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Reimagining the EHR Data on Mobile Apps With the Power of AI

Electronic Health Records (EHRs) are meant to ease the everyday workings of physicians, clinicians, and administrative staff in the healthcare sector. However, the reality is quite different. The usability has rarely lived up to its expectation, and healthcare employees seem to be spending a considerable amount of time on managing this data to make it meaningful. This has resulted in a prevailing disappointment over EHR documentation and patient interaction.

We notice that some of the biggest software developers in the market have identified the frustrations linked to EHR and are working on innovative solutions to close the gap between what EHR is perceived to achieve and the current reality. Artificial Intelligence (AI) is a tool that can turn basic EHR into "Smart EHR" through Machine Learning (ML) and voice recognition technologies. From improvising patient visits to the clinic or other healthcare organizations to accurate and availability of care, the possibilities are endless.

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Cloud Security Is More Important Than Ever

Look at any list of 2019 predictions for IT and you’ll likely see expected advances in blockchain, IoT, AI, and edge computing and machine learning (ML), among many others.

What you don’t often see on the list is an issue that affects the very backbone of all these technologies: cloud security.

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UE Application Initiation and Offloading on MEC Deployments in a Standalone 5G Network

5G is a disruptive technology mandatorily needed to meet the capacity and performance requirements of future networks. Massive bandwidth needs and extremely low latency requirements, needed by burgeoning applications (like AI, IoT, AR/VR), require 5G to be facilitated by other emerging technologies like SDN/NFV and multi-access edge computing (MEC). By bringing the computing closer to the user, MEC promises to meet the desired latency and bandwidth constraints. Standardization bodies, like 3GPP (for 5G) and ETSI (for MEC), have been working towards streamlining the procedures for interworking of 5G core and MEC systems. The 5G and MEC specifications give an insight into the future integration strategy expected – making MEC work as a 5G application function to interact with the 3GPP 5G system for traffic steering and reception of mobility events. But a complete flow of information between MEC function entities and the 5G core network functions on application initiation and UE mobility seems to be missing at this point of time. This paper intends to dig into some of these interworking issues and explains the interactions between the participating entities during the complete application lifecycle.

Keywords — MEC (Multi-access edge computing), 5G (5th generation), UE application offloading, 5G application functions

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Study of Medical AI Boasts Impressive Accuracy, But Doesn’t Tell the Full Story

A new study published recently in Nature Medicine and covered in Quartz suggests that AI systems may be able to someday take the diagnostic reins from physicians, at least when it comes to the diagnosis of common childhood diseases. The study’s deep-learning system was so successful, in fact, that it outperformed some doctors in correctly identifying a range of conditions. The study, however, (though promising) is not without its limitations.

As anyone familiar with how these models work will tell you, these systems are ultimately only as good as the data upon which they’re trained; and in this instance, the data came entirely from one medical center in China. Sure, it was able to successfully find diagnostic patterns when subsequently put to the test among this very specific community, but can we really assume it would be just as successful in, say, Manhattan (NY, not Kansas), having had no training on this vastly different population? There are certainly models out there – like this one I recently wrote about – that perform quite well in zero-shot environments, but the amount and variety of the data required to make this happen is staggering.

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AIOps Solution Open to Third Parties for Autonomous Cloud Management

Great speaking with Brend Greifeneder, CTO at Dynatrace, during Perform 2019 where he announced the next generation of their Artificial Intelligence engine, Davis, which is now powered by new and enhanced algorithms and an ability to ingest data and events from a third-party.

“Four years ago, we pioneered, and continually improve, a unique, deterministic approach to AI that enabled customers to simplify enterprise cloud environments and focus more time on innovation. Because Dynatrace auto-discovers and maps dependencies across the enterprise cloud and analyzes all transactions, our Davis AI engine can truly causate, and drive to the precise root cause of issues versus simple guesses based on the correlation. This concept just got even better through semantically enriching external data and mapping it to our real-time topological models. In addition, unlike other solutions, it doesn't require learning periods, making it effective for highly dynamic clouds,” explained Bernd.

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How Will AI Impact Your Business in 2019?

2019 is going to be the year of AI, with many businesses switching to AI-powered security suites, document processing, data analysis, and more. At its core, AI is about automation and augmentation: it improves upon and speeds up many of the core processes of a business. Whether your business uses AI or not, it's going to be impacted by artificial intelligence in 2019. It's important to know how.

Your Competition Is Already Using AI

As of early January, 61 percent of all businesses had already started implementing AI-based technologies. Even if you aren't using AI to streamline your operations and reduce your costs, your competition is. Your competition could be using artificial intelligence to improve their selling techniques, reduce the cost of their quotes, or simply make life easier for their employees. And that means that if you don't have AI, you're likely falling behind.

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Medical AI Systems Struggle to Perform Well Across IT Systems

The level of expectation surrounding AI in healthcare has reached fever pitch in recent years, with a number of pilot projects achieving positive early results. Most of these projects involved AI systems being trained on a sample dataset of medical data, such as x-rays or other medical imagery, after which the system was capable of providing early detection of various conditions.

The challenge for many of these systems is that they were usually trained on data from a single healthcare provider, with a common health IT system. A recent study highlights how when faced with data from different health systems, such AI technologies often perform much worse than doctors.

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Cloud Robotics: Part 2 of the Robot Development Platforms Series

If you’re a developer interested in robots, you may have heard the news — we’re experiencing a “moment” in cloud robotics services. In under four months (late September 2018 to early January 2019), four technology titans stepped forward to stake major claims in this space. This. Is. Not. An. Accident.

Cloud-based services — from AI to computer vision systems to fleet management—have the potential to make owning, managing, and coding robots much more efficient. Cloud robotics services may be an important step forward on the path to increased robot affordability and ease of development,especially for medium-sized businesses.

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Five Predictions for The Next Decade of Software Delivery

Throughout this series of articles, I have been exploring the state of the practice in DevOps, summarizing recent trends in scaling software delivery. In this post – originally written for a special issue of IEEE Software to celebrate software engineering’s 50th anniversary – I look further ahead to consider how software engineering will evolve over the coming decades. My five predictions stretch far enough into the future that they aren’t intended to be precise; they aim to provide discussion topics for the shape of software engineering trends to come.

These predictions resulted from a celebration of the 50th anniversary of the University of British Columbia (UBC) Computer Science Department in May 2018. I participated in a panel of UBC alumni that discussed topics ranging from AI’s future impact to where computer science students should focus today’s studies to have the best job prospects.

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JVM Advent Calendar: Apache Zeppelin: Stairway to *Notes* Heaven!

Introduction

Continuing from the previous post, Two Years in the Life of AI, ML, DL, and Java, where I expressed my motivation, I mentioned our discussions, one of the discussions was that you can write in languages like Python, R, and Julia in JuPyteR notebooks. Most were not aware that you can also write Java and Scala in addition to Python, SQL, etc. with the help of Apache Zeppelin notebooks. And so, I wanted to share something to broaden everyone’s awareness of Apache Zeppelin and its features. The project itself is written in Java and is an open architecture, which means that Zeppelin can support anything as long as an interpreter for that thing has been provided.

First Things First

In case I have lost some of you, here’s what I meant by JuPyteR notebooks and writing notebooks in different languages. Also, have a look at the list of kernels supported by JuPyteR notebook. In this post, however, we are covering Apache Zeppelin, how to get it to work, and how to use a couple of notes in the Zeppelin environment.