Abstract
This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare. BD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations. Diagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter. BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.
Highlights
Big Data (BD) and Artificial Intelligence (AI) promise huge opportunities but raise huge issues
Outlook: BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data
This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources
Summary
This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources. Structured data is the type of data that fits neatly within fixed fields and columns in relational databases and spreadsheets. Relational databases can input, search, and manipulate structured data relatively quickly. The programming language used for managing structured data is called structured query language (SQL). This language was developed by IBM in the early 1970s and is useful for handling relationships in databases [3]. While structured data gives us a birds-eye view of processes in place and outcomes, unstructured data can give us a much deeper understanding of behaviours, associations, trends and intent. Examples of unstructured data include text files, medical video data from medical imaging devices An astonishing 80% of all data generated today is considered unstructured and this number will continue to rise as new internet-connected devices come online [4]
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