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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Fortran Reference Guide
FORTRAN REFERENCE GUIDE Version 2018 TABLE OF CONTENTS Preface............................................................................................................ xv Audience Description......................................................................................... xv Compatibility and Conformance to Standards............................................................ xv Organization................................................................................................... xvi Hardware and Software Constraints...................................................................... xvii Conventions................................................................................................... xvii Related Publications........................................................................................ xviii Chapter 1. Language Overview............................................................................... 1 1.1. Elements of a Fortran Program Unit.................................................................. 1 1.1.1. Fortran Statements................................................................................. 1 1.1.2. Free and Fixed Source............................................................................. 2 1.1.3. Statement Ordering................................................................................. 2 1.2. The Fortran Character Set.............................................................................. 3 1.3. Free Form Formatting.................................................................................. -
Introduction to Linux on System Z
IBM Linux and Technology Center Introduction to Linux on System z Mario Held IBM Lab Boeblingen, Germany © 2009 IBM Corporation IBM Linux and Technology Center Trademarks The following are trademarks of the International Business Machines Corporation in the United States, other countries, or both. Not all common law marks used by IBM are listed on this page. Failure of a mark to appear does not mean that IBM does not use the mark nor does it mean that the product is not actively marketed or is not significant within its relevant market. Those trademarks followed by ® are registered trademarks of IBM in the United States; all others are trademarks or common law marks of IBM in the United States. For a complete list of IBM Trademarks, see www.ibm.com/legal/copytrade.shtml: *, AS/400®, e business(logo)®, DBE, ESCO, eServer, FICON, IBM®, IBM (logo)®, iSeries®, MVS, OS/390®, pSeries®, RS/6000®, S/30, VM/ESA®, VSE/ESA, WebSphere®, xSeries®, z/OS®, zSeries®, z/VM®, System i, System i5, System p, System p5, System x, System z, System z9®, BladeCenter® The following are trademarks or registered trademarks of other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both. -
AIX Migration to Cloud with IBM Power Virtual Server
AIX Migration to Cloud with IBM Power Virtual Server An IBM Systems Lab Services Tutorial Aaron Bolding Berjis Patel Vess Natchev [email protected] TABLE OF CONTENTS CHAPTER 1: SOLUTION OVERVIEW............................. 1 Introduction ................................................................................ 1 Use Cases .................................................................................. 1 Migration via PowerVC OVA ..................................................... 1 Transfer System Backup Using the Public Internet ..................... 2 Transfer System Backup Using Cloud Object Storage ................. 2 Solution Components and Requirements ........................................ 2 Components .......................................................................... 2 Migration via PowerVC OVA ..................................................... 2 Transfer System Backup Using the Public Internet ..................... 2 Transfer System Backup Using Cloud Object Storage ................. 2 Requirements ........................................................................ 3 Solution Diagrams ....................................................................... 3 Transfer System Backup Using the Public Internet ..................... 3 Transfer System Backup Using Cloud Object Storage ................. 4 CHAPTER 2: IMPLEMENTATION .................................. 5 Migration via PowerVC OVA .......................................................... 5 Procedure to Configure IBM Cloud Object Storage ..................... -
Integration of System for Research in Health with Computation in Cloud Using Artificial Intelligence
MOJ Proteomics & Bioinformatics Research Article Open Access Integration of system for research in health with computation in cloud using artificial intelligence Abstract Volume 7 Issue 4 - 2018 Currently the use of information technology is often applied in the health area. With regard 1 to scientific research, we have the SINPE© Integrated System of Electronic Protocols. Carlos Henrique Kuretzki, José Simão de 2 3 The purpose of this tool is to support the researcher in this area who until now did not Paula Pinto, José Claudio Vianna have statistical tests. The main objective of this work is to provide SINPE© users with the 1Professor of the Program for Analysis and Development of improvement in health data analysis through the use of cognitive computing technologies. Systems and Computer Engineering, Federal University, Brazil 2Professor of the Information Management Program, Federal Keywords: artificial intelligence, cloud computing, information systems University, Brazil 3Professor of the Computer Engineering, Universidade Positivo, Brazil Correspondence: Carlos Henrique Kuretzki, Professor of the Program for Analysis and Development of Systems and Computer Engineering, Federal University, Brazil, Email [email protected] Received: June 29, 2018 | Published: August 08, 2018 Introduction more quickly.4 IBM Watson is a cognitive computing technology and have been used to investigate the biological and health sciences. The health sector is one of the largest in most countries and can It is based on medical literature, patents, genetic, and chemical and significantly benefit from high‒quality, real‒time and location‒ pharmacological data.5 The IBM’s Watson platform combines some independent data. However, many healthcare professionals are capabilities, building a complete cognitive system, which is being not familiar with information technology solutions, business and called the new era of computing. -
RACF Command Tips
RACF Command Tips SHARE ‐ March 2015 Session 18875 RSH Consulting ‐ Robert S. Hansel RSH Consulting, Inc. is an IT security professional services firm established in 1992 and dedicated to helping clients strengthen their IBM z/OS mainframe access controls by fully exploiting all the capabilities and latest innovations in RACF. RSH's services include RACF security reviews and audits, initial implementation of new controls, enhancement and remediation of existing controls, and training. • www.rshconsulting.com • 617‐969‐9050 Robert S. Hansel is Lead RACF Specialist and founder of RSH Consulting, Inc. He began working with RACF in 1986 and has been a RACF administrator, manager, auditor, instructor, developer, and consultant. Mr. Hansel is especially skilled at redesigning and refining large‐scale implementations of RACF using role‐based access control concepts. He is a leading expert in securing z/OS Unix using RACF. Mr. Hansel has created elaborate automated tools to assist clients with RACF administration, database merging, identity management, and quality assurance. • 617‐969‐8211 • [email protected] • www.linkedin.com/in/roberthansel • http://twitter.com/RSH_RACF RACF Command Tips SHARE 2 © 2016 RSH Consulting, Inc. All Rights Reserved. March 2016 Topics . User Commands . Group Commands . Dataset Command . General Resource Commands . PERMIT Command . Generic Profile Refresh . List Commands . SEARCH Command . Console Command Entry . Building Commands with Microsoft Excel RACF and z/OS are Trademarks of the International Business Machines Corporation RACF Command Tips SHARE 3 © 2016 RSH Consulting, Inc. All Rights Reserved. March 2016 User Commands . ADDUSER Defaults: • OWNER ‐ Creator's ID • DFLTGRP ‐ Creator's Current Connect Group • PASSWORD ‐ Pre‐z/OS 2.2: Default Group z/OS 2.2: NOPASSWORD • Always specify when creating new ID . -
Data Transfer Options and Requirements
Data Transfer Options and Requirements Original date 25 Jan 2005 Revision date 21 Aug 2018 Proprietary and Confidential Data Transfer Options and Requirements Summary of changes Date Short description of changes September 2016 Updated name of submission tool to SDSS. August 2018 Rebranded. Contacting support Support is available through the following link: https://www.ibm.com/software/support/watsonhealth/truven_support.html © Copyright IBM Corporation 2018 IBM Confidential 2 Data Transfer Options and Requirements Contents Summary of changes .................................................................................................................................... 2 Contacting support ........................................................................................................................... 2 Contents ........................................................................................................................................................ 3 Overview ....................................................................................................................................................... 5 Electronic data transfer methods ..................................................................................................... 5 Data suppliers ..................................................................................................................... 5 Data recipients ................................................................................................................... -
Implementing Nfsv4 in the Enterprise: Planning and Migration Strategies
Front cover Implementing NFSv4 in the Enterprise: Planning and Migration Strategies Planning and implementation examples for AFS and DFS migrations NFSv3 to NFSv4 migration examples NFSv4 updates in AIX 5L Version 5.3 with 5300-03 Recommended Maintenance Package Gene Curylo Richard Joltes Trishali Nayar Bob Oesterlin Aniket Patel ibm.com/redbooks International Technical Support Organization Implementing NFSv4 in the Enterprise: Planning and Migration Strategies December 2005 SG24-6657-00 Note: Before using this information and the product it supports, read the information in “Notices” on page xi. First Edition (December 2005) This edition applies to Version 5, Release 3, of IBM AIX 5L (product number 5765-G03). © Copyright International Business Machines Corporation 2005. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . xi Trademarks . xii Preface . xiii The team that wrote this redbook. xiv Acknowledgments . xv Become a published author . xvi Comments welcome. xvii Part 1. Introduction . 1 Chapter 1. Introduction. 3 1.1 Overview of enterprise file systems. 4 1.2 The migration landscape today . 5 1.3 Strategic and business context . 6 1.4 Why NFSv4? . 7 1.5 The rest of this book . 8 Chapter 2. Shared file system concepts and history. 11 2.1 Characteristics of enterprise file systems . 12 2.1.1 Replication . 12 2.1.2 Migration . 12 2.1.3 Federated namespace . 13 2.1.4 Caching . 13 2.2 Enterprise file system technologies. 13 2.2.1 Sun Network File System (NFS) . 13 2.2.2 Andrew File System (AFS) . -
Treatment and Differential Diagnosis Insights for the Physician's
Treatment and differential diagnosis insights for the physician’s consideration in the moments that matter most The role of medical imaging in global health systems is literally fundamental. Like labs, medical images are used at one point or another in almost every high cost, high value episode of care. Echocardiograms, CT scans, mammograms, and x-rays, for example, “atlas” the body and help chart a course forward for a patient’s care team. Imaging precision has improved as a result of technological advancements and breakthroughs in related medical research. Those advancements also bring with them exponential growth in medical imaging data. The capabilities referenced throughout this document are in the research and development phase and are not available for any use, commercial or non-commercial. Any statements and claims related to the capabilities referenced are aspirational only. There were roughly 800 million multi-slice exams performed in the United States in 2015 alone. Those studies generated approximately 60 billion medical images. At those volumes, each of the roughly 31,000 radiologists in the U.S. would have to view an image every two seconds of every working day for an entire year in order to extract potentially life-saving information from a handful of images hidden in a sea of data. 31K 800MM 60B radiologists exams medical images What’s worse, medical images remain largely disconnected from the rest of the relevant data (lab results, patient-similar cases, medical research) inside medical records (and beyond them), making it difficult for physicians to place medical imaging in the context of patient histories that may unlock clues to previously unconsidered treatment pathways. -
IBM SPSS Decision Trees Business Analytics
IBM Software IBM SPSS Decision Trees Business Analytics IBM SPSS Decision Trees Easily identify groups and predict outcomes IBM® SPSS® Decision Trees creates classification and decision trees to Highlights help you better identify groups, discover relationships between groups and predict future events. • Identify groups, segments, and patterns in a highly visual manner with classification trees. You can use classification and decision trees for: • Choose from CHAID, Exhaustive • Segmentation CHAID, C&RT and QUEST to find the • Stratification best fit for your data. • Prediction • Present results in an intuitive manner— • Data reduction and variable screening perfect for non-technical audiences. • Interaction identification • Save information from trees as new • Category merging variables in data (information such as • Discretizing continuous variables terminal node number, predicted value and predicted probabilities). Highly visual diagrams enable you to present categorical results in an intuitive manner—so you can more clearly explain the results to non-technical audiences. These trees enable you to explore your results and visually determine how your model flows. Visual results can help you find specific subgroups and relationships that you might not uncover using more traditional statistics. Because classification trees break the data down into branches and nodes, you can easily see where a group splits and terminates. IBM Software IBM SPSS Decision Trees Business Analytics Use SPSS Decision Trees in a variety of applications, • Marketing -
Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth — Pocs Poised to Enter Full-Blown Production
Market Share Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth — POCs Poised to Enter Full-Blown Production Ritu Jyoti Peter Rutten Natalya Yezhkova Ali Zaidi THIS IDC MARKET SHARE EXCERPT FEATURES IBM IDC MARKET SHARE FIGURE FIGURE 1 Worldwide Artificial Intelligence 2018 Share Snapshot Note: 2018 Share (%), Revenue ($M), and Growth (%) Source: IDC, 2019 July 2019, IDC #US45334719e IN THIS EXCERPT The content for this excerpt was taken directly from IDC Market Share: Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth — POCs Poised to Enter Full-Blown Production (Doc # US45334719). All or parts of the following sections are included in this excerpt: Executive Summary, Market Share, Who Shaped the Year, Market Context, Appendix and Learn More. Also included is Figure 1, Table 1 and 2. EXECUTIVE SUMMARY The artificial intelligence (AI) market experienced steady growth in 2018, growing 35.6% to $28.1 billion. As per IDC's Artificial Intelligence Global Adoption Trends and Strategies Survey of 2,473 organizations of various sizes across industries worldwide by those that are using artificial intelligence (AI) solutions, either developing them in-house, using COTS, or a combination of both: 18% had AI models in production, 16% were in the proof-of-concept (POC) stage, and 15% were experimenting with AI. While automation, business agility, and customer satisfaction are the primary drivers for AI initiatives, cost of the solution, lack of skilled personnel, and bias in data have held organizations from implementing AI broadly. In the past 12 months, organizations worldwide have used AI in IT operations, customer service and support, finance and accounting, and ecommerce with major redesign to their business processes to maximize the ROI of AI. -
CIFS SMB2 SMB3 Meets Linux a Year in Review
The Future of File Protocols: CIFS SMB2 SMB3 Meets Linux A Year in Review Steve French Senior Engineer – File System Architect IBM Linux Technology Center 1 IBM, Linux, and Building a Smarter Planet © 2012 IBM Corporation Legal Statement – This work represents the views of the author(s) and does not necessarily reflect the views of IBM Corporation – A full list of U.S. trademarks owned by IBM may be found at http://www.ibm.com/legal/copytrade.shtml. – Linux is a registered trademark of Linus Torvalds. – Other company, product, and service names may be trademarks or service marks of others. 2 © 2012 IBM Corporation Who am I? – Steve French ([email protected] or [email protected]) – Author and maintainer of Linux cifs vfs (for accessing Samba, Windows and various SMB/CIFS based NAS appliances) – Wrote initial SMB2 kernel client prototype – Member of the Samba team, coauthor of SNIA CIFS Technical Reference and former SNIA CIFS Working Group chair – Architect: File Systems/NFS/Samba for IBM Linux Technology Center © 2012 IBM Corporation SMB3: Great Feature Set, Broad Deployment, Amazing Performance ● Introduction of new storage features in Windows 8 causing one of most dramatic shifts in storage industry as companies rapidly move to support “SMB3” (aka SMB2.2) ● “SMB2.2 (CIFS) screams over InfiniBand” (Storage CH Blog) • Is (traditional) SAN use going to die? – “Market trends show virtualization workloads moving to NAS” (Dennis Chapman, Technical Director NetApp, SNIA SDC 2011) – “Unstructured data (file-based) storage is growing faster than structured data” (Thomas Pfenning, Microsoft GM, SNIA SDC 2011) – Customers prefer “file” • SMB2/CIFS market share is MUCH larger than NFS.