Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page

Towards information profiling: data lake content metadata management

There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft

A fast algorithm for detecting gene-gene interactions in genome-wide association studies

With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches. Although many such approaches have been developed and used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few are able to detect gene-gene interactions among different SNPs. Genetic interactions, also known as epistasis, have been recognized to play a pivotal role in contributing to the genetic variation of phenotypic traits. However, because of an extremely large number of SNP-SNP combinations in GWAS, the model dimensionality can quickly become so overwhelming that no prevailing variable selection methods are capable of handling this problem. In this paper, we present a statistical framework for characterizing main genetic effects and epistatic interactions in a GWAS study. Specifically, we first propose a two-stage sure independence screening (TS-SIS) procedure and generate a pool of candidate SNPs and interactions, which serve as predictors to explain and predict the phenotypes of a complex trait. We also propose a rates adjusted thresholding estimation (RATE) approach to determine the size of the reduced model selected by an independence screening. Regularization regression methods, such as LASSO or SCAD, are then applied to further identify important genetic effects. Simulation studies show that the TS-SIS procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as gene-gene interactions. We apply the proposed framework to analyze an ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select 23 active SNPs and 24 active epistatic interactions for the body mass index variation. It shows the capability of our procedure to resolve the complexity of genetic control.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS771 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A user profiling component with the aid of user ontologies

Abstract: What follows is a contribution to the field of user modeling for adaptive teaching and learning programs especially in the medical field. The paper outlines existing approaches to the problem of extracting user information in a form that can be exploited by adaptive software. We focus initially on the so-called stereotyping method, which allocates users into classes adaptively, reflecting characteristics such as physical data, social background, and computer experience. The user classifications of the stereotyping method are however ad hoc and unprincipled, and they can be exploited by the adaptive system only after a large number of trials by various kinds of users. We argue that the remedy is to create a database of user ontologies from which readymade taxonomies can be derived in such a way as to enable associated software to support a variety of different types of users

Tcf7l2 plays pleiotropic roles in the control of glucose homeostasis, pancreas morphology, vascularization and regeneration

Type 2 diabetes (T2D) is a disease characterized by impaired insulin secretion. The Wnt signaling transcription factor Tcf7l2 is to date the T2D-associated gene with the largest effect on disease susceptibility. However, the mechanisms by which TCF7L2 variants affect insulin release from \u3b2-cells are not yet fully understood. By taking advantage of a tcf7l2 zebrafish mutant line, we first show that these animals are characterized by hyperglycemia and impaired islet development. Moreover, we demonstrate that the zebrafish tcf7l2 gene is highly expressed in the exocrine pancreas, suggesting potential bystander effects on \u3b2-cell growth, differentiation and regeneration. Finally, we describe a peculiar vascular phenotype in tcf7l2 mutant larvae, characterized by significant reduction in the average number and diameter of pancreatic islet capillaries. Overall, the zebrafish Tcf7l2 mutant, characterized by hyperglycemia, pancreatic and vascular defects, and reduced regeneration proves to be a suitable model to study the mechanism of action and the pleiotropic effects of Tcf7l2, the most relevant T2D GWAS hit in human populations

Key performance indicators for the National Bowel Cancer Screening Program: technical report

Provides a summary of the development process and the technical specification for the 11 agreed performance indicators that are part of the National Bowel Cancer Screening Program Performance Indicator Set. Summary Cancer contributes significantly to the burden of illness in the Australian community. Bowel cancer is one of the most significant cancer types in terms of incidence and mortality. In 2010, 14,860 people were diagnosed with bowel cancer and in 2011 there were 3,999 deaths from the disease. Screening for bowel cancer is available in Australia through the National Bowel Cancer Screening Program (NBCSP), which aims to reduce the incidence, illness and mortality related to bowel cancer through screening to detect cancers and pre-cancerous lesions in their early stages, when treatment is most successful. Reporting statistics about the NBCSP in a standardised way is vital to ensure that governments, researchers and health workers have access to relevant and reliable statistics about the performance of the program over time. This report describes the National Bowel Cancer Screening Program Performance Indicator Set (NBCSP PIs) and is a reference tool for anyone who wishes to understand, measure and report the progress of bowel cancer screening in Australia. The indicators were developed by the National Bowel Cancer Screening Program Report and Indicator Working Group (the working group) and have been endorsed by the Standing Committee on Screening, the Community Care and Population Health Principal Committee, the National Health Information Standards and Statistics Committee and the National Health Information and Performance Principal Committee. The indicators are consistent with the five Australian Population Based Screening Framework (PBSF) steps of recruitment, screening, assessment, diagnosis and outcomes