machine learning (ML)

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Accelerating Identification and Tracking of Pandemic Disease Outbreaks

A national biosurveillance program requires the collaboration of multiple federal, state and local agencies to provide a comprehensive view of a health-related event. Bitscopic's Praedico™ biosurveillance platform breaks down the data barriers among organizations with an extensible architecture that can incorporate any kind of data. The platform also delivers high performance by incorporating the latest technologies such as big data, NoSQL databases, and machine learning. Read More »

ASF Announces Singapore's Apache SINGA Deep Learning Tool as a Top-Level Project

Press Release | Apache Software Foundation (ASF) | November 4, 2019

The Apache Software Foundation (ASF)...announced today Apache® SINGA™ as a Top-Level Project (TLP). Apache SINGA is an Open Source distributed, scalable machine learning library. The project was originally developed in 2014 at the National University of Singapore, and was submitted to the Apache Incubator in March 2015. "We are excited that SINGA has graduated from the Apache Incubator," said Wei Wang, Vice President of Apache SINGA and Assistant Professor at the National University of Singapore. "The SINGA project started at the National University of Singapore, in collaboration with Zhejiang University, focusing on scalable distributed deep learning. In addition to scalability, during the incubation process, built multiple versions to improve the project's usability and efficiency. Incubating SINGA at the ASF brought opportunities to collaborate, grew our community, standardize the development process, and more." Read More »

How Can Information and Communications Tech Help in Disaster Preparedness and Response?

Renu Mehta | Devdiscourse | July 15, 2019

n the immediate aftermath of disasters, timely and effective information is critical for the decision-making process. ​​​​​Information and Communication Technologies (ICTs) play a significant role in mitigation, preparedness, response, and rehabilitation by facilitating the flow of vital information in a timely manner. To deliver and deploy telecommunications / information and communication resources (transportable, easy to deploy and reliable systems that are non-exclusive) in a timely manner in the event of disasters, the ITU has designed the ITU Framework for Cooperation in Emergencies (IFCE). Innovative technologies such as robotics, drone technology, GIS, and emerging technologies like artificial intelligence (AI), the Internet of Things (IoT), cloud computing and Big Data are transforming the complex process of disaster management.

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Informatics Education

Informatics Education was created in 2007 as the business entity in support of the first edition of our textbook Health Informatics: Practical Guide for Healthcare and Information Technology Professionals. Newer editions were published every 1-2 years with the seventh edition published in June 2018...Since the inception of Informatics Education, the vision has been to support informatics students and faculty.

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Machine Learning in Healthcare: Part 1 - Learn the Basics

This article is the first in a three-part series that will discuss how machine learning impacts healthcare. The first article will be an overview defining machine learning and explaining how it fits into the larger fields of data science and artificial intelligence. The second article will discuss machine learning tools available to the average healthcare worker. The third article will use a common open source machine learning software application to analyze a healthcare spreadsheet. Part I was written to help healthcare workers understand the fundamentals of machine learning and to make them aware that there are simple and affordable programs available that do not require programming skills or mathematics background...

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Machine Learning in Healthcare: Part 2 - Tools Available to the Average Healthcare Worker

A variety of machine learning tools are now available that can be part of the armamentarium of many industries, to include healthcare. Users can choose from commercial expensive applications such as Microsoft Azure Machine Learning Studio, SAS Artificial Intelligence Solutions or IBM SPSS Modeler. Academic medical centers and universities commonly have licenses for commercial statistical/machine learning packages so this may be their best choice. The purpose of this article is to discuss several free open source programs that should be of interest to anyone trying to learn more about machine learning, without the need to know a programming language or higher math.

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Machine Learning in Healthcare: Part 3 - Time for a Hands-On Test

Every inpatient and outpatient EHR could theoretically be integrated with a machine learning platform to generate predictions, in order to alert clinicians about important events such as sepsis, pulmonary emboli, etc. This approach may become essential when genetic information is also included in the EHR which would mandate more advanced computation. However, using machine learning and artificial intelligence (AI) in every EHR will be a significant undertaking because not only do subject matter experts and data scientists need to create and validate the models, they must be re-tested over time and tested in a variety of patient populations. Models could change over time and might not work well in every healthcare system. Moreover, the predictive performance must be clinically, and not just statistically significant, otherwise, they will be another source of “alert fatigue.”

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The Medical Record of the Future: Part II

The current path of progress of the EHR, with its concentration on “meaningful use,” and an intellectual property regime that does not fully exploit the capacity for innovation by end-users is approaching an evolutionary dead-end. It is time to treat the EHR as what it should be: an integral part of medical care that has limitless potential for maximizing the use of information acquired in the provision of health care, and not an impediment to optimal care and a bugaboo for the physician. Read More »

Why Data Scientists Love Kubernetes

Let's start with an uncontroversial point: Software developers and system operators love Kubernetes as a way to deploy and manage applications in Linux containers. Linux containers provide the foundation for reproducible builds and deployments, but Kubernetes and its ecosystem provide essential features that make containers great for running real applications...What you may not know is that Kubernetes also provides an unbeatable combination of features for working data scientists. The same features that streamline the software development workflow also support a data science workflow! To see why, let's first see what a data scientist's job looks like...

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