Information Technology for Evidence Based Medicine: Status and Future Direction

Evidence based medicine (EBM) refers to the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. This article presents systematic review of the use of information technology (IT) to support EBM with a particular emphasis on status and opportunities. Out of 2,490 papers initially scanned, 585 articles were included at the title level review. This was followed by an abstract review, which resulted in 196 articles. On full text scanning of the 196 articles, 69 articles met the inclusion criteria and were included in the final analyses. The key issues and potential for IT support for the practice of EBM are insufficient techniques to produce evidence in a computer interpretable format, insufficient research to combine the evidence from the multiple sources, inadequate techniques that automatically rate the literature and practice-based evidence, and integration of evidence at the clinician’s workflow

A mHealth Architecture for Diabetes Self-Management System

Recent advancement in smartphones coupled with the proliferation of data connectivity has resulted in increased interest and unprecedented growth in mobile applications for diabetes self-management. Nevertheless, a review of the literature highlights critical gaps between available functionality and user requirements and expectations. In this paper, we present a mHealth architecture of diabetes self-management system. The architecture has the following functionalities: automated data-entry through the use of wireless sensors; adherence to clinical guidelines; advanced statistical techniques for diabetes modeling and prediction; and advanced charting capabilities for data presentation and quality control

Understanding the Influence of Digital Divide and Socio-Economic Factors on the Prevalence of Diabetes

https://scholar.dsu.edu/research-symposium/1010/thumbnail.jp

Detection of Prostate Cancer Using Machine Learning Techniques: An Exploratory Study

Prostate Cancer (PCa) is one of the most frequent cancers worldwide and the most common cancer in males. Testing for PCa remains problematic. Evidence is mounting that overdiagnosis and over-treatment can result in adverse side-effects yet have little impact in preventing death from PCa. Consequently, the importance of predictive tools that help physicians in the diagnosis of the condition cannot be understated. Though there exist several predictive models for the detection of clinically significant PCa, these models mainly depend on logistic regression. The objective of this research is to investigate the potential of various machine learning techniques to improve the sensitivity and specificity of detecting clinically significant PCa. Risk factors considered include prostate-specific antigen (PSA), digital rectal examination (DRE), as well as age, race/ethnicity, and family history. According to the results, Logistic Regression has outperformed all the models followed by Random Forest, SVM and XG Boost

INFLUENCE OF THE DIGITAL DIVIDE AND SOCIO-ECONOMIC FACTORS ON PREVALENCE OF DIABETES

More than 100 million Americans have diabetes or prediabetes (29 and 84 million, respectively). Factors such as overweight, sedentary behavior, and history of diabetes in the family have been commonly associated with the onset of type 2 diabetes. Extant literature now points to the effect of socio-economic factors such as education, income, ethnicity, and physical location on the prevalence of the disease. This research aims to investigate the impact of social determinants on diabetes with a particular emphasis on the digital divide. We used data from the Centers for Disease Control and Prevention (CDC) for diagnosed diabetes prevalence, obesity prevalence, and leisure time physical inactivity data for the year 2013 by county. We contrasted the diabetes prevalence data against social factors such as race, educational attainment, income, poverty, unemployment, and digital divide obtained from the US Census Bureau data. Used bivariate, multivariate and regression analysis reveals a statistically significant relation between the prevalence of diabetes and digital divide, race, education, income and unemployment rate, obesity prevalence, and leisure time physical inactivity (P\u3c0.000). Overall, the results demonstrate the significant role of the digital divide in influencing chronic conditions such as diabetes

Social Media for Exploring Adverse Drug Events Associated with Multiple Sclerosis

Application design has been revolutionized with the adoption of microservices architecture. The ability to estimate end-to-end response latency would help software practitioners to design and operate microservices applications reliably and with efficient resource capacity. The objective of this research is to examine and compare data-driven approaches and a variety of resource metrics to predict end-to-end response latency of a containerized microservices workflow running in a cloud Kubernetes platform. We implemented and evaluated the prediction using a deep neural network and various machine learning techniques while investigating the selection of resource utilization metrics. Observed characteristics and performance metrics from both microservices and platform levels were used as prediction indicators. To compare performance models, we experimented with a benchmarking open-source Sock Shop containerized application. A deep neural network technique exhibited the best prediction accuracy using all metrics, while other machine learning techniques demonstrated acceptable performance using a subset of the metrics

Mobile Applications for Diabetes Self-Management: Status and Potential

Background:Advancements in smartphone technology coupled with the proliferation of data connectivity has resulted in increased interest and unprecedented growth in mobile applications for diabetes self-management. The objective of this article is to determine, in a systematic review, whether diabetes applications have been helping patients with type 1 or type 2 diabetes self-manage their condition and to identify issues necessary for large-scale adoption of such interventions.Methods:The review covers commercial applications available on the Apple App Store (as a representative of commercially available applications) and articles published in relevant databases covering a period from January 1995 to August 2012. The review included all applications supporting any diabetes self-management task where the patient is the primary actor.Results:Available applications support self-management tasks such as physical exercise, insulin dosage or medication, blood glucose testing, and diet. Other support tasks considered include decision support, notification/alert, tagging of input data, and integration with social media. The review points to the potential for mobile applications to have a positive impact on diabetes self-management. Analysis indicates that application usage is associated with improved attitudes favorable to diabetes self-management. Limitations of the applications include lack of personalized feedback; usability issues, particularly the ease of data entry; and integration with patients and electronic health records.Conclusions:Research into the adoption and use of user-centered and sociotechnical design principles is needed to improve usability, perceived usefulness, and, ultimately, adoption of the technology. Proliferation and efficacy of interventions involving mobile applications will benefit from a holistic approach that takes into account patients\u27 expectations and providers\u27 needs

Social Media for Exploring Adverse Drug Events Associated with Multiple Sclerosis

Multiple Sclerosis (MS) affects 400,000 people in the USA and almost 2.5 million people worldwide. There is no cure for MS. A variety of disease-modifying therapies are currently available. They aim to reduce disease activity that ultimately leads to disability. However, such drugs have adverse effects that vary widely among patients making the choice of a suitable drug particularly challenging. With the proliferation of social media, this research aims to understand the perspective of people with MS on social media (Twitter) in regard to Adverse Drug Events (ADEs) and to analyze ADEs as perceived by MS patients. This study helps in understanding ADEs associated with MS drugs and can further inform future medical research by highlighting and prioritizing additional clinical trials needed to better assess such adverse drug effects

Leveraging Advanced Analytics to Generate Dynamic Medical Systematic Reviews

According to Khan et al, “a review earns the adjective systematic if it is based on a clearly formulated question, identifies relevant studies, appraises their quality and summarizes the evidence by use of explicit methodology”. Conducting systematic reviews tend to be resource intensive and may suffer from problems such as publication bias, time-lag bias, duplicate bias, citation bias, and outcome reporting bias. This research aims to develop a system to facilitate the creation of systematic reviews. Starting with a clinical question, the proposed system will query ClinicalTrial.gov to search published RCTs. The system will exploit advanced data analytics techniques to systematically mine clinical trials obtained from the ClinicalTrial.gov. From the theoretical perspective, the system provides context for exploring the feasibility and efficacy of using advanced analytics techniques for generating machine readable, real time medical evidence. From a practical perspective, the system is expected to produce cost efficient medical evidence