Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows

Author:

Tan Yanchao1ORCID,Zhang Hengyu2ORCID,Zhou Zihao1ORCID,Ma Guofang3ORCID,Wang Fan2ORCID,Liu Weiming2ORCID,Liao Xinting2ORCID,Hertzberg Vicki S4ORCID,Yang Carl5ORCID

Affiliation:

1. College of Computer and Data Science, Fuzhou University, Fuzhou, China

2. College of Computer Science, Zhejiang University, Hangzhou, China

3. School of Computer Science, Zhejiang Gongshang University, Hangzhou, China

4. Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA

5. Department of Computer Science, Emory University, Atlanta, GA, USA

Funder

Zhejiang Gongshang University Digital+ Disciplinary Construction Management Project

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

ACM

Reference21 articles.

1. Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time

2. J. Bose, A. Smofsky, R. Liao, P. Panangaden, and W. Hamilton. Latent variable modelling with hyperbolic normalizing flows. In International Conference on Machine Learning, pages 1045--1055. PMLR, 2020.

3. R. T. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud. Neural ordinary differential equations. In NeurIPS, 2018.

4. E. Choi, M. T. Bahadori, J. A. Kulas, A. Schuetz, W. F. Stewart, and J. Sun. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In NeurIPS, 2016.

5. GRAM

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