Content provided by Byung-Hak Kim, the first author of the paper Deep Claim: Payer Response Prediction from Claims Data with Deep Learning. Peer-review research has been the cornerstone of advancing the practice of medicine, it's time to apply this same scientific rigor to improving the back office of healthcare. Alpha Health is proud to have our research featured at ICML2020. The paper outlines a predictive model we've developed that has the potential to help significantly reduce wasteful healthcare spending. What's New: The paper describes one of the company's machine learning models believed to be the first published deep learning-based system that successfully predicts how a claim will be paid in advance of submission to a payer.
Hypatos, a Germany and Poland based automation startup, raised around $11.8 million in a seed funding round. Many companies like Blackfin Tech, UVC Partners, Grazia Equity, and Plug Ventures participated in the round. The company will use these funds to widen its scope of documents for automated document processing. Hypatos will also focus on further expansion of the company across Europe, North America, and Asia. Hypatos started due to the need for an AI solution for an accounting startup called Smacc to apply deep learning algorithms to automate a more comprehensive range of back-office operations. The primary focus was on the financial and insurance sectors with heavy financial document processing needs.
Amazon unveiled its first wearable on Thursday and it's not what you think. Instead of a cheap smartwatch with Alexa, the Amazon Halo is a minimal wearable with no screen that's designed to keep your body and your relationships healthy. While the Halo connects to your smartphone, you won't be using it to listen to music or get directions. It doesn't have GPS or Wi-Fi or NFC. It has 50-meter water resistance so you can wear it while swimming and 7-day battery life so it can monitor your sleep at night.
As employees return to work after the COVID-19 crisis has subsided, insurers and employers will likely experience a surge in claims related to the virus. Analysts expect coverages for workers compensation, employer liability, and business interruption to be especially hard hit.[i] The California Workers Compensation Insurance Rating Bureau estimates annual losses in its state will be $1.2 billion.[ii] Extrapolating nationally, losses would be approximately $5 billion. Most states have enacted legislation or executive orders to designate critical occupations in the wake of the virus.
Artificial intelligence (AI) is one of the signature issues of our time, but also one of the most easily misinterpreted. The prominent computer scientist Andrew Ng's slogan "AI is the new electricity"2 signals that AI is likely to be an economic blockbuster--a general-purpose technology3 with the potential to reshape business and societal landscapes alike. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years.4 Such provocative statements naturally prompt the question: How will AI technologies change the role of humans in the workplaces of the future? An implicit assumption shaping many discussions of this topic might be called the "substitution" view: namely, that AI and other technologies will perform a continually expanding set of tasks better and more cheaply than humans, while humans will remain employed to perform those tasks at which machines ...
A new whitepaper coauthored by researchers at the Vector Institute for Artificial Intelligence examines the ethics of AI in surgery, making the case that surgery and AI carry similar expectations but diverge with respect to ethical understanding. Surgeons are faced with moral and ethical dilemmas as a matter of course, the paper points out, whereas ethical frameworks in AI have arguably only begun to take shape. In surgery, AI applications are largely confined to machines performing tasks controlled entirely by surgeons. AI might also be used in a clinical decision support system, and in these circumstances, the burden of responsibility falls on the human designers of the machine or AI system, the coauthors argue. Privacy is a foremost ethical concern. AI learns to make predictions from large data sets -- specifically patient data, in the case of surgical systems -- and it's often described as being at odds with privacy-preserving practices.
Despite the recession, organisations have been hiring job roles for data science and analysts. In one of our previous articles, we discussed some of the ways that a data science enthusiast needs to do in order to get hired during the pandemic. In this article, we list down the 6 latest job openings for data scientists and analysts one must apply now. About: Being a Data Scientist at UnitedHealth Group, you will be working in teams addressing statistical, machine learning and data understanding problems. You will be contributing to the development as well as the deployment of machine learning models, operational research, semantic analysis, statistical methods, among others for finding structure in large data sets.
Brokerages who use artificial intelligence could find opportunities to upsell based on changes in a client's lifestyle, according to a software vendor executive. The more data you feed a machine learning model and the more you train it, the better it gets, said Kevin Deveau, managing director of FICO Canada, part of San Jose, Calif.-based Fair Isaac Corp., in a recent interview. Artificial intelligence (AI) is when technology mimics human cognition such as learning from experience, identifying patterns and deriving insights, said Mark Breading, a partner with Boston-based Strategy Meets Action. Machine learning is a type of AI in which computers act without being explicitly programmed, SAS Institute Inc. notes. Bigger brokerages with enough money to invest in AI and machine learning could use those technologies to build a "360-degree view" of a customer, said Deveau, in the context of how the COVID-19 pandemic is forcing companies to change the way they operate.
Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on artificial intelligence. As there are many startups working on various different applications, we want to share our insights with you. Here, we take a look at 5 promising genetic algorithm startups. For our 5 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 111 relevant solutions.
Mike Tyson famously said that "Everyone has a plan until they get punched in the mouth". Every company had a strategic plan coming into 2020. Then, Covid-19 walked into the ring. Insurance has been hit hard by Covid-19 and economic hardship. With many insurers focused on cash conservation, leading insurers can emerge from the crisis even stronger if they make smart investments in AI. Insurers' massive customer datasets and their famously manual processes create some'quick win' AI opportunities.