Tutorials

Algorithmic Fairness in Multimodal Machine Learning for Health Informatics

Sunday | November 10, 2024 | 9:00 – 17:00

The proposed tutorial is both timely and appropriate for the BHI community. As the integration of multimodal data—including text, audio, physiology, and images—becomes increasingly prevalent in health informatics research and applications, ensuring fairness and equity in algorithmic decision-making is crucial. This tutorial will address emerging challenges in mitigating biases that may arise from diverse data sources, design of machine learning (ML) algorithms, and ML-supported clinical decision-making. By highlighting state-of-the-art methodologies, tools, and best practices, the tutorial aims to equip researchers and practitioners with the knowledge to critically assess and enhance the fairness of their models. Given the growing importance of ethical artificial intelligence (AI) and the need for inclusive healthcare solutions, this tutorial is highly relevant to BHI 2024, aligning with its focus on the responsible application of AI in health.

Preliminary Program

9.10-9.15 Introductory Remarks

9.15-10.00 Fairness in AI/ML: From Algorithms to Applications in the Data Science Lifecycle (In-person)

Na Zou, Department of Industrial Engineering, University of Houston

9.15-10.00 Coffee Break

10.15-11.00 Building Fair-Aware Frameworks for Mental Health: Addressing AI Bias and Ethical Challenges in Digital Therapeutics (Online)

Adela C. Timmons, Department of Psychology, The University of Texas at Austin

11:00-11.15 Break

11.15-12.00 Fairness and bias in organ transplant: From AI perspective (Online)

Sirui Ding, Bakar Computational Health Sciences Institute, University of California San Fransisco

12:00-13.45 Lunch Break

13:45-15.00 Benchmarking Algorithmic Fairness for Health Informatics & Tutorial (In-person)

Xiaotian (Max) Han, Department of Computer and Data Sciences, Case Western Reserve University

15:00-15.30 Coffee Break

15:30-16.15 Demographic bias in speech-based machine learning models for digital health (In-person)

Theodora Chaspari, Institute of Cognitive Science & Department of Computer Science, University of Colorado Boulder

16:15-16.30 Discussions

Theodora Chaspari

University of Colorado Boulder, USA

Adela C. Timmons

The University of Texas at Austin, USA

Na Zou

University of Houston, USA

Sirui Ding

University of California San Francisco, USA

Xiaotian (Max) Han

Case Western Reserve University, USA

Tools and techniques for learning digital twins for biomedical applications

Sunday | November 10, 2024 | 9:00 – 12:30

Digital twins are an exciting new area with applications in many biomedical fields including precision medicine, generation of patient specific data to aid in AI for health, and large scale in-silico testing of devices and drugs. However, a serious bottleneck in real world usage of digital twins is their fidelity. Can digital twins be accurately learned from real world data? This question is at the center of any attempt at using digital twins in medicine and health. In this tutorial, we take a deep dive into the challenges of learning digital twins from real world data, which include digital twin structures, first principle based model recovery, data scarcity, data heterogeneity, data privacy and data requirement trade-off, learning from incomplete knowledge, and generalization across domains. We then discuss tools and techniques both state-of-art and cutting-edge novel research, to address these challenges. This tutorial directly addresses the conference theme of Deep Medicine and AI for Health.

Ayan Banerjee

Arizona State University, USA

Marzia Cescon

University of Houston, USA

Sandeep K.S. Gupta

Arizona State University, USA