8th International Conference on ^{Biostatistics & Bioinformatics} September 18-19, 2019 San Francisco, USA

Session Introduction

Hella Othman AlOthman

completed her MBBS at the age of 22 years from King Abdul-Aziz University School of Medicine

Title: Title: PHYSICIANS KNOW ABOUT BIOSTATISTICS BETTER THAN THEY THINK IN SAUDI ARABIA

Biography:

Hella Othman AlOthman has completed her MBBS at the age of 22 years from King Abdul-Aziz University School of Medicine and continue postgraduate family medicine in  4th year in King Abdul-Aziz Medical City . Intreseted in Biostatistics and informatics researches and teaching.
 

Abstract:

         Biostatistics is the discipline that helps in making decisions when analyzing biomedical data with formulating explicit rules and for biases in designing studies in particular. Physicians were found to have limited knowledge of biostatistics. This study aims to assess the knowledge, familiarity and competency of statistical concepts among physicians in Saudi Arabia. This is a cross sectional study which was conducted in Riyadh, Saudi Arabia. A convenient sample of 440 physicians were identified and contacted through digital survey monkey, WhatsApp groups and twitter. A questionnaire was used to obtain statistical knowledge of the participants. Data was entered and analyzed with SPSS (v24). Frequencies and percentages were calculated for categorical variable. Chi-square was used to show gap difference between attitudes toward knowledge. P < 0.000 was taken as significant.This study found that more than half of participants showed their interest in research and approximately 46% had at least one article published. Around 65% studied formal course on biostatistics in their undergraduate and postgraduate years. 59% were familiar to specificity and sensitivity, 53% to Median, 47% to Null hypothesis, 45% to Variables, 44% to Confident interval. Gap difference between familiarity and knowledge of various biostatical concepts was found statistically significant. Physicians were found to have sufficient information about basic principles of biostatistics which are commonly used in medical research articles. Steps should be taken to educate senior staff and other trainees by conducting practical courses and also indulging medical students in research projects.

 

Session Introduction

Prabhav Vanguri

student at Westwood High School. He has conducted scientific research for the last three years, including research at University of Texas at Austin and at the Southern Methodist University in Dallas.

Title: Fall-iN-TELL: Smart Electronic Framework for Personalized Accurate Fall Detection

Biography:

Prabhav Vanguri is a student at Westwood High School. He has conducted scientific research for the last three years, including research at University of Texas at Austin and at the Southern Methodist University in Dallas. He has won multiple awards for his research at the Texas State Science and Engineering Fair. He is also the recipient of multiple U.S. Airforce wards, and the Intel Excellence in Computer Science Scholarship

Abstract:

Millions of older adults experience falls each year. Early response after a fall is vital because it has been established that the earlier the fall is reported, the lower the morbidity-mortality rate is. Gait stability is an important fall risk indicator for older adults. The goal of this project is to develop a reliable fall detection device that takes individual gait traits into consideration in order to accurately detect fall. In older adults, the risk of falling increases during normal daily activities that involve movement. In this research, movement data analysis of eight daily life activities during which falls likely occur are measured and analyzed. Based on this analysis, a quantifiable gait stability scale was developed to improve fall detection accuracy. A fall detection device was developed using a microcontroller with an inertial measurement unit. This device was programmed to detect falls by recording movement thresholds to distinguish between normal movements and falls. These thresholds are personalized based on the individual gait traits that are closely associated with falls. A warning system was also implemented in the device that is capable of alerting emergency services automatically, in case the user is incapacitated. The accuracy of this fall detection device was tested with the same group of adult volunteers whose gait traits were studied for analysis. Falls were simulated by dropping the prototype during various normal daily life activities. These tests were highly successful in predicting falls and generating alerts to provide timely intervention.

 

Session Introduction

Uchechukwu

Department of Statistics, Michael Okpara University of Agriculture, Umudike. Abia State

Title: Bayesian statistics

Biography:

Prof. Joy Nwabueze is the first female Professor of Statistics in Nigeria, she is currently Deputy Vice Chancellor Administration, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

 

Abstract:

The study proposed a modified stochastic search variable selection method for selecting restriction in VAR models. This is done by eliciting a new class of modified SSVS prior using diffuse prior for the variance covariance which allows for non-diagonal treatment of the variance covariance matrix.  The performance of the modified stochastic variable was evaluated using a Monte Carlo experiment and concludes by assessing the out of sample forecast performance of three macroeconomic series of Nigeria with the modified stochastic variable selection in VAR model. The modified stochastic search variable selection method was compared with existing VAR models in literature using the Mean square Error. The modified stochastic search variable selection method outperforms most of the existing bayesian and non-bayesian VAR models

 

Session Introduction

Petra Perner

Institute of Computer Vision and Applied Computer Sciences IBaI, Leipzig, Germany

Title: Why Quantitative Measurements of Cellur Events and What can Pattern Recognition and Data Mining Do For It?

Biography:

Prof. Petra Perner (IAPR Fellow) is the director of the Institute of Computer Vision and Applied Computer Sciences IBaI. She received her Diploma degree in electrical engineering and her PhD degree in computer science for the work on “Data Reduction Methods for Industrial Robots with Direct Teach-in-Programing”. Her habilitation thesis was about “A Methodology for the Development of Knowledge-Based Image-Interpretation Systems". She has been the principal investigator of various national and international research projects. She received several research awards for her research work and has been awarded with 3 business awards for her work on bringing intelligent image interpretation methods and data mining methods into business. Her research interest is image analysis and interpretation, machine learning, data mining, big data, machine learning, image mining and case-based reasoning.

Abstract:

Cellular assays are highly recommended scientific tools in modern drug research and examination of pathological processes on cellular level. Within the project QuantPro we developed based on innovative cell-based microscopy end-point assays a robust quantitative reference model for better understanding or quantitative measurements of dynamic or time dependent events in three key disease-relevant cellular pathways involving either internalisation (cell invasion or endocytosis of viruses or bacterial pathogens) or internal movements (mitochondria). Furthermore, we developed a suitable complex cellular functional experimental setup for drug screening in oncology which allows for microtiter plate-based screening modalities, multiple wavelength operation and high-resolution imaging. A highly innovative technology component for the application of a newly developed imaging system “Selective Plane Illumination Microscope (SPIM)” to extend the dynamic measurements made by two-dimensional microscopy and confocal scanning sections to 3D microscopy by confocal theta microscopy. Novel bioinformatic and software solutions were develped to evaluate both cellular models in a dynamic 3D mode. The talk summarizes the results of the project QuantPro.

Session Introduction

Sumith Gunasekera

B.Sc.(Sp.) degree in Physics in 1995 from the University of Colombo, Colpetty, Colombo 03, District of Colombo

Title: Estimating the Youden index under the multivariate ROC curve in the presence of missing values of mass diseased- and healthy-biomarker data

Biography:

Sumith Gunasekera received the (Special Bachelor of Science) B.Sc.(Sp.) degree in Physics in 1995 from the University of Colombo, Colpetty, Colombo 03, District of Colombo, Western province, Democratic Socialist Republic of Sri Lanka (DSRSL) (formerly known as Ceilão (in Portuguese under their rule), Seylon (by Dutch under their rule), and Ceylon (by British under their rule), and the (Doctor of Philosophy) Ph.D. degree in Statistics in 2009 from the University of Nevada at Las Vegas (UNLV), Las Vegas, NV, United States of America (USA). Sumith joined the Department of Mathematics at The University of Tennessee at Chattanooga, Chattanooga TN, USA in 2009, and has been an Associate Professor of Statistics since 2015. He is the author of many seminal statistical articles and is the recipient of several grants and awards. His research interests include statistical inference, reliability, survival analysis, design of experiments under classical, Bayesian, and generalized frameworks.

Abstract:

Abstract—In the present day scenario, classification tools have gained much attention of researchers in solving real life classification problems which arise in various disciplines. Basing on the prominence and demand to handle such problems, those variety of statistical tools have emerged and were brought under the hub of Statistical Decision Theory (SDT). The common problem of interest in classification is in allocating an individual or object to one of the predefined groups (or populations) by using a threshold. These problems were addressed by using a performance tool namely, Receiver Operating Characteristic (ROC) Curve. The Youden index (J) is a frequently used summary measure of the ROC curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of optimal cut-point (c ?) for the biomarkers. The proposed research demonstrates how J for the diseased and healthy subjects can be extended to multi-biomarkers in the higher-dimensional space by analytic and extensive-computational continuation of the mass missing data of multi-biomarkers from the breast cancer patients available at http://archive.ics.uci.edu/ml/datasets/ mammographic+mass, and by intensive and extensive simulations and computations of big data with the aid of generalized variable method. This computational-extensive mass-data-based procedure is novel and reduce the high number of unnecessary biopsies by helping physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. This goal is accomplished by the comparison of classical and generalized variable procedures for J and c ? for the multi-biomarkers with missing data, where missing data are cleaned or tackled by imputations. These are juxtaposed using confidence intervals, pvalues, power of the test, size of the test, and coverage probability with a wide-ranging simulation study featuring a selection of various scenarios. Index Terms—Generalized variable method, High-performing computations, Multivariate normal distribution, Multivariate Youden index, Power of the test, Size of the test, Shiftedexponential distribution, Youden index