Rule-based Computer Aided Decision Making for Traumatic Brain Injuries

Belle, Ashwin; Ji, Soo Yeon; Huynh, Toan; Najarian, Kayvan

doi:10.1007/978-3-642-40017-9_11

Cited by 2 publications

(1 citation statement)

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“…However, there are opportunities for developing algorithms to address data filtering, interpolation, transformation, feature extraction, feature selection, and so forth. Furthermore, with the notoriety and improvement of machine learning algorithms, there are opportunities in improving and developing robust CDSS for clinical prediction, prescription, and diagnostics [ 180 , 181 ].…”

Section: Discussionmentioning

confidence: 99%

Big Data Analytics in Healthcare

Belle

Raghuram

Soroushmehr

et al. 2015

BioMed Research International

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The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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Section: Discussionmentioning

confidence: 99%