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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022
EditorsRajashree Paul
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages159-162
Number of pages4
ISBN (Electronic)9781665483032
DOIs
StatePublished - 2022
Event12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022 - Virtual, Las Vegas, United States
Duration: Jan 26 2022Jan 29 2022

Publication series

Name2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022

Conference

Conference12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022
Country/TerritoryUnited States
CityVirtual, Las Vegas
Period1/26/221/29/22

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

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