Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis

Yashbir Singh; William Jons; Joseph D. Sobek; John E. Eaton; Bradley J. Erickson; Barrett J. Anderies; Jaidip Jagtap

doi:10.1109/CCWC54503.2022.9720887

Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis

Yashbir Singh, William Jons, Joseph D. Sobek, John E. Eaton, Bradley J. Erickson, Barrett J. Anderies, Jaidip Jagtap

Radiology

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

Abstract

This paper proposes a computationally efficient method for estimating the topology of manifold data in the context of medical applications. Betti numbers computed with persistent homology tools can be more useful for hepatic decompensation prediction in patients with Primary Sclerosing Cholangitis. We propose an alternative method that uses Betti numbers to estimate hepatic decompensation status. The results show that the proposed methodology is capable of distinguishing between hepatic decompensation and non-hepatic decompensation status. We discovered that using a Betti number-based machine-learning approach, we can make accurate predictions from small datasets, such as predicting who is likely to have hepatic decompensation and those who do not.

Original language	English (US)
Title of host publication	2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022
Editors	Rajashree Paul
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	159-162
Number of pages	4
ISBN (Electronic)	9781665483032
DOIs	https://doi.org/10.1109/CCWC54503.2022.9720887
State	Published - 2022
Event	12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022 - Virtual, Las Vegas, United States Duration: Jan 26 2022 → Jan 29 2022

Publication series

Name	2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022

Conference

Conference	12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022
Country/Territory	United States
City	Virtual, Las Vegas
Period	1/26/22 → 1/29/22

Keywords

Betti number
Hepatic decompensation
Machine learning
Magnetic resonance
Topological data analysis

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1109/CCWC54503.2022.9720887

Cite this

Singh, Y., Jons, W., Sobek, J. D., Eaton, J. E., Erickson, B. J., Anderies, B. J., & Jagtap, J. (2022). Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis. In R. Paul (Ed.), 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022 (pp. 159-162). (2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCWC54503.2022.9720887

Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis. / Singh, Yashbir; Jons, William; Sobek, Joseph D. et al.

2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022. ed. / Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2022. p. 159-162 (2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022).

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

Singh, Y, Jons, W, Sobek, JD, Eaton, JE, Erickson, BJ, Anderies, BJ & Jagtap, J 2022, Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis. in R Paul (ed.), 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022. 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 159-162, 12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022, Virtual, Las Vegas, United States, 1/26/22. https://doi.org/10.1109/CCWC54503.2022.9720887

Singh Y, Jons W, Sobek JD, Eaton JE, Erickson BJ, Anderies BJ et al. Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis. In Paul R, editor, 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 159-162. (2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022). doi: 10.1109/CCWC54503.2022.9720887

Singh, Yashbir ; Jons, William ; Sobek, Joseph D. et al. / Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis. 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022. editor / Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 159-162 (2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022).

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abstract = "This paper proposes a computationally efficient method for estimating the topology of manifold data in the context of medical applications. Betti numbers computed with persistent homology tools can be more useful for hepatic decompensation prediction in patients with Primary Sclerosing Cholangitis. We propose an alternative method that uses Betti numbers to estimate hepatic decompensation status. The results show that the proposed methodology is capable of distinguishing between hepatic decompensation and non-hepatic decompensation status. We discovered that using a Betti number-based machine-learning approach, we can make accurate predictions from small datasets, such as predicting who is likely to have hepatic decompensation and those who do not.",

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author = "Yashbir Singh and William Jons and Sobek, {Joseph D.} and Eaton, {John E.} and Erickson, {Bradley J.} and Anderies, {Barrett J.} and Jaidip Jagtap",

note = "Funding Information: ACKNOWLEDGMENT This research work has been supported by Halloran PSC Benefactors Fund and we would like to thank the effort of Radiology Informatics Lab members at Mayo Clinic Rochester. Funding Information: This research work has been supported by Halloran PSC Benefactors Fund and we would like to thank the effort of Radiology Informatics Lab members at Mayo Clinic Rochester Publisher Copyright: {\textcopyright} 2022 IEEE.; 12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022 ; Conference date: 26-01-2022 Through 29-01-2022",

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doi = "10.1109/CCWC54503.2022.9720887",

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AU - Singh, Yashbir

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AU - Sobek, Joseph D.

AU - Eaton, John E.

AU - Erickson, Bradley J.

AU - Anderies, Barrett J.

AU - Jagtap, Jaidip

N1 - Funding Information: ACKNOWLEDGMENT This research work has been supported by Halloran PSC Benefactors Fund and we would like to thank the effort of Radiology Informatics Lab members at Mayo Clinic Rochester. Funding Information: This research work has been supported by Halloran PSC Benefactors Fund and we would like to thank the effort of Radiology Informatics Lab members at Mayo Clinic Rochester Publisher Copyright: © 2022 IEEE.

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N2 - This paper proposes a computationally efficient method for estimating the topology of manifold data in the context of medical applications. Betti numbers computed with persistent homology tools can be more useful for hepatic decompensation prediction in patients with Primary Sclerosing Cholangitis. We propose an alternative method that uses Betti numbers to estimate hepatic decompensation status. The results show that the proposed methodology is capable of distinguishing between hepatic decompensation and non-hepatic decompensation status. We discovered that using a Betti number-based machine-learning approach, we can make accurate predictions from small datasets, such as predicting who is likely to have hepatic decompensation and those who do not.

AB - This paper proposes a computationally efficient method for estimating the topology of manifold data in the context of medical applications. Betti numbers computed with persistent homology tools can be more useful for hepatic decompensation prediction in patients with Primary Sclerosing Cholangitis. We propose an alternative method that uses Betti numbers to estimate hepatic decompensation status. The results show that the proposed methodology is capable of distinguishing between hepatic decompensation and non-hepatic decompensation status. We discovered that using a Betti number-based machine-learning approach, we can make accurate predictions from small datasets, such as predicting who is likely to have hepatic decompensation and those who do not.

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KW - Magnetic resonance

KW - Topological data analysis

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Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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