In this article, we discuss the design and implementation of a novel DCN system, which utilizes a knowledge-defined NO-M to operate a HOEDCN cost-effectively and energy-efficiently. The motivations behind the proposed HOE-DCN system are the urgent need to address the scalability, energy, and manageability issues in existing DCN systems. To realize the knowledge-defined NO-M, we follow the principle of predictive analytics in the human brain to design three artificial intelligence modules based on deep learning and make them operate collaboratively. The proposed HOE-DCN system is implemented in a network testbed, and we conduct experiments that involve both control and data plane operations to demonstrate its advantages. The experimental results show that the HOE-DCN simultaneously achieves high-performance service provisioning and improved energy efficiency. Furthermore, by analyzing the pros and cons of the HOE-DCN system, we also point out several directions to work on in the future.

Machine learning (ML) is a powerful technique to tackle many problems in data mining and predictive analytics. We believe that ML will be of considerable potentials in the field of bioinformatics since the high-throughput technology is producing ever increasing biological data. In this review, we summarized major ML algorithms and conditions that must be paid attention to when applying these algorithms to genomic problems in details and we provided a list of examples from different perspectives and data analysis challenges at present.
Keywords: Machine learning | Bioinformatics | Gene | Genomics

Motivation and Objectives: Smartphones are emerging as personal fitness assistants, collecting data through in-built or external sensors. The next frontier for these devices is to use advanced sensors and machine learning algorithms to offer more personalized and advanced medical assessments. Along these lines, the objective of this paper is to develop a multi-label classification model to detect heart compli- cations through electrocardiograms (EKGs) collected by an FDA-approved single-lead EKG sensor attached to a smartphone. The EKG sensor produces a standard EKG chart, but for such a sensor to be useful to a consumer, an interpretation of the graph is necessary. Materials and Method: We adapt a machine-learning approach to detect multiple heart conditions simul- taneously from the generated EKG graph. Three different multi-label machine learning models (binary relevance, label powerset and multi-perceptron neural network) were built and compared to categorize five different heart states: Normal, Atrial Fibrillation, Atrioventricular Block, Sinus Bradycardia and Sinus Tachycardia. The binary relevance model was selected based on the accuracy. Results and Implications: The model generated rules inductively from the data to interpret nine out of every ten heart conditions correctly. Our model is being adapted for commercial use by a company (as a part of their App) that markets the EKG sensor for smartphones. Our model is usable in a cardiovascular disease alert expert system that will potentially allow users to monitor their heart health continuously and prevent a serious illness by providing this information in the early stages.
Keywords: Multi-label classification | Machine learning | EKG sensor | Smartphone app | Predictive analytics

Objectives: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS. Methods: We selected patients meeting the following criteria in 2 large US healthcare claims databases (Truven Health MarketScan Commercial [CCAE]; Optum Clinformatics [Optum]): underwent MxS between January 1, 2007, to October 1, 2013 (first = index); aged $18 years; continuous enrollment 180 days pre-index (baseline) to 730 days postindex; baseline T2D diagnosis and treatment. The outcome was no antihyperglycemic medication treatment from 365 to 730 days after MxS. A regularized logistic regression model was trained using the following candidate predictor categories measured at baseline: demographics, conditions, medications, measurements, and procedures. A 75% to 25% split of the CCAE group was used for model training and testing; the Optum group was used for external validation. Results: 13 050 (CCAE) and 3477 (Optum) patients met the study inclusion criteria. Antihyperglycemic medication cessation rates were 72.9% (CCAE) and 70.8% (Optum). The model possessed good internal discriminative accuracy (area under the curve [AUC] = 0.778 [95% CI = 0.761-0.795] in CCAE test set N = 3527) and transportability (external AUC = 0.759 [95% CI = 0.741-0.777] in Optum N = 3477). Conclusion: The application of machine learning techniques to real-world healthcare data can yield useful predictive models to assist patient selection. In future practice, establishment of prerequisite technological infrastructure will be needed to implement such models for real-world decision support.
Keywords: prediction | machine learning | type 2 diabetes | metabolic surgery | antihyperglycemic medication

Background: The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. Purpose: This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. Data sources: A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. Study selection: Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. Data extraction: Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. Results: A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. Conclusion: This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.
Keywords: Big data , Analytics , Healthcare , Predictive analytics , Evidence-based medicine