Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients

Kuo Chun Chiu; Dongping Du; Nandini Nair; Yuncheng Du

doi:10.1109/EMBC46164.2021.9630234

Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients

Kuo Chun Chiu
, Dongping Du
, Nandini Nair
, Yuncheng Du

Department of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.

Original language	English (US)
Title of host publication	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2144-2147
Number of pages	4
ISBN (Electronic)	9781728111797
DOIs	https://doi.org/10.1109/EMBC46164.2021.9630234
State	Published - 2021
Event	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico Duration: Nov 1 2021 → Nov 5 2021

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Country/Territory	Mexico
City	Virtual, Online
Period	11/1/21 → 11/5/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC46164.2021.9630234

Cite this

Chiu, K. C., Du, D., Nair, N., & Du, Y. (2021). Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 (pp. 2144-2147). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC46164.2021.9630234

Chiu, Kuo Chun ; Du, Dongping ; Nair, Nandini et al. / Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2144-2147 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{72c63b57c736415e8e367605a0844beb,

title = "Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients",

abstract = "Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.",

author = "Chiu, \{Kuo Chun\} and Dongping Du and Nandini Nair and Yuncheng Du",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 ; Conference date: 01-11-2021 Through 05-11-2021",

year = "2021",

doi = "10.1109/EMBC46164.2021.9630234",

language = "English (US)",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2144--2147",

booktitle = "43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021",

address = "United States",

}

Chiu, KC, Du, D, Nair, N & Du, Y 2021, Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients. in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 2144-2147, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, Mexico, 11/1/21. https://doi.org/10.1109/EMBC46164.2021.9630234

Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients. / Chiu, Kuo Chun; Du, Dongping; Nair, Nandini et al.
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2144-2147 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients

AU - Chiu, Kuo Chun

AU - Du, Dongping

AU - Nair, Nandini

AU - Du, Yuncheng

PY - 2021

Y1 - 2021

N2 - Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.

AB - Heart-transplant recipients are at high risk of developing skin cancer, while Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) are commonly detected. This paper utilized the database from the United Network for Organ Sharing (UNOS) to study the incidence rate of SCC and BCC among heart transplant recipients. Cox proportional hazards model and two deep neural network-based models were studied, and their performance were compared. In addition, Lasso regression, Chi-square test, and Wilcoxon signed-rank test were applied to identify key risk factors. The neural network-based survival models showed better accuracy compared to the standard Cox regression model, which indicates the advantage of deep learning approaches in survival analysis and risk prediction for post-transplant skin cancer.This study investigates the performance of deep learning (DL) models in clinical applications for predicting the risk of skin cancer in heart transplant recipients. The DL models outperform the standard models in assessing the incidence rate of skin cancer across different time spans.

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M3 - Conference contribution

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AN - SCOPUS:85122529543

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 2144

EP - 2147

BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 1 November 2021 through 5 November 2021

ER -

Chiu KC, Du D, Nair N, Du Y. Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2144-2147. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC46164.2021.9630234

Deep Neural Network-Based Survival Analysis for Skin Cancer Prediction in Heart Transplant Recipients

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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