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Explainable AI in healthcare and med...

Buckeridge, David L.

Explainable AI in healthcare and medicinebuilding a culture of transparency and accountability /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable AI in healthcare and medicineedited by Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge.
Reminder of title:	building a culture of transparency and accountability /
other author:	Shaban-Nejad, Arash.
Published:	Cham :Springer International Publishing :2021.
Description:	xxii, 344 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-3-030-53352-6
ISBN:	9783030533526$q(electronic bk.)

Explainable AI in healthcare and medicinebuilding a culture of transparency and accountability /
Explainable AI in healthcare and medicinebuilding a culture of transparency and accountability /[electronic resource] :edited by Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge. - Cham :Springer International Publishing :2021. - xxii, 344 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.9141860-949X ;. - Studies in computational intelligence ;v. 216..

Explainability and Interpretability: Keys to Deep Medicine -- Fast Similar Patient Retrieval from Large Scale Healthcare Data: A Deep Learning-based Binary Hashing Approach -- A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs -- Machine learning discrimination of Parkinson's Disease stages from walk-er-mounted sensors data -- Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Rein-forcement Learning -- A Generalizable Method for Automated Quality Control of Functional Neuroimaging Datasets -- Uncertainty Characterization for Predictive Analytics with Clinical Time Series Data -- A Dynamic Deep Neural Network for Multimodal Clinical Data Analysis -- DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data -- A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Pa-tients from Nonalcoholic Fatty Liver Disease Patients using Electronic Medical Records.

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.

ISBN: 9783030533526$q(electronic bk.)

Standard No.: 10.1007/978-3-030-53352-6doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / E97 2021

Dewey Class. No.: 610.28563

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520 $a 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.
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