XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Facebook recently deployed Facebook Messages, its first ever user-facing application built on the Apache Hadoop platform. Apache HBase is a database-like layer built on Hadoop designed to support billions of messages per day. This paper describes the reasons why Facebook chose Hadoop and HBase over other systems such as Apache Cassandra and Voldemort and discusses the applicationBs requirements for consistency, availability, partition tolerance, data model and scalability. I explore the enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the system, and how this solution has significant advantages over the sharded MySQL database scheme used in other applications at Facebook and many other web-scalecompanies. I discuss the motivations behind my design choices, the challenges that we face in day-to-day operations, and future capabilities and improvements still under development.I offer these observations on the deployment as a model for other companies who are contemplating a Hadoop-based solution over traditional sharded RDBMS deployments.

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Apache Hadoop Goes Realtime at Facebook

Serotonin is an ancient molecular signal and a recognized neurotransmitter brainwide distributed with particular presence in hippocampus. Almost all serotonin receptor subtypes are expressed in hippocampus, which implicates an intricate modulating system, considering that they can be localized as autosynaptic, presynaptic, and postsynaptic receptors, even colocalized within the same cell and being target of homo- and heterodimerization. Neurons and glia, including immune cells, integrate a functional network that uses several serotonin receptors to regulate their roles in this particular part of the limbic system.

/pdf/serotonin-receptors-in-hippocampus-39vgyxb9y3.pdf

Serotonin Receptors in Hippocampus

Performance evaluation of cloud-based log file analysis with Apache Hadoop and Apache Spark

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

/pdf/cnn-based-multiclass-brain-tumor-detection-using-medical-25uqmehb.pdf

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Pervasive computing has the potential to offer low-cost, high performance, and user centric solutions to exchange the information and communicate seamlessly in highly dynamic, heterogeneous environment. Here small and influential dissimilar devices or nodes have to set up independent network unknown by the user. Communicating devices are resource-restricted and equipped with micro or bio-sensors to acknowledge the signals where traditional security systems based on cryptography and encryption are not enough for promising level of security assurance. In this paper we explore trust and security challenges and appraise the opportunities in autonomous mobile pervasive ad-hoc networks to improve security. Trust management models in human-centric applications can enhance the security assurance. Many researchers proposed various trust models for different scenarios. Inspiring from such models we propose a trust computation metric based on node’s impulsive behavior to become malicious node in dynamic scenario and breach the security. In winding up, we put our efforts to present energy efficient, secure and trusted clustering to enhance the security assurance and significant adaptation of trustworthy communication in user-centric m-healthcare applications where information is ubiquitous.

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Trusted and secure clustering in mobile pervasive environment

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human’s life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart’s electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT–BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.

/pdf/a-hybrid-deep-learning-approach-for-ecg-based-arrhythmia-21vjyouk.pdf

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification

As we know World Wide Web plays vital role in serving the needs of the user's on web. The web log files are generated as a result of an interaction between the client and the service provider on web. Web log file contains the massive hidden valuable information pertaining to the visitors, if mined can be used for predicting the navigation behavior of the users. However the task of discovering frequent sequence patterns from the web log is challenging. Sequential pattern mining provides a significant role in serving a promising approach of the access behavior of the user. This paper focuses on adopting an intelligent technique that can provide personalized web service for accessing related web pages more efficiently and effectively, so that it can be determined which web pages are more likely to be accessed by the user in future. This paper uses two intelligent algorithms for predicting the user behavior's namely Apriori and Eclat and also does the performance comparison of the two algorithms in terms of time and space complexity for the filtered data.

http://ieeexplore.ieee.org/iel7/6472814/6481100/06481192.pdf

User behavior analysis in web log through comparative study of Eclat and Apriori

The unremitting increase of computational strength has produced tremendous flow of data in the past two decades. This tremendous flow of data is known as “big data”. Big data is the data which cannot be processed with the aid of existing tools or techniques and if processed can result in interesting information's such as analysing the behaviour of the user, business intelligence etc. This paper discusses the difference between the traditional relational database and big data; it also shows the characteristics of big data. The paper also focuses on the distinct big data channels processes along with the various challenges and as well as on how big data is a solution to the organizations. Big data does not only focus to store and handle the large volume of data but also to analysed and extract the correct information from the data in lesser time span. At last it discusses about hadoop an open source framework that allows the distributed processing for massive datasets on cluster of computers which is shown with using the log file for extraction of information based on user query.

Bhaskar Pant

Papers

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Trusted and secure clustering in mobile pervasive environment

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification

User behavior analysis in web log through comparative study of Eclat and Apriori

Big data: Mining of log file through hadoop