosis |
| Grantee: | José Fernando Rodrigues Júnior |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/08376-0 - Analysis and improvement of urban systems using digital maps in the form of complex networks |
| Grantee: | Gabriel Spadon de Souza |
| Support Opportunities: | Scholarships in Brazil - Doctorate |