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Explainable AI in Healthcare and Medicine

Building a Culture of Transparency and Accountability

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  • © 2021

Overview

Editors:
  1. Arash Shaban-Nejad

    1. Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center (UTHSC), Oak-Ridge National Lab (ORNL), Memphis, USA

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  2. Martin Michalowski

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

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  3. David L. Buckeridge

    1. McGill Clinical & Health Informatics, Montreal, Canada

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  • Highlights the latest advances in explainable AI in health care and medicine by presenting significant findings on theory, methods, systems, and applications
  • Includes revised versions of selected papers presented at the 2020 AAAI International Workshop on Health Intelligence (W3PHIAI2020), held in New York City, USA, on February 7, 2020
  • Interconnects three major fields: artificial intelligence, medicine, and clinical and public health informatics
  • Emphasizes potential and current applications, clinical and public health benefits, and industrial/entrepreneurial opportunities

Part of the book series: Studies in Computational Intelligence (SCI, volume 914)

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Table of contents (32 chapters)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Explainability and Interpretability: Keys to Deep Medicine

    • Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge
    Pages 1-10

  3. Fast Similar Patient Retrieval from Large Scale Healthcare Data: A Deep Learning-Based Binary Hashing Approach

    • Ke Wang, Eryu Xia, Shiwan Zhao, Ziming Huang, Songfang Huang, Jing Mei et al.
    Pages 11-21

  4. A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

    • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Robert Jenssen
    Pages 23-36

  5. Machine Learning Discrimination of Parkinson’s Disease Stages from Walker-Mounted Sensors Data

    • Nabeel Seedat, Vered Aharonson
    Pages 37-44

  6. Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning

    • Taiyu Zhu, Kezhi Li, Pantelis Georgiou
    Pages 45-53

  7. A Generalizable Method for Automated Quality Control of Functional Neuroimaging Datasets

    • Matthew Kollada, Qingzhu Gao, Monika S. Mellem, Tathagata Banerjee, William J. Martin
    Pages 55-68

  8. Uncertainty Characterization for Predictive Analytics with Clinical Time Series Data

    • Yang Guo, Zhengyuan Liu, Savitha Ramasamy, Pavitra Krishnaswamy
    Pages 69-78

  9. A Dynamic Deep Neural Network for Multimodal Clinical Data Analysis

    • Maria Hügle, Gabriel Kalweit, Thomas Hügle, Joschka Boedecker
    Pages 79-92

  10. DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data

    • Ali Oskooei, Sophie Mai Chau, Jonas Weiss, Arvind Sridhar, María Rodríguez Martínez, Bruno Michel
    Pages 93-105

  11. A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Patients from Nonalcoholic Fatty Liver Disease Patients Using Electronic Medical Records

    • Pradyumna Byappanahalli Suresha, Yunlong Wang, Cao Xiao, Lucas Glass, Yilian Yuan, Gari D. Clifford
    Pages 107-113

  12. Visualization of Deep Models on Nursing Notes and Physiological Data for Predicting Health Outcomes Through Temporal Sliding Windows

    • Jienan Yao, Yuyang Liu, Brenna Li, Stephen Gou, Chloe Pou-Prom, Joshua Murray et al.
    Pages 115-129

  13. Constructing Artificial Data for Fine-Tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation

    • Gaurav Singh, Zahra Sabet, John Shawe-Taylor, James Thomas
    Pages 131-145

  14. Character-Level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis

    • Kenya Sakka, Kotaro Nakayama, Nisei Kimura, Taiki Inoue, Yusuke Iwasawa, Ryohei Yamaguchi et al.
    Pages 147-153

  15. Extracting Structured Data from Physician-Patient Conversations by Predicting Noteworthy Utterances

    • Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey P. Bigham, Zachary C. Lipton
    Pages 155-169

  16. A Multi-talent Healthcare AI Bot Platform

    • Martin Horn, Xiang Li, Lin Chen, Sabin Kafle
    Pages 171-177

  17. Natural vs. Artificially Sweet Tweets: Characterizing Discussions of Non-nutritive Sweeteners on Twitter

    • Hande Batan, Dianna Radpour, Ariane Kehlbacher, Judith Klein-Seetharaman, Michael J. Paul
    Pages 179-185

  18. On-line (TweetNet) and Off-line (EpiNet): The Distinctive Structures of the Infectious

    • Byunghwee Lee, Hawoong Jeong, Eun Kyong Shin
    Pages 187-194

  19. Medication Regimen Extraction from Medical Conversations

    • Sai P. Selvaraj, Sandeep Konam
    Pages 195-209

  20. Quantitative Evaluation of Emergency Medicine Resident’s Non-technical Skills Based on Trajectory and Conversation Analysis

    • Kei Sato, Masaki Onishi, Ikushi Yoda, Kotaro Uchida, Satomi Kuroshima, Michie Kawashima
    Pages 211-220
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About this book

This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.

Editors and Affiliations

  • Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center (UTHSC), Oak-Ridge National Lab (ORNL), Memphis, USA

    Arash Shaban-Nejad

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

    Martin Michalowski

  • McGill Clinical & Health Informatics, Montreal, Canada

    David L. Buckeridge

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