Trustworthy Computing for Biomedical Challenges

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This tutorial focuses on the role of trustworthy machine learning techniques in transforming healthcare through the analysis of vast and intricate medical data. It covers the core techniques of accuracy, robustness, fairness, and interpretability in computational medicine, including their challenges and potential for future development. The tutorial aims to provide a comprehensive overview of trustworthy machine learning techniques that can be adopted in healthcare and is suitable for audiences with a background in biomedicine and machine learning. By emphasizing the importance of trustworthy machine learning in healthcare, the tutorial aims to offer insights into how these techniques can lead to more precise diagnoses and treatment regimes.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages534-535
Number of pages2
ISBN (Electronic)9798350302639
DOIs
StatePublished - 2023
Event11th IEEE International Conference on Healthcare Informatics, ICHI 2023 - Houston, United States
Duration: Jun 26 2023Jun 29 2023

Publication series

NameProceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023

Conference

Conference11th IEEE International Conference on Healthcare Informatics, ICHI 2023
Country/TerritoryUnited States
CityHouston
Period6/26/236/29/23

Keywords

  • genetics
  • health informatics
  • interpretability
  • robustness
  • trustworthy machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Health Informatics

Fingerprint

Dive into the research topics of 'Trustworthy Computing for Biomedical Challenges'. Together they form a unique fingerprint.

Cite this