This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities ...
The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techn...
Nederlands tijdschrift voor geneeskunde
Feb 28, 2019
In the near future, making a correct medical diagnosis will be increasingly supported by artificial intelligence. The development of algorithms that integrate all data from an individual into the diagnostic process calls for a multidisciplinary appro...
Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet s...
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, ...
Journal of medical imaging and radiation oncology
Feb 5, 2019
INTRODUCTION: Natural language processing (NLP) is an emerging tool which has the ability to automate data extraction from large volumes of unstructured text. One of the main described uses of NLP in radiology is cohort building for epidemiological s...
CPT: pharmacometrics & systems pharmacology
Feb 3, 2019
Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. Th...
One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a sub...
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., gen...
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