Physical Sciences and Mathematics Commons^™

Regional Integration: Physician Perceptions On Electronic Medical Record Use And Impact In South West Ontario, Sadiq Raji

Electronic Thesis and Dissertation Repository

Regional initiatives in the health care context in Canada are typically organized and administered along geographic boundaries or operational units. Regional integration of Electronic Medical Records (EMR) has been continuing across Canadian provinces in recent years, yet the use and impact of regionally integrated EMRs are not routinely assessed and questions remain about their impact on and use in physicians’ practices. Are stated goals of simplifying connections and sharing of electronic health information collected and managed by many health services providers being met? What are physicians’ perspectives on the use and impact of regionally integrated EMR? In this thesis, I …

Thaw Publications, Carl Landwehr, David Kotz

Computer Science Technical Reports

In 2013, the National Science Foundation's Secure and Trustworthy Cyberspace program awarded a Frontier grant to a consortium of four institutions, led by Dartmouth College, to enable trustworthy cybersystems for health and wellness. As of this writing, the Trustworthy Health and Wellness (THaW) project's bibliography includes more than 130 significant publications produced with support from the THaW grant; these publications document the progress made on many fronts by the THaW research team. The collection includes dissertations, theses, journal papers, conference papers, workshop contributions and more. The bibliography is organized as a Zotero library, which provides ready access to citation materials …

Towards Development Of A Remote Charting System For Connected Healthcare, Alex Bodurka

Masters Theses

Health Care Providers play a crucial role in a patients well-being. While their primary role is to treat the patient, it is also vital to ensure that they can spend adequate time with the patient to create a unique treatment plan and build a personal relationship with their patients to help them feel comfortable during their treatment. Health Care Providers are frequently required to manually record patient data to track their healthcare progress during their hospital stay. However, with hospitals continuously trying to optimize their workflows, this crucial one-on-one time with the patient is often not practical.

As a solution, …

Gym Usage Behavior & Desired Digital Interventions: An Empirical Study, Meeralakshmi Radhakrishnan, Archan Misra, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Understanding individual’s exercise motives, participation patterns in a gym and reasons for dropout are essential for designing strategies to help gym-goers with long-term exercise adherence. In this work, we derive insights on various exercise-related behaviors of gymgoers, including evidence of a significant number of individuals exhibiting early dropout and also describing their attitudes towards digital technologies for sustained gym participation. By utilizing gym visitation data logs of 6513 individuals over a longitudinal period of 16 months in a campus gym, we show the retention and dropout rates of gym-goers. Our data indicates that 32% of the people quit their gym …

Barriers And Facilitators In Implementing A Pilot, Pragmatic, Telemedicine-Delivered Healthy Lifestyle Program For Obesity Management In A Rural, Academic Obesity Clinic, John A. Batsis, Auden C. Mcclure, Aaron B. Weintraub, Diane Sette, Sivan Rotenberg, Courtney J. Stevens, Diane Gilbert-Diamond, David Kotz, Stephen Bartels, Summer B. Cook, Richard I. Rothstein

Dartmouth Scholarship

Few evidence-based strategies are specifically tailored for disparity populations such as rural adults. Two-way video-conferencing using telemedicine can potentially surmount geographic barriers that impede participation in high-intensity treatment programs offering frequent visits to clinic facilities. We aimed to understand barriers and facilitators of implementing a telemedicine-delivered tertiary-care, rural academic weight-loss program for the management of obesity.

A Neutrosophic Clinical Decision-Making System For Cardiovascular Diseases Risk Analysis, Florentin Smarandache, Shaista Habib, Wardat-Us- Salam, M. Arif Butt, Muhammad Akram

Branch Mathematics and Statistics Faculty and Staff Publications

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirtyfive symptoms as input parameters, which are, gender, …

Ecu-Ioht, Mohiuddin Ahmed, Surender Byreddy, Anush Nutakki, Leslie F. Sikos, Paul Haskell-Dowland

Research Datasets

In recent times, cyberattacks on Internet of Health Things (IoHT) have continuously been growing, and so it is important to develop robust countermeasures. However, there is a lack of publicly available datasets reflecting cyberattacks on IoHT, mainly due to privacy concerns. To strengthen the cyber security of IoHT, we have developed the dataset, named ECU-IoHT, that is built upon an IoHT environment having different attacks performed that exploit various vulnerabilities. This dataset was designed to help the healthcare security community in analyzing attack behaviour and developing robust countermeasures. To the best of our knowledge, no other publicly available datasets have …

Automatic Recognition, Segmentation, And Sex Assignment Of Nocturnal Asthmatic Coughs And Cough Epochs In Smartphone Audio Recordings: Observational Field Study, Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, Tobias Kowatsch

Dartmouth Scholarship

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio …

Projecting The Covid-19 Weekly Deaths And Hospitalizations For Jefferson County, Kentucky, Seyed Karimi, Natalie Dupre, W. Paul Mckinney, Bert B. Little, Naiya Patel, Sarah Moyer

The University of Louisville Journal of Respiratory Infections

Introduction: The trends in the numbers of active hospitalizations and fatalities caused by the COVID-19 in Jefferson County, Kentucky, were projected over the period May 7 to August 20, 2020.

Methods: The projections provided in this report are from a susceptible-exposed-infectious-recovered (SEIR) model. The model was calibrated using the COVID-19 transmission dynamics parameters from relevant literature and clinical dynamics parameters from the county’s data. The model was used to measure the impact of public health policy interventions designed to contain the infection. The policy was modeled by its intervention day and impact on the transmission of the virus such that …

Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the booming of Internet of Things (IoT), smart health (s-health) is becoming an emerging and attractive paradigm. It can provide an accurate prediction of various diseases and improve the quality of healthcare. Nevertheless, data security and user privacy concerns still remain issues to be addressed. As a high potential and prospective solution to secure IoT-oriented s-health applications, ciphertext policy attribute-based encryption (CP-ABE) schemes raise challenges, such as heavy overhead and attribute privacy of the end users. To resolve these drawbacks, an optimized vector transformation approach is first proposed to efficiently transform the access policy and user attribute set into …

Camps: Efficient And Privacy-Preserving Medical Primary Diagnosis Over Outsourced Cloud, Jianfeng Hua, Guozhen Shi, Hui Zhu, Fengwei Wang, Ximeng Liu, Hao Li

Research Collection School Of Computing and Information Systems

With the flourishing of ubiquitous healthcare and cloud computing technologies, medical primary diagnosis system, which forms a critical capability to link big data analysis technologies with medical knowledge, has shown great potential in improving the quality of healthcare services. However, it still faces many severe challenges on both users' medical privacy and intellectual property of healthcare service providers, which deters the wide adoption of medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis framework (CAMPS). Within CAMPS framework, the precise diagnosis models are outsourced to the cloud server in an encrypted manner, and …

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom

Research & Publications

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …

Emerging Technologies In Healthcare: Analysis Of Unos Data Through Machine Learning, Reyhan Merekar

Student Theses and Dissertations

The healthcare industry is primed for a massive transformation in the coming decades due to emerging technologies such as Artificial Intelligence (AI) and Machine Learning. With a practical application to the UNOS (United Network of Organ Sharing) database, this Thesis seeks to investigate how Machine Learning and analytic methods may be used to predict one-year heart transplantation outcomes. This study also sought to improve on predictive performances from prior studies by analyzing both Donor and Recipient data. Models built with algorithms such as Stacking and Tree Boosting gave the highest performance, with AUC’s of 0.6810 and 0.6804, respectively. In this …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Who And When To Screen: Multi-Round Active Screening For Network Recurrent Infectious Diseases Under Uncertainty, Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, Alpan Raval, Milind Tambe

Research Collection School Of Computing and Information Systems

Controlling recurrent infectious diseases is a vital yet complicated problem in global health. During the long period of time from patients becoming infected to finally seeking treatment, their close contacts are exposed and vulnerable to the disease they carry. Active screening (or case finding) methods seek to actively discover undiagnosed cases by screening contacts of known infected people to reduce the spread of the disease. Existing practice of active screening methods often screen all contacts of an infected person, requiring a large budget. In cooperation with a research institute in India, we develop a model of the active screening problem …

Early Detection Of Mild Cognitive Impairment With In-Home Sensors To Monitor Behavior Patterns In Community-Dwelling Senior Citizens In Singapore: Cross-Sectional Feasibility Study, Iris Rawtaer, Rathi Mahendran, Ee Heok Kua, Hwee-Pink Tan, Hwee Xian Tan, Tih-Shih Lee, Tze Pin Ng

Research Collection School Of Computing and Information Systems

Background: Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.Objective: The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.Methods: We recruited 59 community-dwelling seniors (aged >65 years …

Finding Trends In Big City Health Issues With Data Visualization, Shridhar Kulkarni

Dissertations and Theses

In recent years, data visualization has become one of the most effective tools to understand and identify unseen features of the large datasets available. An open source data set available for health issues for big cities across the United States was obtained. There are numerous indicators presented in the dataset including Demographics, Chronic Health Diseases, Social and Economic Factors, Food Safety, Mortality Rates, Cancer and Life Expectancy Rates. The dataset encompassed myriad of demographics as well as specific data for a number of US cities. The data was explored in different methods in Data points in terms of the demographic …

The Effectiveness Of Transfer Learning Systems On Medical Images, James Boit

Masters Theses & Doctoral Dissertations

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned …

A Survey Of Feature Extraction And Fusion Of Deep Learning For Detection Of Abnormalities In Video Endoscopy Of Gastrointestinal-Tract, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani, Farhan Riaz

Publications

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for …

Cyber Security In The Healthcare Industry, Giovanni Ordonez 20

Honor Scholar Theses

No abstract provided.

Cybersecurity Risk-Responsibility Taxonomy: The Role Of Cybersecurity Social Responsibility In Small Enterprises On Risk Of Data Breach, Keiona Davis

CCE Theses and Dissertations

With much effort being placed on the physical, procedural, and technological solutions for Information Systems (IS) cybersecurity, research studies tend to focus their efforts on large organizations while overlooking very smaller organizations (below 50 employees). This study addressed the failure to prevent data breaches in Very Small Enterprises (VSEs). VSEs contribute significantly to the economy, however, are more prone to cyber-attacks due to the limited risk mitigations on their systems and low cybersecurity skills of their employees. VSEs utilize Point-of-Sale (POS) systems that are exposed to cyberspace, however, they are often not equipped to prevent complex cybersecurity issues that can …

Electrospinning Piezoelectric Fibers For Biocompatible Devices, Bahareh Azimi, Mario Milazzo, Andrea Lazzeri, Stefano Berrettini, M. Jasim Uddin, Zhao Qin, Markus J. Buehler, Serena Danti

Chemistry Faculty Publications and Presentations

The field of nanotechnology has been gaining great success due to its potential in developing new generations of nanoscale materials with unprecedented properties and enhanced biological responses. This is particularly exciting using nanofibers, as their mechanical and topographic characteristics can approach those found in naturally occurring biological materials. Electrospinning is a key technique to manufacture ultrafine fibers and fiber meshes with multifunctional features, such as piezoelectricity, to be available on a smaller length scale, thus comparable to subcellular scale, which makes their use increasingly appealing for biomedical applications. These include biocompatible fiber-based devices as smart scaffolds, biosensors, energy harvesters, and …

Lightweight Sharable And Traceable Secure Mobile Health System, Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li

Research Collection School Of Computing and Information Systems

Mobile health (mHealth) has emerged as a new patient centric model which allows real-time collection of patient data via wearable sensors, aggregation and encryption of these data at mobile devices, and then uploading the encrypted data to the cloud for storage and access by healthcare staff and researchers. However, efficient and scalable sharing of encrypted data has been a very challenging problem. In this paper, we propose a Lightweight Sharable and Traceable (LiST) secure mobile health system in which patient data are encrypted end-to-end from a patient’s mobile device to data users. LiST enables efficient keyword search and finegrained access …