Explainable Analytics to Predict the Quality of Life in Patients with Prostate Cancer from Longitudinal Data-Reference-Cited by-同舟云学术

Explainable Analytics to Predict the Quality of Life in Patients with Prostate Cancer from Longitudinal Data

Published:2022-04-15 Issue:1 Volume:36 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Sharifi Fatemeh¹^ORCID,Mohammed Emad²^ORCID,Crump Trafford³^ORCID,Far Behrouz H.¹^ORCID

Affiliation:

1. Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada

2. Faculty of Science, Department of Engineering, Thompson Rivers University, Kamloops, BC, Canada

3. Department of Surgery, University of Calgary, Calgary, AB, Canada

Funder

Conjoint Health Research Ethics Board at the University of Calgary

Publisher

Informa UK Limited

Subject

Artificial Intelligence

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2022.2055393

Reference50 articles.

1. Barik, A., and J. Honorio. 2019. Learning bayesian networks with low rank conditional probability tables. Advances in Neural Information Processing Systems 32.

2. Development of a Ready-to-Use Graphical Tool Based on Artificial Neural Network Classification: Application for the Prediction of Late Fecal Incontinence After Prostate Cancer Radiation Therapy

3. Comparison on efficacy of radical prostatectomy versus external beam radiotherapy for the treatment of localized prostate cancer

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