Medical ML/DL system shall facilitate a deep understanding of the underlying healthcare task, which (in most cases) can only be achieved by utilising other forms of patients data. For example, radiology is not all about clinical imaging. Other patient EMR data is crucial for radiologists to derive the precise conclusion for an imaging study. This calls for the integration and data exchange between all healthcare systems. Despite extensive research on data exchange standards for healthcare, there is a huge ignorance in following those standards in healthcare IT systems which broadly affects the quality and efficacy of healthcare data, accumulated through these systems.
UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!
There's an old adage, "good help is hard to get," that is making something of a comeback in today's increasingly dynamic and competitive global human resource industry. In 2016, the HR industry's total operating income reached €491 billion, while 2007-2016 CAGR was about 9 percent. Flexible labour accounts for about 71 percent of modern market share, 20 percent comes from management service providers, 8 percent from high-end talent search, and 1 percent from recruitment process outsourcing and specialized services. Recruitment and staffing are challenging areas that have been getting the most market investments. There are numerous derived services and platforms catering to recruiting: headhunters for high-end talents, background investigation services, consulting firms, and of course the popular online recruitment platforms such as LinkedIn and Glassdoor.
You can call yourself a guru of retail pricing if you can make the right pricing decisions for every one of your products, separately and combined, based on their demand elasticity at any given moment. Each of your pricing decisions has to help you reach all of your current business goals and ensure the best shopping experience at the same time. In other words: to find a balance between your profits and traffic. Let's take a step back to where everything begins – your business strategy. The company's strategy is supposed to be converted into its pricing strategy and subsequently into pricing tactics.
The advent of large data sets from many sources (big data), machine learning, and artificial intelligence (AI) are poised to revolutionize asthma care on both the investigative and clinical levels, according to an article published in the Journal of Allergy and Clinical Immunology. During 15-minute clinic visits, only a short amount of time is spent understanding and treating what is a complex disease, and only a fraction of the necessary data is captured in the electronic health record. "Our patients and the pace of data growth are compelling us to incorporate insights from Big Data to inform care," the researchers posit. "Predictive analytics, using machine learning and artificial intelligence has revolutionized many industries," including the healthcare industry. When used effectively, big data, in conjunction with electronic health record data, can transform the patient's healthcare experience.
If you listen to university advertisements for data science masters degrees, you'd believe that data scientists are so in-demand that they can walk into any company, state their salary, and start work straight away. Interviewing for data science positions is tough, and job-seekers face some bad behaviour from recruiters and hiring managers. Many companies understand that they need to do something with data, but they don't know what. They'll say they want machine learning when they really want a few dashboards. I'm going to put some advice here for anyone about to face the same job market.
The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data. Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. This boom of geospatial big data combined with advancements in machine learning is enabling organizations across industry to build new products and capabilities. Maps leveraging geospatial data are used widely across industry, spanning multiple use cases, including disaster recovery, defense and intel, infrastructure and health services. For example, numerous companies provide localized drone-based services such as mapping and site inspection (reference Developing for the Intelligent Cloud and Intelligent Edge).
KNIME, a unified software platform for creating and productionizing data science, announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials. Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers.
In the digital era of innovative products and services, Insurtech technologies are bringing great opportunities to the insurance sector and accelerating the industry's transformation. Advances in AI and data science are leading insurers toward the effective use of machine learning, data modeling and predictive analytics to improve back-end processes and streamlining and automation of the front-end experience for both consumers and insurance companies. Customers are acquiring insurance policies much faster and easier with the help of automated processes. These technologies differ depending on the systems that employ them and the people they serve. Integration gateways relying on data and AI are creating new customer experiences.
MADS East 2019 was a two-day conference in December that gave attendees endless opportunities to expose themselves to new ideas in the space of data science for marketing. Some of this year's conference perks included: tables for one-on-one networking, a half-an-hour off the record roundtable with 7 industry leaders, two unique tracks per day, buffet-style lunches, breakfasts, snacks, a refreshing break for cocktails at the Opening Night Party, and NYC Times Square views. This article is my summary of the Day 1 presentations I was able to attend, including lessons and reminders from the speakers. Aside from staying up to date on industry trends, MADS East has also proven itself a valuable opportunity for data and marketing people who are looking to engage with professionals of varying career levels. I was expecting to be the only individual with little background in data or extended industry experience present, but to my surprise, there was a decent balance between early, mid and late-career attendees.