Health Information Technology Commons^™

Reducing Mr Acquisition Times Using Artificial Intelligence: A Systematic Review, Fatin M. Arief

Radiologic Sciences

Purpose To synthesize existing evidence on artificial intelligence (AI) applications for reducing magnetic resonance (MR) imaging acquisition time, evaluate their impact on image quality, and identify challenges in clinical implementation.

Method A systematic literature review was conducted using PubMed, CINAHL, and Scopus including peer reviewed, English-language articles published between 2020 and 2026. Both qualitative and quantitative primary research were eligible and had to be on AI use in MR processes. Findings were analyzed through thematic synthesis, and the quality of each study was appraised using relevant critical appraisal tools.

Results Three major themes emerged: AI methods in MR procedures, perceived …

A Systematic Review Of Intrusion Detection Systems For Internet Of Medical Things: Performance, Efficiency, Explainability, And Generalization, Oswald Adohinzin, Youssef Harrath

Research & Publications

The Internet of Medical Things (IoMT) has transformed health care delivery through medical devices, remote patient monitoring, and real-time clinical decision support. However, the proliferation of IoMT devices introduces security vulnerabilities that put patient safety and data privacy at risk. Intrusion Detection Systems (IDS) have emerged as essential components for protecting IoMT networks from cyberattacks. This article presents a systematic review of IoMT-IDS research, analyzing 53 high-quality papers published between 2020 and 2025, identified through database searches spanning 2016–2025 across IEEE Xplore, Springer, ScienceDirect, and ACM Digital Library. We organize the literature through a comprehensive taxonomy spanning classical machine learning …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

Data-Driven Survival Modeling For Breast Cancer Prognostics: A Comparative Study With Machine Learning And Traditional Survival Modeling Methods, Theophilus Gyedu Baidoo, Hansapani Rodrigo

School of Mathematical & Statistical Sciences Faculty Publications

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes.

Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) …

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

School of Public Health Faculty Publications

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to …

Harnessing Sociodemographic And Anthropometric Insights To Predict Type 2 Diabetes Risk: A Machine Learning Approach, Rico Kurniawan, Rezki Yunanda, Okky Assetya Pratiwi

Jurnal Biostatistik, Kependudukan, dan Informatika Kesehatan

The complexity of diabetes makes early diagnosis and effective management challenging for healthcare settings. This study developed and evaluated models using machine learning algorithms to predict the risk of diabetes. The data were primarily sourced from the Indonesia Family Life Survey (IFLS). Diabetes status was identified using A1c whole blood sample values (A1c WBS ³5.7%). Sociodemographic and anthropometric factors were set as predictors, most of which were significantly correlated with diabetes status. Discrete machine learning algorithms such as decision tree, k-nearest neighbors (KNN), random forest (RF), naïve Bayer, support vector machine (SVM), and neural network (MLP) were applied to construct …

Sexual Health In The Era Of Artificial Intelligence: A Scoping Review Of The Literature, Elia Abou Chawareb, Brian H. Im, Sherry Lu, Muhammed A.M. Hammad, Tiffany R. Huang, Henry Chen, Faysal A. Yafi

Student Papers, Posters & Projects

INTRODUCTION: Artificial Intelligence (AI) has witnessed significant growth in the field of medicine, leveraging machine learning, artificial neuron networks, and large language models. These technologies are effective in disease diagnosis, education, and prevention, while raising ethical concerns and potential challenges. However, their utility in sexual medicine remains relatively unexplored.

OBJECTIVE: We aim to provide a comprehensive summary of the status of AI in the field of sexual medicine.

METHODS: A comprehensive search was conducted using MeSH keywords, including "artificial intelligence," "sexual medicine," "sexual health," and "machine learning." Two investigators screened articles for eligibility within the PubMed and MEDLINE databases, with …

Predicting Mental Health Disparities Using Machine Learning For African Americans In Southeastern Virginia, Ismail El Moudden, Michael C. Bittner, Matvey V. Karpov, Isaac O. Osunmakinde, Akosua Acheamponmaa, Breshell J. Nevels, Mamadou T. Mbaye, Tonya L. Fields, Karthiga Jordan, Messaoud Bahoura

Department of Obstetrics & Gynecology Faculty Publications

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. …

Intelligent Health Monitoring Systems Via Flexible Hybrid Electronics And Machine Learning, Masoud Panahi

Dissertations

Advanced data analytics approaches, such as artificial intelligence (AI), are increasingly being integrated into all aspects of patient care. This integration is paving the way for minimally invasive or non-invasive treatment modalities. By combining recent advancements in fabrication technology and computing ingenuity, wearable devices now enable continuous health monitoring and provide data for AI-driven analysis. These intelligent devices seamlessly integrate with the Internet of Things (IoT), enabling remote or “at-home” patient monitoring.

This dissertation study investigates the integration of recent advancements in flexible hybrid electronics (FHE) and machine learning (ML) to develop intelligent health monitoring systems. The developed FHE-based health …

Keunggulan Dan Tantangan Dalam Penggunaan Computer Vision Untuk Diagnosis Pneumonia Pediatri: A Systematic Review, Hafshah Farah Fadhilah, Rico Kurniawan

Jurnal Biostatistik, Kependudukan, dan Informatika Kesehatan

Pneumonia pediatrik merupakan penyebab utama kematian anak-anak di bawah usia lima tahun. Teknologi computer vision menawarkan potensi besar untuk meningkatkan diagnosis pneumonia pediatrik dengan menganalisis gambar radiografi dada secara otomatis. Penelitian ini menggunakan metode systematic literature review dengan pendekatan PRISMA, meninjau artikel dari database IEEE Xplore, Science Direct, dan Scopus yang diterbitkan antara tahun 2020 hingga 2024. Studi ini menemukan bahwa algoritma deep learning seperti Convolutional Neural Networks (CNN) menunjukkan akurasi tinggi dalam diagnosis pneumonia pediatrik. Namun, tantangan seperti kebutuhan akan data berkualitas tinggi, interpretasi hasil AI, dan integrasi teknologi ini dengan sistem kesehatan yang ada masih perlu diatasi. Penggunaan …

Reliable Detection Of Generalized Convulsive Seizures Using An Off-The-Shelf Digital Watch: A Multisite Phase 2 Study, Yash Shashank Vakilna, Xiaojin Li, Jaison S Hampson, Yan Huang, John C Mosher, Yuri Dabaghian, Xi Luo, Blanca Talavera, Sandipan Pati, Masel Todd, Ryan Hays, Charles Akos Szabo, Guo-Qiang Zhang, Samden D Lhatoo

Faculty, Staff and Student Publications

Objective: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy.

Methods: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of …

Academic Detailing As A Health Information Technology Implementation Method: Supporting The Design And Implementation Of An Emergency Department-Based Clinical Decision Support Tool To Prevent Future Falls, Hanna J Barton, Apoorva Maru, Margaret A Leaf, Daniel J Hekman, Douglas A Wiegmann, Manish N Shah, Brian W Patterson

Faculty, Staff and Student Publications

BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation.

OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct …

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

Machine Learning Algorithms To Study Multi-Modal Data For Computational Biology, Khandakar Tanvir Ahmed

Graduate Thesis and Dissertation 2023-2024

Advancements in high-throughput technologies have led to an exponential increase in the generation of multi-modal data in computational biology. These datasets, comprising diverse biological measurements such as genomics, transcriptomics, proteomics, metabolomics, and imaging data, offer a comprehensive view of biological systems at various levels of complexity. However, integrating and analyzing such heterogeneous data present significant challenges due to differences in data modalities, scales, and noise levels. Another challenge for multi-modal analysis is the complex interaction network that the modalities share. Understanding the intricate interplay between different biological modalities is essential for unraveling the underlying mechanisms of complex biological processes, including …

Effectiveness Of An Emergency Department-Based Machine Learning Clinical Decision Support Tool To Prevent Outpatient Falls Among Older Adults: Protocol For A Quasi-Experimental Study, Daniel J Hekman, Amy L Cochran, Apoorva P Maru, Hanna J Barton, Manish N Shah, Douglas Wiegmann, Maureen A Smith, Frank Liao, Brian W Patterson

Faculty, Staff and Student Publications

Background

Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether …

Non-Melanoma Skin Cancer Detection In The Age Of Advanced Technology: A Review, Haleigh Stafford, Jane Buell, Elizabeth Chiang, Uma Ramesh, Michael Migden, Priyadharsini Nagarajan, Moran Amit, Dan Yaniv

Faculty, Staff and Student Publications

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with …

Machine Learning-Based Aggression Detection In Children With Adhd Using Sensor-Based Physical Activity Monitoring, Catherine Park, Mohammad Dehghan Rouzi, Md Moin Uddin Atique, M G Finco, Ram Kinker Mishra, Griselda Barba-Villalobos, Emily Crossman, Chima Amushie, Jacqueline Nguyen, Chadi Calarge, Bijan Najafi

Center on Aging Staff Publications

Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected. Machine learning techniques, specifically random forest, were used to analyze patterns that identify physical-aggressive incident …

The Clinical Suitability Of An Artificial Intelligence-Enabled Pain Assessment Tool For Use In Infants: Feasibility And Usability Evaluation Study, Jeffery David Hughes, Paola Chivers, Kreshnik Hoti

Research outputs 2022 to 2026

Background: Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health–based (mHealth) pain assessment tools could address current challenges and is an area requiring further research. Objective: The purpose of this study is to evaluate the accuracy and feasibility aspects of PainChek Infant and, therefore, assess its applicability in the intended setting. Methods: By observing infants just before, during, and after immunization, we evaluated the accuracy and precision at different cutoff scores of …

An Analytic And Systemic View Of The Digital Transformation Of Healthcare, Xuejuan J. Zhang

Full-Text Theses & Dissertations

Industry 4.0 represents a digital revolution that is driven by technologies that blur the lines between the physical and digital worlds. Industry 4.0, the latest industrial revolution, is poised to have a profound impact on all aspects of society. In order to understand how the healthcare industry is being transformed by the convergence of the physical and digital realms, a systems perspective is taken in this study. Two research questions are addressed regarding the opportunities and interventions that can be provided by both analytical and systems conceptions of digital transformation. I use a systemic literature review approach to address the …

Architectural Design Of A Blockchain-Enabled, Federated Learning Platform For Algorithmic Fairness In Predictive Health Care: Design Science Study, Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead …

Design Of Robust Blockchain-Envisioned Authenticated Key Management Mechanism For Smart Healthcare Applications, Siddhant Thapiyal, Mohammad Wazid, Devesh Pratap Singh, Ashok Kumar Das, Sachin Shetty

VMASC Publications

The healthcare sector is a very crucial and important sector of any society, and with the evolution of the various deployed technologies, like the Internet of Things (IoT), machine learning and blockchain it has numerous advantages. However, in this section, the data is much more vulnerable than others, because the data is strictly private and confidential, and it requires a highly secured framework for the transmission of data between entities. In this article, we aim to design a blockchain-envisioned authentication and key management mechanism for the IoMT-based smart healthcare applications (in short, we call it SBAKM-HS). We compare the various …

Leveraging Context Patterns For Medical Entity Classification, Garrett Johnston

Computer Science Senior Theses

The ability of patients to understand health-related text is important for optimal health outcomes. A system that can automatically annotate medical entities could help patients better understand health-related text. Such a system would also accelerate manual data annotation for this low-resource domain as well as assist in down- stream medical NLP tasks such as finding textual similarity, identifying conflicting medical advice, and aspect-based sentiment analysis. In this work, we investigate a state-of-the-art entity set expansion model, BootstrapNet, for the task of medical entity classification on a new dataset of medical advice text. We also propose EP SBERT, a simple model …

The Contribution Of Ethical Governance Of Artificial Intelligence & Machine Learning In Healthcare, Tina Nguyen

Electronic Theses and Dissertations

With the Internet Age and technology progressively advancing every year, the usage of Artificial Intelligence (AI) along with Machine Learning (ML) algorithms has only increased since its introduction to society. Specifically, in the healthcare field, AI/ML has proven to its end-users how beneficial its assistance has been. However, despite its effectiveness and efficiencies, AI/ML has also been under scrutiny due to its unethical outcomes. As a result of this, two polarizing views are typically debated when discussing AI/ML. One side believes that AI/ML usage should continue regardless of its unsureness, while the other side argues that this technology is too …

Novel Deep Learning Approach To Model And Predict The Spread Of Covid-19, Devante Ayris, Maleeha Imtiaz, Kye Horbury, Blake Williams, Mitchell Blackney, Celine Shi Hui See, Syed Afaq Ali Shah

Research outputs 2022 to 2026

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained …

The Age Of Artificial Intelligence: Use Of Digital Technology In Clinical Nutrition, Berkeley K. Limketkai, Kasuen Mauldin, Natalie Manitius, Laleh Jalilian, Bradley R. Salonen

Faculty Research, Scholarly, and Creative Activity

Purpose of review

Computing advances over the decades have catalyzed the pervasive integration of digital technology in the medical industry, now followed by similar applications for clinical nutrition. This review discusses the implementation of such technologies for nutrition, ranging from the use of mobile apps and wearable technologies to the development of decision support tools for parenteral nutrition and use of telehealth for remote assessment of nutrition.

Recent findings

Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data. Machine learning has allowed for more complex analyses of the increasing volume of data collected. The …

Developing A New Analytical Model For Combatting Crises: A Comprehensive Review, Seth Spire

Research Awards

As the world emerges from the COVID-19 pandemic, it is critical to reflect on the lessons learned and prepare for potential future health crises. While data analytics and artificial intelligence (AI) played a pivotal role in managing the pandemic, there is much room for improvement and further use. This study examines the current state of data science tools employed during COVID-19, evaluating their advantages, limitations, and challenges in their broader implementation. We also review literature on future directions for AI in healthcare. Our findings highlight significant challenges, including difficulties in accessing usable data and common distrust of AI models for …

Machine Learning To Predict Sports-Related Concussion Recovery Using Clinical Data, Yan Chu, Gregory Knell, Riley P. Brayton, Scott O. Burkhart, Xiaoqian Jiang, Shayan Shams

Faculty Publications - Doctor of Psychology (PsyD) Program

Objectives: Sport-related concussions (SRCs) are a concern for high school athletes. Understanding factors contributing to SRC recovery time may improve clinical management. However, the complexity of the many clinical measures of concussion data precludes many traditional methods. This study aimed to answer the question, what is the utility of modeling clinical concussion data using machine-learning algorithms for pre- dicting SRC recovery time and protracted recovery? Methods: This was a retrospective case series of participants aged 8 to 18 years with a diagnosis of SRC. A 6- part measure was administered to assess pre-injury risk factors, initial injury severity, and post-concussion …

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 …

Analysis Of Massive Online Medical Consultation Service Data To Understand Physicians’ Economic Return: Observational Data Mining Study, Jinglu Jiang, Ann-Frances Cameron, Ming Yang

Management and Accounting Faculty Scholarship

Background: Online health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services. Objective: This study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the …