2020/04/05 by Arshia Rehman, Rehman, Arshia, Saeeda Naz +3 · 1 citation
Health Professions · #Artificial Intelligence in Healthcare #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Other Statistics (stat.OT)
paper · pdf · doi:10.48550/arxiv.2004.09010
openalex publication_date 2020/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Clinicians decisions are becoming more and more evidence-based meaning in no\nother field the big data analytics so promising as in healthcare. Due to the\nsheer size and availability of healthcare data, big data analytics has\nrevolutionized this industry and promises us a world of opportunities. It\npromises us the power of early detection, prediction, prevention and helps us\nto improve the quality of life. Researchers and clinicians are working to\ninhibit big data from having a positive impact on health in the future.\nDifferent tools and techniques are being used to analyze, process, accumulate,\nassimilate and manage large amount of healthcare data either in structured or\nunstructured form. In this paper, we would like to address the need of big data\nanalytics in healthcare: why and how can it help to improve life?. We present\nthe emerging landscape of big data and analytical techniques in the five\nsub-disciplines of healthcare i.e.medical image analysis and imaging\ninformatics, bioinformatics, clinical informatics, public health informatics\nand medical signal analytics. We presents different architectures, advantages\nand repositories of each discipline that draws an integrated depiction of how\ndistinct healthcare activities are accomplished in the pipeline to facilitate\nindividual patients from multiple perspectives. Finally the paper ends with the\nnotable applications and challenges in adoption of big data analytics in\nhealthcare.\n