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Multimodal AI in Healthcare

A Paradigm Shift in Health Intelligence

  • Book
  • © 2023

Overview

Editors:
  1. Arash Shaban-Nejad

    1. Oak-Ridge National Laboratory (ORNL), Center for Biomedical Informatics, The University of Tennessee Health Science Center (UTHSC), Memphis, USA

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  2. Martin Michalowski

    1. School of Nursing, University of Minnesota, Minneapolis, USA

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  3. Simone Bianco

    1. Research Center, IBM Almaden, San Jose, USA

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  • Highlight2sthe latest achievements in the use of Multimodal AI in medicine and healthcare
  • Includes revised versions of selected papers presented at the 2022 AAAI Workshop on Health Intelligence
  • Interconnects three major fields: Artificial intelligence, Medicine and clinical and public health informatics

Part of the book series: Studies in Computational Intelligence (SCI, volume 1060)

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Table of contents (30 chapters)

  1. Front Matter

    Pages i-xxii
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  2. Multimodal Artificial Intelligence: Next Wave of Innovation in Healthcare and Medicine

    • Arash Shaban-Nejad, Martin Michalowski, Simone Bianco
    Pages 1-9

  3. Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge

    • Ashwani Tanwar, Jingqing Zhang, Julia Ive, Vibhor Gupta, Yike Guo
    Pages 11-28

  4. Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations

    • Zhengyuan Liu, Pavitra Krishnaswamy, Nancy F. Chen
    Pages 29-40

  5. Clinical Dialogue Transcription Error Correction Using Seq2Seq Models

    • Gayani Nanayakkara, Nirmalie Wiratunga, David Corsar, Kyle Martin, Anjana Wijekoon
    Pages 41-57

  6. Customized Training of Pretrained Language Models to Detect Post Intents in Online Health Support Groups

    • Tootiya Giyahchi, Sameer Singh, Ian Harris, Cornelia Pechmann
    Pages 59-75

  7. EXPECT-NLP: An Integrated Pipeline and User Interface for Exploring Patient Preferences Directly from Patient-Generated Text

    • David Johnson, Nick Dragojlovic, Nicola Kopac, Yifu Chen, Marilyn Lenzen, Sarah Le Huray et al.
    Pages 77-89

  8. Medication Error Detection Using Contextual Language Models

    • Yu Jiang, Christian Poellabauer
    Pages 91-99

  9. Latent Representation Weights Learning of the Indefinite Length of Views for Conception Diagnosis

    • Bo Li, Mengze Sun, Yuan Yu, Yuanyuan Zhao, Zhongliang Xiang, Zhiyong An
    Pages 101-116

  10. Phenotyping with Positive Unlabelled Learning for Genome-Wide Association Studies

    • Andre Vauvelle, Hamish Tomlinson, Aaron Sim, Spiros Denaxas
    Pages 117-135

  11. Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation

    • Karina Zadorozhny, Patrick Thoral, Paul Elbers, Giovanni Cinà
    Pages 137-153

  12. A Robust System to Detect and Explain Public Mask Wearing Behavior

    • Akshay Gupta, Biplav Srivastava
    Pages 155-169

  13. A Federated Cox Model with Non-proportional Hazards

    • D. Kai Zhang, Francesca Toni, Matthew Williams
    Pages 171-185

  14. A Step Towards Automated Functional Assessment of Activities of Daily Living

    • Bappaditya Debnath, Mary O’brien, Swagat Kumar, Ardhendu Behera
    Pages 187-202

  15. The Interpretation of Deep Learning Based Analysis of Medical Images—An Examination of Methodological and Practical Challenges Using Chest X-ray Data

    • Steinar Valsson, Ognjen Arandjelović
    Pages 203-213

  16. Predicting Drug Functions from Adverse Drug Reactions by Multi-label Deep Neural Network

    • Pranab Das, Dilwar Hussain Mazumder
    Pages 215-226

  17. Pattern Discovery in Physiological Data with Byte Pair Encoding

    • Nazgol Tavabi, Kristina Lerman
    Pages 227-243

  18. Predicting ICU Admissions for Hospitalized COVID-19 Patients with a Factor Graph-based Model

    • Yurui Cao, Phuong Cao, Haotian Chen, Karl M. Kochendorfer, Andrew B. Trotter, William L. Galanter et al.
    Pages 245-256

  19. Semantic Network Analysis of COVID-19 Vaccine Related Text from Reddit

    • Chad A. Melton, Jintae Bae, Olufunto A. Olusanya, Jon Hael Brenas, Eun Kyong Shin, Arash Shaban-Nejad
    Pages 257-266

  20. Towards Providing Clinical Insights on Long Covid from Twitter Data

    • Rohan Bhambhoria, Jad Saab, Sara Uppal, Xin Li, Artur Yakimovich, Junaid Bhatti et al.
    Pages 267-278
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Keywords

About this book

This book aims to highlight the latest achievements in the use of AI and multimodal artificial intelligence in biomedicine and healthcare. Multimodal AI is a relatively new concept in AI, in which different types of data (e.g. text, image, video, audio, and numerical data) are collected, integrated, and processed through a series of intelligence processing algorithms to improve performance. The edited volume contains selected papers presented at the 2022 Health Intelligence workshop and the associated Data Hackathon/Challenge, co-located with the Thirty-Sixth Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI and Multimodal AI in public health and medicine.

Editors and Affiliations

  • Oak-Ridge National Laboratory (ORNL), Center for Biomedical Informatics, The University of Tennessee Health Science Center (UTHSC), Memphis, USA

    Arash Shaban-Nejad

  • School of Nursing, University of Minnesota, Minneapolis, USA

    Martin Michalowski

  • Research Center, IBM Almaden, San Jose, USA

    Simone Bianco

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