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Artificial Intelligence for Personalized Medicine

Promoting Healthy Living and Longevity

  • Book
  • © 2023

Overview

Editors:
  1. Arash Shaban-Nejad

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

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  2. Martin Michalowski

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Simone Bianco

    1. Altos Labs - Bay Area Institute of Science, Redwood City, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Highlights the latest achievements in the use of AI in personalized medicine and healthcare
  • Interconnects three major fields: Artificial intelligence, Medicine and clinical, and public health informatics
  • Includes revised versions of selected papers presented at the 2023 AAAI Workshop on Health Intelligence

Part of the book series: Studies in Computational Intelligence (SCI, volume 1106)

Included in the following conference series:

Conference proceedings info: W3PHAI 2023.

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About this book

This book aims to highlight the latest achievements in the use of AI in personalized medicine and healthcare delivery. The edited book contains selected papers presented at the 2023 Health Intelligence workshop, co-located with the Thirty-Seven 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 in medicine and public health.

 


Keywords

Table of contents (20 chapters)

  1. Front Matter

    Pages i-xviii
    Download chapter PDF

  2. Artificial Intelligence for Personalized Care, Wellness, and Longevity Research

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

  3. Towards Trust of Explainable AI in Thyroid Nodule Diagnosis

    • Truong Thanh Hung Nguyen, Van Binh Truong, Vo Thanh Khang Nguyen, Quoc Hung Cao, Quoc Khanh Nguyen
    Pages 11-26

  4. Federated Learning over Harmonized Data Silos

    • Dimitris Stripelis, José Luis Ambite
    Pages 27-41

  5. Investigation of Drift Detection for Clinical Text Classification

    • Hammam Abdelwahab, Claudio Martens, Niklas Beck, Dennis Wegener
    Pages 43-56

  6. Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy

    • Alexandre Larouche, Audrey Durand, Richard Khoury, Caroline Sirois
    Pages 57-74

  7. Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients

    • Emma Rocheteau, Ioana Bica, Pietro Liò, Ari Ercole
    Pages 75-93

  8. Bayesian-Based Parameter Estimation to Quantify Trust in Medical Devices

    • Mini Thomas, Omar Boursalie, Reza Samavi, Thomas E. Doyle
    Pages 95-108

  9. EEG Analysis of Neurodevelopmental Disorders by Integrating Wavelet Transform and Visual Analysis

    • Soo-Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong H. Jeong
    Pages 109-121

  10. Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN

    • Anaelia Ovalle, Sunipa Dev, Jieyu Zhao, Majid Sarrafzadeh, Kai-Wei Chang
    Pages 123-136

  11. Identification, Explanation and Clinical Evaluation of Hospital Patient Subtypes

    • Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber, Michael Ambler, Christopher P. Bourdeaux, Alexander Hepburn et al.
    Pages 137-149

  12. Automatically Extracting Information in Medical Dialogue: Expert System and Attention for Labelling

    • **nshi Wang, Xunzhu Tang
    Pages 151-161

  13. Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer’s Disease

    • Maha M. Alwuthaynani, Zahraa S. Abdallah, Raul Santos-Rodriguez
    Pages 163-174

  14. Knowledge Augmentation for Early Depression Detection

    • Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder
    Pages 175-191

  15. Deep Annotation of Therapeutic Working Alliance in Psychotherapy

    • Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf
    Pages 193-207

  16. Neural Topic Modeling of Psychotherapy Sessions

    • Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Ravi Tejwani
    Pages 209-219

  17. BAUFER: A Baseline-Enabled Facial Expression Recognition Pipeline Trained with Limited Annotations

    • Charlotte von Numers, Yinan Yu, Aleksandra Petkova, Emmette Hutchison, Jesper Havsol
    Pages 221-236

  18. Robustness for ECG Classification by Adversarial Training Over Clinical Features

    • Suparshva Jain, Amit Sangroya, Lovekesh Vig, C. Anantaram
    Pages 237-253

  19. A Transformer-Based Deep Learning Algorithm to Auto-Record Undocumented Clinical One-Lung Ventilation Events

    • Zhihua Li, Alexander Nagrebetsky, Sylvia Ranjeva, Nan Bi, Dianbo Liu, Marcos F. Vidal Melo et al.
    Pages 255-272

  20. Analyzing the Trends of Responses to COVID-19 Related Tweets from News Stations: An Analysis of Three Countries

    • Andrew Fisher, Rajesh Sharma, Vijay Mago
    Pages 273-288
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Editors and Affiliations

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

    Arash Shaban-Nejad

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

    Martin Michalowski

  • Altos Labs - Bay Area Institute of Science, Redwood City, USA

    Simone Bianco

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