Medicine and Health Sciences Commons^™

Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas

SMU Data Science Review

In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …

Exploring Emotion Recognition For Vr-Ebt Using Deep Learning On A Multimodal Physiological Framework, Nicholas Dass

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality …

Feasibility And Acceptability Of A Rural, Pragmatic, Telemedicine‐ Delivered Healthy Lifestyle Programme, John A. Batsis, Auden C. Mcclure, Aaron B. Weintraub, David F. Kotz, Sivan Rotenberg, Summer B. Cook, Diane Gilbert-Diamond, Kevin Curtis, Courtney J. Stevens, Diane Sette, Richard I. Rothstein

Dartmouth Scholarship

Background: The public health crisis of obesity leads to increasing morbidity that are even more profound in certain populations such as rural adults. Live, two‐way video‐conferencing is a modality that can potentially surmount geographic barriers and staffing shortages. Methods: Patients from the Dartmouth‐Hitchcock Weight and Wellness Center were recruited into a pragmatic, single‐arm, nonrandomized study of a remotely delivered 16‐week evidence‐based healthy lifestyle programme. Patients were provided hardware and appropriate software allowing for remote participation in all sessions, outside of the clinic setting. Our primary outcomes were feasibility and acceptability of the telemedicine intervention, as well as potential effectiveness on …

Exploring The State-Of-Receptivity For Mhealth Interventions, Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, Tobias Kowatsch

Dartmouth Scholarship

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this …

The Dynamic Impacts Of Online Healthcare Community On Physician Altruism: A Hidden Markov Model, Kai Luo, Qiu-Hong Wang, Hock Hai Teo

Research Collection School Of Computing and Information Systems

Physician altruism is not only a key foundation of modern medical professionalism, but also a critical component in the theoretical health economics study. There is considerable interest in understanding the impacts of contemporary healthcare technology on physician altruism. In this paper, we investigate the dynamic influence of multiple incentive mechanisms developed by an online healthcare community (OHC) on physician altruism. We model physician altruism as the degree of tendency to benefit the patients at the cost of oneself and focus on the incentive mechanisms that give physicians social and economic returns. The dynamics of physician altruism is characterized via a …

Examining The Theoretical Mechanisms Underlying Health Information Exchange Impact On Healthcare Outcomes: A Physician Agency Perspective, Fang Zhou, Qiu-Hong Wang, Hock Hai Teo

Research Collection School Of Computing and Information Systems

Health information exchange (HIE) is presumed to reduce medical costs by facilitating information sharing across healthcare providers. Existing studies focused on different medical costs or one set of costs, and resulted in mixed findings. We examine the effects of patient access to HIE on two of the most important medical costs of a hospitalization episode - test costs and medication costs - through a natural experiment and the discharge data of a hospital. Besides the negative direct effect of access to HIT on tests costs, we also find its positive spillover effect on medication costs, such that more patients having …

Objective Sleep Quality As A Predictor Of Mild Cognitive Impairment In Seniors Living Alone, Brian Chen, Hwee-Pink Tan, Irus Rawtaer, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia. In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction …

Improving Medication Information Presentation Through Interactive Visualization In Mobile Apps: Human Factors Design, Don Roosan, Yan Li, Anandi Law, Huy Truong, Mazharul Karim, Jay Chok, Moom Roosan

Pharmacy Faculty Articles and Research

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.

Objective: The …

Introducing A Mobile Health Care Platform In An Underserved Rural Population: Reducing Assimilations Gaps On Adoption And Use Via Nudges, Joseph Hodges

USF Tampa Graduate Theses and Dissertations

Rural communities are often overlooked when it comes to offering cutting edge consumer healthcare technologies. Mobile applications usually exclude populations in rural demographics due to the infrastructure requirements and available technology in the region. The population studied is a low income rural health plan in southwest Georgia. They are uniquely considered as they have the highest healthcare costs in the U.S. and are compared to healthcare costs among higher income populations like Vail, Colorado. Innovations, such as mobile healthcare applications, have the capacity to offset some of these costs, but even if adoption occurs, this does not guarantee use will …

Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study Of Plasmodium Falciparum Hsp70s, Arnold Amusengeri, Lindy Astl, Kevin Lobb, Gennady M. Verkhivker, Özlem Tastan Bishop

Mathematics, Physics, and Computer Science Faculty Articles and Research

Combating malaria is almost a never-ending battle, as Plasmodium parasites develop resistance to the drugs used against them, as observed recently in artemisinin-based combination therapies. The main concern now is if the resistant parasite strains spread from Southeast Asia to Africa, the continent hosting most malaria cases. To prevent catastrophic results, we need to find non-conventional approaches. Allosteric drug targeting sites and modulators might be a new hope for malarial treatments. Heat shock proteins (HSPs) are potential malarial drug targets and have complex allosteric control mechanisms. Yet, studies on designing allosteric modulators against them are limited. Here, we identified allosteric …

Characterizing And Predicting Repeat Food Consumption Behavior For Just-In-Time Interventions, Yue Liu, Helena Huey Chong Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin

Research Collection School Of Computing and Information Systems

Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community …

Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee

Research Collection School Of Computing and Information Systems

We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of …

Vitamon: Measuring Heart Rate Variability Using Smartphone Front Camera, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko, Youngki Lee

Research Collection School Of Computing and Information Systems

We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network …

Estimating Glycemic Impact Of Cooking Recipes Via Online Crowdsourcing And Machine Learning, Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Research Collection School Of Computing and Information Systems

Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can …

Artificial Intelligence And The Challenge For Rural Medicine, James Denvir

Marshall Journal of Medicine

Recent advances in artificial intelligence, machine learning, and deep learning are beginning to have an impact on everyday experiences, from natural language processing used in automated telephone call centers to semi-autonomous vehicles. These techniques have also been applied to medical care. In this editorial we discuss applications of AI to medicine and argue for a proactive approach to include rural medicine in this paradigm shift.

Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan

Theses

As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …

Game-Assisted Rehabilitation For Post-Stroke Survivors, Hee-Tae Jung

Doctoral Dissertations

Stroke is a leading cause of permanent impairments among its survivors. Although patients need to go through intensive, longitudinal rehabilitation to regain function before the stroke, patients show poor engagement and adherence to rehabilitation therapies which hampers their recovery. As a means to enhance stroke survivors' motivation, engagement, and adherence to intensive and longitudinal rehabilitation, the use of games in stroke rehabilitation has received attention from research and clinical communities. In order to realize this, it is important to take a holistic, end-to-end research approach that encompasses 1) the development of game technologies that are not only entertaining but also …

Digital Addiction: A Conceptual Overview, Amarjit Kumar Singh, Pawan Kumar Singh

Library Philosophy and Practice (e-journal)

Abstract

Digital addiction referred to an impulse control disorder that involves the obsessive use of digital devices, digital technologies, and digital platforms, i.e. internet, video game, online platforms, mobile devices, digital gadgets, and social network platform. It is an emerging domain of Cyberpsychology (Singh, Amarjit Kumar and Pawan Kumar Singh; 2019), which explore a problematic usage of digital media, device, and platforms by being obsessive and excessive. This article analyses, reviewed the current research, and established a conceptual overview on the digital addiction. The research literature on digital addiction has proliferated. However, we tried to categories the digital addiction, according …

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …

A Computational Model For Recovery From Brain Injury, Wayne Wakeland

Systems Science Friday Noon Seminar Series

A computational simulation model calculates recovery trajectories following traumatic brain injury (TBI). Prior publications include a multi-scale framework for studying concussion and a systems-level causal loop diagram (CLD) and discussion of feedback processes. The scope of the computational model goes beyond concussion to include all severities of TBI. A set of first order ordinary differential equations and their associated parameters determines recovery trajectories. While highly speculative, the model serves to demonstrate the potential utility of computational models in this context. Much more research will be needed to create a properly supported research model that could be used for clinical trial …

Follow-Up Decision Support Tool For Public Healthcare: A Design Research Perspective, Shah J. Miah, Najmul Hasan, John Gammack

All Works

© 2019, Korean Society of Medical Informatics. All rights reserved. Objectives: Mobile health (m-Health) technologies may provide an appropriate follow-up support service for patient groups with post-treatment conditions. While previous studies have introduced m-Health methods for patient care, a smart system that may provide follow-up communication and decision support remains limited to the management of a few specific types of diseases. This paper introduces an m-Health solution in the current climate of increased demand for electronic information exchange. Methods: Adopting a novel design science research approach, we developed an innovative solution model for post-treatment follow-up decision support interaction for use …

Smartbfa: A Passive Crowdsourcing System For Point-To-Point Barrier-Free Access, Mohammed Nazir Kamaldin, Susan Kee, Songwei Kong, Chengkai Lee, Huiguang Liang, Alisha Saini, Hwee-Pink Tan, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

At the Bloomberg Live `Sooner Than You Think' forum [1] held in Singapore in 2018, nearly 75% of delegates picked inclusiveness to be the key measure of success for a smart city. An inclusive smart city is a citizen-centered approach that extends the experiences provided by smart city solutions to all citizens, including seniors and persons with disabilities (PwDs).Despite existing regulations on barrier-free accessibility for buildings and public infrastructure, pedestrian infrastructure is generally still inaccessible to PwDs in many parts of the world. In this paper, we present SmartBFA (Smart Mobility and Accessibility for Barrier Free Access) - a publicly-funded …

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

A Low-Cost Soft Robotic Hand Exoskeleton For Use In Therapy Of Limited Hand–Motor Function, Grant Rudd, Liam Daly, Vukica Jovanovic, Filip Cukov

Engineering Technology Faculty Publications

We present the design and validation of a low-cost, customizable and 3D-printed anthropomorphic soft robotic hand exoskeleton for rehabilitation of hand injuries using remotely administered physical therapy regimens. The design builds upon previous work done on cable actuated exoskeleton designs by implementing the same kinematic functionality, but with the focus shifted to ease of assembly and cost effectiveness as to allow patients and physicians to manufacture and assemble the hardware necessary to implement treatment. The exoskeleton was constructed solely from 3D-printed and widely available of-the-shelf components. Control of the actuators was realized using an Arduino microcontroller, with a custom-designed shield …

Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones …

Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra

University of New Orleans Theses and Dissertations

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …

Law Library Blog (August 2019): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Successful Shot Locations And Shot Types Used In Ncaa Men’S Division I Basketball, Olivia D. Perrin

All NMU Master's Theses

The primary purpose of the current study was to investigate the effect of court location (distance and angle from basket) and shot types used on shot success in NCAA Men’s DI basketball during the 2017-18 season. A secondary purpose was to further expand the analysis based on two additional factors: player position (guard, forward, or center) and team ranking. All statistical analyses were completed in RStudio and three binomial logistic regression analyses were performed to evaluate factors that influence shot success; one for all two and three point shot attempts, one for only two point attempts, and one for only …

Investigating The Effects Of Social And Temporal Dynamics In Fitness Games On Children's Physical Activity, Ankita Samariya

Boise State University Theses and Dissertations

Obesity in children has more than doubled in the last four decades. A variety of fitness technologies such as activity trackers, exergames, and mobile applications are available to promote physical activity, but few are designed adequately to meet children's specific needs. In my initial research, I found that children prefer having social interaction, a narrative, and flexibility while interacting with such technologies. Other research has shown that persuasive displays encouraged adults towards physical activity. As a part of this work, a collection of mobile applications, collectively known as KidFit Suite were developed. These applications encourage children to be physically active …