Digital Commons Network^™

2026 Cyber-Resilient Health Care Workshop Report, Malcolm Schongalla, Sergey Bratus

Computer Science Technical Reports

The ISTS and the Dartmouth College Cybersecurity Cluster hosted the successful, inaugural Cyber-Resilient Health Care (CRHC) Workshop, March 5th & 6th, 2026. The event theme was "Innovation and Implementation," in response to the need to shift from reactive to proactive resiliency measures in the healthcare sector. Approximately 30 experts in clinical health care, cybersecurity, medical technology, policy, and innovation met to discuss solution-focused innovations addressing hard, cyber-related problems in health care. The agenda featured keynotes, an expert panel, innovation pitches, small group discussions, and a tabletop infrastructure disaster exercise. Participants gained insights into the obstacles and solutions involved in supporting …

Parkinson’S Disease Phenotype Stratification Using Multiple Correspondence Analysis, Kelly Astudillo

Dissertations, Theses, and Capstone Projects

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with over 12 million people projected to be affected by 2040 (Dorsey et al., 2018). Deep phenotyping and stratification can provide useful information regarding PD pathogenesis and can aid in the  development of disease modifying therapies that aim to delay the progression or prevent the onset of neurodegeneration (Blandini et al., 2019; Smith & Schapira, 2022). Utilizing multivariate methods such as multiple correspondence analysis (MCA) permits for the simultaneous analysis of distinct data modalities. To the best of our knowledge, MCA has not been previously used to explore phenotype patterns …

A Predictive Coding Account Of Spatial Working Memory Following Prophylactic Levetiracetam Administration Prior To Traumatic Brain Injury, Omeima Mutwali

Dissertations, Theses, and Capstone Projects

Traumatic brain injury (TBI) symptom prevention and remediation is an important area of research that would benefit vulnerable groups, including active-duty and veteran soldiers. These patients can sustain penetrative forces in fields of combat or in training, which result in focal lesions that trigger inflammatory and degenerative processes in the brain. Both primary and secondary injuries are associated with changes to cognition, behavior and affective state. This disease poses increased risk of epileptogenesis, as well. Given these outcomes, prior research has evaluated levetiracetam (LEV) as a prophylactic treatment for seizures, cognitive deficits and negative emotionality. LEV acts as a presynaptic …

Computational Insights Into Nucleosome Dynamics In Epigenetics Using Molecular Dynamics Simulations, Rutika Patel

Dissertations, Theses, and Capstone Projects

Nucleosome core particles (NCP) are the building blocks that form a highly organized and compact chromatin structure. Nucleosomes package DNA in the nucleus of eukaryotic cells. The NCP consists of about 147 base pairs of DNA wrapped around the histone octamer, with 1.65 superhelical turns in a left-handed manner. The histone octamer is composed of two copies of H3, H4, H2A, and H2B. Together with histone H1 and linker DNA, they further assemble into a higher-order chromatin structure. The nucleosome complex is stabilized by electrostatic interactions between positively charged histone residues and the negatively charged DNA backbone. To effectively access …

Reflections And Revisionism: Rethinking The U.S. Response To Covid-19, Wendy E. Parmet

Michigan Law Review

A review of In Covid’s Wake: How Our Politics Failed Us. By Stephen Macedo and Frances Lee.

Mathematical Models Of Multisensory Detection And Decision-Making, Rebecca M. Brady

Doctoral

The brain seamlessly integrates signals from multiple sensory modalities to interpret the world efficiently. By using information from various senses, the brain can enhance its ability to detect and respond to stimuli more quickly and accurately. However, combining sensory cues from multiple modalities is only sometimes beneficial as it may lead to illusions and reduced behavioural performance. Behavioural and electrophysiological experiments have revealed that detection and decision-making strategies for multisensory cues evolve throughout human development and ageing. Additionally, studies have demonstrated that maladaptive multisensory processing is a key indicator of a proclivity to falls in older adults and individuals with …

The Impact Of Economic Stability On Diagnostic Timing Of Autism Spectrum Disorder In Children, Leslie Box Simms

ETDs from 2020-2029

Diagnosis of neurodevelopmental delays in children, including autism spectrum disorder, is an often lengthy and burdensome process, potentially placing children with economic instability at risk for diagnostic delay. The first aim of this dissertation was to explore the impact of economic stability on diagnostic timing of autism spectrum disorder via a scoping review of the literature. Papers were summarized by study design, data sourcing, handling of variables, statistical methodology, and the effect of economic stability factors on diagnostic timing of autism. Income and healthcare insurance were the most frequently employed measures of economic stability, with transportation, housing, and food security …

Global Youth Development: Challenges And Remedies, Raymond Chimezie

Faculty Publications and Presentations

Young people have enormous potential for growth and innovation, but not all have access to resources and opportunities to explore, achieve their dreams, and contribute immensely to society. Absence or limited opportunities and resources pose great challenges to young people’s aspirations. Through a review of literature and study of some countries young people globally face challenges, though at varying degrees like unemployment, limited access to healthcare, social services, and education; lack of professional support, skills acquisition and retraining, and political exclusion; judicial inequity, loneliness, and poor parenting. There still exist policies, structures, or regulations that encourage racial and ethnic or …

Content Matters, Context Matters: Unraveling Behavior Dynamics In An Online Health Community For Tobacco Cessation, Tavleen Singh, Runzhi Zhou, Kayo Fujimoto, Sahiti Myneni

Faculty, Staff and Student Publications

Objectives: The objective of this research was to examine the content and context-specific information diffusion patterns underlying communication pertaining to tobacco use from online health communities (OHCs).

Materials and methods: We utilized a mixed-methods approach comprising multidimensional qualitative coding to identify themes and communication attributes, automated text analysis leveraging advances in large language models (LLMs) to classify message content and context, and social network analysis to examine the dynamics of peer interactions in this study. Using QuitNet, an online tobacco cessation forum (n = 64 632 members, n = 2.39 million forum messages spanning 2000-2015), we extracted message-level features …

Ai In Healthcare: Regulatory Guidelines And Judge-Made Negligence Principles For Ai Implementers, Gary K. Y. Chan

Research Collection Yong Pung How School Of Law

The use of artificial intelligence (AI) in healthcare may, notwithstanding its potential benefits, result in harm to patients from allegedly negligent acts or omissions by hospitals and medical doctors. In such circumstances, how should the principles in the tort of negligence (duty of care, breach, causation, remoteness of damage, and defences) respond to AI innovations in healthcare? In particular, how may the standard of care expected of hospitals and medical doctors be informed by regulatory guidelines? We refer to case law precedents and regulatory guidelines on the roles and responsibilities of doctors and hospitals as AI implementers. Importantly, they prompt …

Retrospective Analysis Of A Washu-Based Knowledge-Based Planning (Kbp) Model For Breast And Chest Wall Planning In Guatemala: Compliance With American Society For Radiation Oncology (Astro) 2026 Guidelines, Milton E Ixquiac Cabrera, Erick O Montenegro, Matthew Schmidt, Baozhou Sun, James A Kavanaugh, Taoran Li, Angel Velarde, Vicky De Falla, Francisco Reynoso

Faculty, Staff and Students Publications

Background/purpose

Breast cancer is among the most prevalent malignancies treated at the Liga Nacional Contra el Cáncer (LNCC) in Guatemala, representing a significant proportion of annual radiotherapy cases. Access to high-quality, standardized treatment planning in resource-constrained settings remains a critical challenge. This study evaluates the dosimetric performance of knowledge-based planning (KBP) models adapted from Washington University (WashU) in St. Louis for breast and chest wall radiotherapy at LNCC, validated against a retrospective 2025 clinical cohort, and benchmarked against the ASTRO 2026 Practical Radiation Oncology guidelines.

Materials and methods

A retrospective analysis of 84 treatment plans (40 left, 44 right) for …

Transient Yap Activation Uncovers The Neurogenic Potential Of Proliferative Mammalian Müller Glia, English J Laserna, Irina V Saltykova, Benjamin M Hall, Xuefei Tong, Justin S Dhindsa, Borna Sarker, Ayrea E Hurley, Paul G Swinton, William R Lagor, Nicholas M Tran, James F Martin, Ross A Poché

Faculty, Staff and Students Publications

The Hippo pathway effector YAP promotes spontaneous proliferation of Müller glia (MG), suggesting that bypassing Hippo signaling and activating YAP could enhance retinal regeneration. However, whether proliferative adult MGs retain meaningful neurogenic competence remains unclear. Here, using viral delivery of a Hippo-resistant YAP variant to wild-type adult MGs, we achieved transient YAP activation in adult MGs, inducing proliferation followed by cell-cycle withdrawal and differentiation. Intersectional genetic lineage tracing and EdU labeling, combined with transcriptomic analyses, revealed that YAP-activated MGs predominantly regenerate MGs, whereas only a subset gives rise to bipolar cell-like neurons. These results indicate that proliferative MGs acquire a …

Paid Employment And Ability To Work Among People Receiving Dialysis: A Systematic Review Of Qualitative Studies, Ao Zhang, Adam Martin, Karine Manera, Chandana Guha, Martin Howell, Patrizia Natale, Nicole Scholes-Robertson, Dharshana Sabanayagam, Adeera Levin, Wolfgang Winkelmayer, Kevin F Erickson, Germaine Wong, Allison Jaure, Anita Van Zwieten

Faculty, Staff and Students Publications

Rationale & objective: People receiving dialysis have reduced workforce participation, which can affect mental well-being and exacerbate the financial burden of dialysis. This study describes the experiences and perspectives of people receiving dialysis on employment and their ability to work.

Study design: Systematic review and thematic synthesis of qualitative studies.

Setting & study populations: Adults aged 16 years and over receiving dialysis.

Search strategy & sources: MEDLINE, Embase, and PsycINFO were searched to May 2025 for qualitative and mixed-methods studies that reported the perspectives of people receiving dialysis on employment or ability to work.

Data extraction: Text from results and …

Social Determinants As A Predictor For Heart Disease Mortality: Oregon Regional Study, Jewel Jones

University Honors Theses

The study examined the association between selected social determinants of health and heart disease mortality across geographic regions in Oregon. Using a regional analysis design, county-level population and mortality data were grouped into nine geographic regions to evaluate patterns of cardiovascular disease burden across the state. Social determinant variables were obtained from publicly available U.S. Census Bureau datasets and included poverty, crowding among households, single-parent households, lack of broadband internet subscription, housing cost burden, high school diploma attainment, and unemployment.

Heart disease mortality counts per 100,000 population were derived from official birth and death certificate records. Statistical analyses were conducted …

A Trans-Omics Gene-Smoking Interaction Study Of Lung Cancer Based On Consortium Data, Ning Xie, Xiaowen Xu, Yanru Wang, Aoxuan Wang, Xiang Wang, Xuan Wang, Mengsheng Zhao, Jiacheng Zhou, Yongyue Wei, Manel Esteller, Zhibin Hu, Hongbing Shen, Rayjean J Hung, Christopher I Amos, Yi Li, David C Christiani, Feng Chen, Yang Zhao, Ruyang Zhang

Faculty, Staff and Students Publications

Rationale: Genetically predicted molecular traits provide a cost-effective approach for identifying biomarkers and uncovering underlying biological mechanisms. We extended this framework to investigate gene-smoking interactions in lung cancer susceptibility.

Objectives: To identify trans-omics gene-smoking interactions affecting lung cancer risk and to assess how biomarkers modify effect of smoking.

Methods: We conducted the first trans-omics gene-smoking interaction study of lung cancer by integrating consortium-scale individual genotype data (27 737 cases vs 449 910 noncases) from the International Lung Cancer OncoArray Consortium (ILCCO-OncoArray), Transdisciplinary Research Into Cancer of the Lung (TRICL), Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), and the …

Climate-Driven Stochastic Modelling Of Crimean-Congo Haemorrhagic Fever Transmission In Uganda, P. G. Kpatchana, J. Aduda, K. M. Agboka

All Peer-Reviewed Publications

Crimean-Congo haemorrhagic fever (CCHF) is a climate-sensitive tick-borne zoonosis that remains a significant public health concern in Uganda, where temperature and vapour pressure deficit influence tick ecology and consequently disease transmission. This study aimed to develop and analyse a climate-driven stochastic model for CCHF transmission among ticks, livestock, and humans under environmental variability in Uganda. A compartmental transmission model was formulated and extended into a stochastic differential equation framework by incorporating multiplicative environmental noise. Climate-dependent tick recruitment, development, and mortality were parameterized using district-level temperature and vapour pressure deficit data to capture spatial heterogeneity in transmission risk. Theoretical analyses based …

Deadline-Chasing In Digital Health: Forecasting Emr Adoption Dynamics And Regulatory Impact In Indonesian Primary Care, Suryo Satrio, Bukhori Muhammad Aqid

Journal of Indonesian Health Policy and Administration

Indonesia is accelerating digital health adoption under Minister of Health Regulation No. 24/2022, which mandates the use and integration of Electronic Medical Records (EMRs) with SATUSEHAT. However, evidence on how quickly primary health care facilities (Fasilitas Kesehatan Tingkat Pertama – FKTP) are adopting EMRs and what the adoption curve looks like remains limited. Survey findings also suggest key barriers and enablers, including security and data migration concerns, as well as human resource and infrastructure constraints. This observational study used provider-network data from a single EMR vendor. Key measures included cumulative registered facilities, monthly registrations, same-month activation (total_active/monthly inflow) as …

Intervention Levers For Stunting Reduction In Indonesia: Evidence From The 2023 Indonesian Health Survey Data, Iwan Ariawan, Hafizah Jusril, Zahra Izza Afifa, Azka Fitri, Elmarizha Sekar Utami, Mikail Hasan, Dwi Puspasari

Kesmas

Stunting remains the largest public health challenge among macro-nutrition problems in Indonesia, affecting almost a quarter of children under five in 2023. The prevalence is considered high according to the World Health Organization standard. This study analyzed 15 aggregated provincial variables from the 2023 Indonesian Health Survey using Structural Equation Modeling (SEM), focusing on determinants of stunting among children under two to identify primary intervention levers. Findings indicated that intervention urgency should focus on the first 1,000 days, particularly the steep increase in stunting prevalence observed in the 12–24-month age range. While the highest prevalence is in Eastern provinces (e.g., …

Parameter Density Estimation For Cardiac Electrophysiology Models Using Data Consistent Deep Learning, Michael Luo

Dissertations

Mathematical models of biological rhythms and excitable systems can provide insights into mechanisms underlying cardiac electrical dynamics. However, estimating the parameters of these models from experimental observations is often difficult due to noise, heterogeneity, and unobserved variables. For example, in an electrocardiogram (ECG) recording, information about the electrical properties of different regions of the heart is compressed into a single voltage trace. Additionally, variability within these signals may contain important information about population heterogeneity, regional differences in electrophysiology, and time-dependent modulation.

This dissertation develops, explores, and evaluates methods that perform feature-based distributional inference for complex nonlinear dynamical systems. The objective …

Mri-Based Deep Learning Radiomics Model For Automated Classification Of Disc Degeneration In The Lumbar Spine, Shiv Patil, Om Gandhi, Mert Karabacak, Matthew Carr, Konstantinos Margetis

Student Papers, Posters & Projects

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging (MRI) is critical for clinical management and patient selection for spine surgery. This study aims to develop and evaluate machine learning (ML) models that combine features from deep learning (DL) and radiomics for the automated prediction of Pfirrmann grade (PG), a measure of disc degeneration, using multi-parametric lumbar spine MRI. Sagittal T1, T2, and T2 SPACE MRIs of 218 patients with LBP were acquired from the SPIDER dataset. For each intervertebral disc and available sequence, …

Self-Esteem Development And Emotional And Behavioral Disorders, Bahaa Al Ghraibeh

Archives of Assessment Psychology

Self-esteem is a fundamental psychological construct that develops across the lifespan and influences academic, social, emotional, and behavioral functioning. Research consistently links healthy self-esteem with positive developmental outcomes, including improved educational achievement, stronger interpersonal relationships, and better mental health. At the same time, emotional and behavioral disorders (EBD) are associated with persistent emotional, behavioral, academic, and social difficulties that can interfere with healthy self-development. This paper reviews the developmental trajectory of self-esteem from early childhood through adulthood and examines how the experiences commonly associated with EBD may negatively affect that trajectory. The review discusses how children begin forming self-esteem at …

Knowledge, Attitudes, And Practices Among Hepatitis B Patients In Jakarta, Indonesia: A Cross-Sectional Study, Hashem S. Arkok, Tri Yunis Miko Wahyono, Nurhayati Adnan Prihartono, Dipo Aldila

Kesmas

Hepatitis B remains a major public health problem in Indonesia. This study assessed the knowledge, attitudes, and practices (KAP) of hepatitis B patients in Jakarta to identify gaps in prevention and control and to explore relationships among KAP components. A cross-sectional study was conducted among 128 patients at a government hospital in South Jakarta, using a structured questionnaire that collected demographic information and assessed knowledge, attitudes, and practices regarding hepatitis B transmission and prevention. Despite moderate knowledge levels, major gaps remained. Only a small proportion (10.9%) correctly identified common symptoms, transmission routes (10.2%), and possible complications (18.8%). Misconceptions about transmission …

Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee

Faculty Publications

Background: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. Methods: Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient’s history. Hyperparameter-tuned …

Panda-Plus-Bench: A Clinical Benchmark For Evaluating The Robustness Of Ai Foundation Models In Prostate Cancer Diagnosis, Joshua L. Ebbert, Dennis Della Corte

Faculty Publications

Artificial intelligence foundation models are increasingly deployed for prostate cancer Gleason grading, where GP3/GP4 distinction directly impacts treatment decisions (active surveillance vs. intervention). However, these models may achieve high validation accuracy by learning specimen-specific artifacts rather than generalizable biological features, limiting real-world clinical utility. We introduce PANDA-PLUS-Bench, a curated benchmark dataset derived from expertly annotated prostate biopsies designed specifically to quantify this failure mode. The benchmark comprises nine carefully selected whole slide images from nine unique patients containing diverse Gleason patterns, with non-overlapping tissue patches extracted at both 512 × 512 and 224 × 224-pixel resolutions across eight augmentation conditions. …

Modeling The Impact Of Treatment During An Ongoing Tuberculosis Outbreak, Bach Le, Anna Robicheaux, Jude Shive, Alana Wells, Erin N. Bodine

Spora: A Journal of Biomathematics

In response to a significant tuberculosis outbreak in Wyandotte and Johnson Counties, Kansas, this study presents a compartmental model of ordinary differential equations to evaluate the impact of standard antibiotic treatment. The model incorporates latent, active, and treated disease states. Parameter values were informed by epidemiological data and uncertain parameter value ranges were explored systematically through uncertainty analysis using constrained Latin hypercube sampling. Cumulative infections and deaths, and the basic reproduction number, were computed over a five-year simulation period. Sensitivity analyses using partial rank correlation coefficients identified symptomatic treatment rate and transmission rate as primary drivers of cumulative infections and …

Predicting The Outcome Of Ischemic Hepatitis With Real-Patient Data Using Machine Learning Tools, Christiana Beard, Madison Utterback, Olcay Akman, Priya Kohli, William M. Lee, Aditi Ghosh

Spora: A Journal of Biomathematics

Ischemic hepatitis (IH) results from shock-related conditions that impair oxygenated blood flow to the liver, causing hepatocyte death. Diagnosis relies largely on clinical history due to the absence of specific diagnostic tests and limited ability to predict outcomes. This study applies machine learning methods to real-world IH patient data to improve outcome prediction. Biomedical indicators analyzed include creatinine, international normalized ratio (INR), aspartate aminotransferase (AST), alanine transaminase (ALT), and bilirubin. Data were collected from multiple U.S. centers through the Acute Liver Failure Study Group (ALFSG), a multicenter network focused on this rare condition. We implemented logistic regression, regression tree methods …

Hepatoprotective Evaluation Of Benzoylated Emodin Derivatives: Integrating Bioinformatics And In Vitro Studies, Firdayani Firdayani, Putri Hawa Syaifie, Siska Andrina Kusumastuti, Muthia Rahayu Iresha, Wirawan Adikusuma, Dhecella Winy Cintya Ningrum, Etik Mardliyati, Ayu Masyita, Ariza Yandwiputra Besari, Fathir Azzaki Iradata, Lucy Arianie

BioMedicine

Background: Emodin exhibits various pharmacological activities, including hepatoprotective effects. However, its clinical application is limited by poor absorption and low oral bioavailability. This study aimed to optimize emodin through benzoylation and to assess the hepatoprotective potential of the resulting derivatives integrating bioinformatics and in vitro methods.

Methods: We conducted network pharmacology, molecular docking and molecular dynamics (MD) simulations to predict potential targets and interactions of benzoylated emodin derivatives. The derivatives were synthesized and evaluated for cytotoxicity using the MTT assay. The hepatoprotective effects were assessed in vitro using a paracetamol-induced HepG2 cell injury model.

Results: Network pharmacology analysis and gene …

Epileptic Seizure Prediction From Eeg Using Continual Learning With Cnns, Adnan Amin, Ammar Bathich, Feras Al-Obeidat, Safa Naes, Maria Jose Sousa

All Works

Epilepsy is a persistent neurological disorder that affects over 50 million people worldwide, with nearly one-third of patients remaining unresponsive to conventional therapeutic treatments. This study introduces a progressively adaptive seizure prediction framework designed to enhance early detection and clinical decision-making. The proposed model employs a deep learning strategy grounded in continual learning (CL) principles, using Convolutional Neural Networks (CNNs) in combination with knowledge distillation techniques. This enables the model to assimilate new data while retaining previously learned information. The approach was evaluated on the publicly available Bonn University EEG dataset, following a sequential learning process in which each successive …

Bridging Images And Language In Radiology: A Comprehensive Prisma Systematic Review Of Transformer Vision-Language Models And Clinical Readiness, Mohammad T. Khasawneh Dr., Sadaf Tabatabaee

Systems Science and Industrial Engineering Student Scholarship

Transformer vision-language models (VLMs) promise end-to-end automation of radiology reporting and related multimodal tasks. However, evidence remains fragmented across datasets, architectures, evaluation practices, and levels of clinical validation, limiting fair comparison and safe translation into practice. Following PRISMA 2020/PRISMA-S, search engines including PubMed, IEEE Xplore, Web of Science, and Google Scholar were systematically searched for peer-reviewed, English-language studies published between 2019 and 2025 that used paired radiology images and free-text reports. Dual reviewers screened records and extracted data using a locked schema covering datasets, modalities, architectures, training objectives, evaluation metrics, and indicators of clinical readiness. Free-text model descriptions were normalized …

Deep Cuts: Bearing Witness To The Erosion Of Evidence, Maya Lakshmi Srinivasan Md

Masters Theses

In my Master of Fine Arts thesis in Printmaking at the Rhode Island School of Design, I examine the intersection of my dual practices as a general surgery resident and artist. Both disciplines require precision, incision, and tactility to navigate the human condition. My art practice is a method of processing the emotional toll of surgical training and the simultaneous societal erosion of scientific authority. My thesis work focuses on challenging the rise of medical pseudoscience and anti-intellectualism. The focal piece in this installation, In Tallow We Trust, consists of three woodblock relief prints at the scale of an …