Goto

Collaborating Authors

Results


Access and Action: Healthcare Systems Put Big Data to Work

#artificialintelligence

Across all industries, organizations are now managing more data, nearly 14 petabytes on average, according to Dell Technologies' 2020 Global Data Protection Index (1 petabyte is just over 1 million gigabytes). In healthcare, providers and patients want to see more done with all that data. Some 75 percent of healthcare consumers want to work together with providers on wellness goals, according to Deloitte research, and 85 percent of physicians expect interoperability and data sharing to become standardized. The pandemic has highlighted the value of innovative technologies to gather, manage and gain insights from the vast stores of data that hospitals collect, guiding them toward improved care and adaptive clinical workflows. "The pandemic has been a huge validation of the path we were on and the investments we've made in data management," Lamm says.


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


Top 10 Healthcare Industry Trends & Innovations in 2021

#artificialintelligence

Healthcare industry trends that we witness today are new technologies and solutions that address the requirements for clinical diagnosis, treatment, and disease management. The global COVID-19 pandemic led to an upsurge in technologies for disinfecting, limiting transmission, detecting disease spread, as well as for treatment, patient management, and immunization. The advancements in the healthcare industry range from e-consultations, telemedicine, real-time diagnosis to accessing digital therapeutics provided by immersion technology tools. Genetic analysis, clinical data storage, and big data & analytics enable the development of precision medicine. The adoption of artificial intelligence (AI), the internet of things (IoT), and data management practices is making hospitals smarter. These solutions enhance workflows and staff scheduling and provide connected infrastructure, devices, and systems to accelerate accurate and equitable clinical services. For this in-depth research on the Top Healthcare Industry Trends & Startups, we analyzed a sample of 3.622 global startups and scaleups.


Precision Medicine Informatics: Principles, Prospects, and Challenges

arXiv.org Artificial Intelligence

Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.


Top Data Sources for Journalists in 2018 (350 Sources)

@machinelearnbot

There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.