Vignesh Subbian

Interests

Research

Medical Informatics, Healthcare Systems Engineering, Acute Respiratory Failure, Traumatic Brain Injury, Engineering Ethics, Asset-based Practices

Teaching

Software Engineering, Biomedical Data Science and Informatics

Courses

Scholarly Contributions

Chapters

• Subbian, V., Galvin, H. K., Petersen, C., & Solomonides, A. (2021). Ethical, Legal, and Social Issues (ELSI) in Mental Health Informatics. In Mental Health Informatics(pp 479--503). Springer.
• Franco, M., Lozano, G. I., & Subbian, V. (2020). HSIs and Community Partners: A Framework for Strengthening Servingness Through Engagement. In Hispanic Serving Institutions (HSIs) in Practice: Defining "Servingness" at HSIs(pp 153-174). Information Age Publishing.

Journals/Publications

• Fisher, J. M., Subbian, V., Essay, P., Pungitore, S., Bedrick, E. J., & Mosier, J. M. (2024). Acute Respiratory Failure From Early Pandemic COVID-19: Noninvasive Respiratory Support vs Mechanical Ventilation. CHEST critical care, 2(1).
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  The optimal strategy for initial respiratory support in patients with respiratory failure associated with COVID-19 is unclear, and the initial strategy may affect outcomes.
• Stowell, J. R., Henry, M. B., Pugsley, P., Edwards, J., Burton, H., Norquist, C., Katz, E. D., Koenig, B. W., Indermuhle, S., Subbian, V., Ghaderi, H., & Akhter, M. (2024). Impact of the COVID-19 Pandemic on Emergency Department Encounters in a Major Metropolitan Area. The Journal of emergency medicine, 66(3), e383-e390.
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  The end of 2019 marked the emergence of the COVID-19 pandemic. Public avoidance of health care facilities, including the emergency department (ED), has been noted during prior pandemics.
• Essay, P., Zhang, T., Mosier, J., & Subbian, V. (2023). Managed critical care: impact of remote decision-making on patient outcomes. The American journal of managed care, 29(7), e208-e214.
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  Tele-intensive care unit (tele-ICU) use has become increasingly common as an extension of bedside care for critically ill patients. The objective of this work was to illustrate the degree of tele-ICU involvement in critical care processes and evaluate the impact of tele-ICU decision-making authority.
• Ghaderi, H., Foreman, B., Nayebi, A., Tipirneni, S., Reddy, C. K., & Subbian, V. (2023). A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping. Journal of biomedical informatics, 143, 104401.
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  Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Clinical data associated with TBI patients are often measured over time and represented as time-series variables characterized by missing values and irregular time intervals. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.
• Ghaderi, H., Foreman, B., Nayebi, A., Tipirneni, S., Reddy, C. K., & Subbian, V. (2023). Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023, 379-388.
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  Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.
• Lehmann, C. U., Subbian, V., & , S. E. (2023). Advances in Clinical Decision Support Systems: Contributions from the 2022 Literature. Yearbook of medical informatics, 32(1), 179-183.
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  To summarize significant research contributions published in 2022 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2023.
• Nayebi, A., Tipirneni, S., Reddy, C. K., Foreman, B., & Subbian, V. (2023). WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values. Journal of biomedical informatics, 144, 104438.
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  Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80 % compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable explanations with higher quality.
• Pungitore, S., & Subbian, V. (2023). Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of healthcare informatics research, 7(3), 313-331.
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  Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks. We searched PubMed® and IEEE Xplore® databases for studies from January 1, 2010 applying machine learning models to EHR data for patient outcome prediction. Publications applying time-series methods were selected for further review. We identified 92 studies and summarized them by clinical context and definition and reporting of the prediction problem. For the time windows of interest, 12 studies did not discuss window lengths, 57 used a single set of window lengths, and 23 evaluated the relationship between window length and model performance. We also found that 72 studies had appropriate reporting of the prediction task. However, evaluation of prediction problem formulation for temporal EHR data was complicated by heterogeneity in assessing and reporting of these concepts. Even among studies modeling similar clinical outcomes, there were variations in terminology used to describe the prediction problem, rationale for window lengths, and determination of the outcome of interest. As temporal modeling using EHR data expands, minimal reporting standards should include time-series specific concerns to promote rigor and reproducibility in future studies and facilitate model implementation in clinical settings.
• Pungitore, S., Olorunnisola, T., Mosier, J., Subbian, V., & , N. C. (2023). Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023, 589-598.
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  Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PASC based on symptomatology; 2) generalizable and reproducible phenotyping heuristics and meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom duration. In this study, we defined computable phenotypes (or heuristics) and meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom duration. We also developed a symptom profile for PASC based on a common data standard. We identified four phenotypes based on COVID-19 severity (mild vs. moderate/severe) and duration of PASC symptoms (subacute vs. chronic). The symptoms groups with the highest frequency among phenotypes were cardiovascular and neuropsychiatric with each phenotype characterized by a different set of symptoms.
• Stocking, J. C., Taylor, S. L., Fan, S., Wingert, T., Drake, C., Aldrich, J. M., Ong, M. K., Amin, A. N., Marmor, R. A., Godat, L., Cannesson, M., Gropper, M. A., Utter, G. H., Sandrock, C. E., Bime, C., Mosier, J., Subbian, V., Adams, J. Y., Kenyon, N. J., , Albertson, T. E., et al. (2023). A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population. CHEST critical care, 1(3).
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  Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.
• Zhang, T., Gephart, S. M., Subbian, V., Boyce, R. D., Villa-Zapata, L., Tan, M. S., Horn, J., Gomez-Lumbreras, A., Romero, A. V., & Malone, D. C. (2023). Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support. Applied clinical informatics, 14(4), 779-788.
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  Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts.
• Alonso, A., Alam, A. B., Kamel, H., Subbian, V., Qian, J., Boerwinkle, E., Cicek, M., Clark, C. R., Cohn, E. G., Gebo, K. A., Loperena-Cortes, R., Mayo, K. R., Mockrin, S., Ohno-Machado, L., Schully, S. D., Ramirez, A. H., & Greenland, P. (2022). Epidemiology of atrial fibrillation in the All of Us Research Program. PloS One, 17(3), e0265498.
• Ardalan, Z., & Subbian, V. (2022). Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review. Frontiers in artificial intelligence, 5, 780405.
• Essay, P. T., Mosier, J. M., Nayebi, A., Fisher, J. M., & Subbian, V. (2022). Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning. Respiratory Care.
• Essay, P., Fisher, J. M., Mosier, J. M., & Subbian, V. (2022). Validation of an Electronic Phenotyping Algorithm for Patients With Acute Respiratory Failure. Critical care explorations, 4(3), e0645.
• Glowalla, G., & Subbian, V. (2022). Data Sharing Between Jail and Community Health Systems: Missing Links and Lessons for Re-Entry Success. Studies in health technology and informatics, 290, 47-51.
• Griffin, A. C., He, L., Sunjaya, A. P., King, A. J., Khan, Z., Nwadiugwu, M., Douthit, B., Subbian, V., Nguyen, V., Braunstein, M., Jaffe, C., & Schleyer, T. (2022). Clinical, technical, and implementation characteristics of real-world health applications using FHIR. JAMIA open, 5(4), ooac077.
• Hansten, P. D., Tan, M. S., Horn, J. R., Gomez-Lumbreras, A., Villa-Zapata, L., Boyce, R. D., Subbian, V., Romero, A., Gephart, S., & Malone, D. C. (2022). Colchicine Drug Interaction Errors and Misunderstandings: Recommendations for Improved Evidence-Based Management. Drug Safety, 1-20.
• Kostka, K., Duarte-Salles, T., Prats-Uribe, A., Sena, A. G., Pistillo, A., Khalid, S., Lai, L. Y., Golozar, A., Alshammari, T. M., Dawoud, D. M., Nyberg, F., Wilcox, A. B., Andryc, A., Williams, A., Ostropolets, A., Areia, C., Jung, C. Y., Harle, C. A., Reich, C. G., , Blacketer, C., et al. (2022). Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS. Clinical epidemiology, 14, 369-384.
• Lehmann, C. U., Fultz Hollis, K., Petersen, C., DeMuro, P. R., Subbian, V., Koppel, R., Solomonides, A. E., Berner, E. S., Pan, E. C., Adler-Milstein, J., & Goodman, K. W. (2022). Selecting venues for AMIA events and conferences: guiding ethical principles. Journal of the American Medical Informatics Association : JAMIA, 29(8), 1319-1322.
• Nayebi, A., Tipirneni, S., Foreman, B., Reddy, C. K., & Subbian, V. (2022). An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022, 815-824.
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  A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reasons behind their decisions in ways that are interpretable and understandable to human users. . Numerous XAI approaches have been developed thus far, and a comparative analysis of these strategies seems necessary to discern their relevance to clinical prediction models. To this end, we first implemented two prediction models for short- and long-term outcomes of traumatic brain injury (TBI) utilizing structured tabular as well as time-series physiologic data, respectively. Six different interpretation techniques were used to describe both prediction models at the local and global levels. We then performed a critical analysis of merits and drawbacks of each strategy, highlighting the implications for researchers who are interested in applying these methodologies. The implemented methods were compared to one another in terms of several XAI characteristics such as understandability, fidelity, and stability. Our findings show that SHAP is the most stable with the highest fidelity but falls short of understandability. Anchors, on the other hand, is the most understandable approach, but it is only applicable to tabular data and not time series data.
• Petersen, C., Berner, E. S., Cardillo, A., Fultz Hollis, K., Goodman, K. W., Koppel, R., Korngiebel, D. M., Lehmann, C. U., Solomonides, A. E., & Subbian, V. (2022). AMIA's code of professional and ethical conduct 2022. Journal of the American Medical Informatics Association : JAMIA, 30(1), 3-7.
• Villa Zapata, L., Boyce, R. D., Chou, E., Hansten, P. D., Horn, J. R., Gephart, S. M., Subbian, V., Romero, A., & Malone, D. C. (2022). QTc Prolongation with the Use of Hydroxychloroquine and Concomitant Arrhythmogenic Medications: A Retrospective Study Using Electronic Health Records Data. Drugs - Real World Outcomes, 9(3), 415-423.
• Villa Zapata, L., Subbian, V., Boyce, R. D., Hansten, P. D., Horn, J. R., Gephart, S. M., Romero, A., & Malone, D. C. (2022). Overriding Drug-Drug Interaction Alerts in Clinical Decision Support Systems: A Scoping Review. Studies in health technology and informatics, 290, 380-384.
• Alper, B. S., Dehnbostel, J., Afzal, M., Subbian, V., Soares, A., Kunnamo, I., Shahin, K., McClure, R. C., & , C. K. (2021). Making science computable: Developing code systems for statistics, study design, and risk of bias. Journal of biomedical informatics, 115, 103685.
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  The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcoming the interoperability problems in health information exchange was extended to evidence-based medicine (EBM) knowledge with the EBMonFHIR project. A 13-step Code System Development Protocol was created in September 2020 to support global development of terminologies for exchange of scientific evidence. For Step 1, we assembled expert working groups with 55 people from 26 countries by October 2020. For Step 2, we identified 23 commonly used tools and systems for which the first version of code systems will be developed. For Step 3, a total of 368 non-redundant concepts were drafted to become display terms for four code systems (Statistic Type, Statistic Model, Study Design, Risk of Bias). Steps 4 through 13 will guide ongoing development and maintenance of these terminologies for scientific exchange. When completed, the code systems will facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction. We hope the achievements reached thus far will outlive COVID-19 and provide an infrastructure to make science computable for future generations. Anyone may join the effort at https://www.gps.health/covid19_knowledge_accelerator.html.
• Chou, E., Boyce, R. D., Balkan, B., Subbian, V., Romero, A., Hansten, P. D., Horn, J. R., Gephart, S., & Malone, D. C. (2021). Designing and Evaluating Contextualized Drug-Drug Interaction Algorithms. Journal of the American Medical Informatics Association (JAMIA) Open, 4(1), ooab023.
• Ehsani, S., Reddy, C. K., Foreman, B., Ratcliff, J., & Subbian, V. (2021). Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial. JMIR Biomedical Engineering, 6(1), e24698. doi:10.2196/24698
• Essay, P., Mosier, J., & Subbian, V. (2021). Phenotyping COVID-19 Patients by Ventilation Therapy: Data Quality Challenges and Cohort Characterization. Studies in health technology and informatics, 281, 198-202.
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  The COVID-19 pandemic introduced unique challenges for treating acute respiratory failure patients and highlighted the need for reliable phenotyping of patients using retrospective electronic health record data. In this study, we applied a rule-based phenotyping algorithm to classify COVID-19 patients requiring ventilatory support. We analyzed patient outcomes of the different phenotypes based on type and sequence of ventilation therapy. Invasive mechanical ventilation, noninvasive positive pressure ventilation, and high flow nasal insufflation were three therapies used to phenotype patients leading to a total of seven subgroups; patients treated with a single therapy (3), patients treated with either form of noninvasive ventilation and subsequently requiring intubation (2), and patients initially intubated and then weaned onto a noninvasive therapy (2). In addition to summary statistics for each phenotype, we highlight data quality challenges and importance of mapping to standard terminologies. This work illustrates potential impact of accurate phenotyping on patient-level and system-level outcomes including appropriate resource allocation under resource constrained circumstances.
• Ghaderi, H., Stowell, J. R., Akhter, M., Norquist, C., Pugsley, P., & Subbian, V. (2021). Impact of COVID-19 Pandemic on Emergency Department Visits: A Regional Case Study of Informatics Challenges and Opportunities. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021, 496-505.
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  In this paper, we examined informatics challenges and opportunities related to emergency department visit data during public health emergencies. We investigated the impact of COVID-19 pandemic on the volume and acuity of adult patients visiting the emergency department (ED) of a medical center in Arizona during the pandemic compared to the pre-pandemic period. We performed a negative binomial regression analysis to understand how different public health-related mandates and statewide business opening/closing orders in Arizona affected the daily emergency department visits. The results of this study show that the average daily ED visits decreased by 20% during the COVID-19 pandemic in comparison with the same period in 2019. In addition, the business closure order had the most impact on emergency department visits in comparison to other public health mandates.
• Giangreco, N. P., Lina, S., Qian, J., Kuoame, A., Subbian, V., Boerwinkle, E., Cicek, M., Clark, C. R., Cohen, E., Gebo, K. A., Loperena-Cortes, R., Mayo, K., Mockrin, S., Ohno-Machado, L., Schully, S. D., Tatonetti, N. P., & Ramirez, A. H. (2021). Pediatric data from the research program: demonstration of pediatric obesity over time. JAMIA open, 4(4), ooab112.
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  To describe and demonstrate use of pediatric data collected by the Research Program.
• Gutruf, P., Utzinger, U., & Subbian, V. (2021). Moving from Pedagogy to Andragogy in Biomedical Engineering Design: Strategies for Lab-at-Home and Distance Learning. Biomedical engineering education, 1(2), 301-305.
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  Engineering design courses are particularly challenging to deliver in online or distance modalities because of the hands-on, collaborative nature of the design process and the need for physical resources and work spaces. In this work, we describe how we rapidly transformed two design courses in the middle two years of the biomedical engineering (BME) program to an online format during the 2019 coronavirus pandemic. In addition to time and safety constraints, we identified access to design spaces with biochemistry, computing, electronic, computing, and manufacturing tools, and team-based learning as major challenges to distance learning in BME design courses. To this end, we mapped and translated various course and design activities to an online environment using a combination of customized at-home laboratory kits and distributed team structures. Drawing upon our pilot experience as well as principles from online and adult learning theories, we offer an overview of strategies to retain hands-on and team-based activities and rapidly implement BME design courses in online or distance modalities.
• Lane, J. C., Weaver, J., Kostka, K., Duarte-Salles, T., Abrahao, M. T., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Biedermann, P., Banda, J. M., Burn, E., Casajust, P., Fister, K., Hardin, J., Hester, L., Hripcsak, G., Kaas-Hansen, B. S., Khosla, S., , Kolovos, S., et al. (2021). Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study. Rheumatology (Oxford, England), 60(7), 3222-3234.
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  Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.
• Nayebi, A., Tipirneni, S., Foreman, B., Ratcliff, J., Reddy, C. K., & Subbian, V. (2021). Recurrent Neural Network based Time-Series Modeling for Long-term Prognosis Following Acute Traumatic Brain Injury. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021, 900-909.
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  We developed a prognostic model for longer-term outcome prediction in traumatic brain injury (TBI) using an attention-based recurrent neural network (RNN). The model was trained on admission and time series data obtained from a multi-site, longitudinal, observational study of TBI patients. We included 110 clinical variables as model input and Glasgow Outcome Score Extended (GOSE) at six months after injury as the outcome variable. Designed to handle missing values in time series data, the RNN model was compared to an existing TBI prognostic model using 10-fold cross validation. The area under receiver operating characteristic curve (AUC) for the RNN model is 0.86 (95% CI 0.83-0.89) for binary outcomes, whereas the AUC of the comparison model is 0.69 (95% CI 0.67-0.71). We demonstrated that including time series data into prognostic models for TBI can boost the discriminative ability of prediction models with either binary or ordinal outcomes.
• Prats-Uribe, A., Sena, A. G., Lai, L. Y., Ahmed, W. U., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Carter, W., Casajust, P., Dawoud, D., Golozar, A., Jonnagaddala, J., Mehta, P. P., Gong, M., Morales, D. R., Nyberg, F., Posada, J. D., Recalde, M., , Roel, E., et al. (2021). Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study. BMJ (Clinical research ed.), 373, n1038.
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  To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents.
• Recalde, M., Roel, E., Pistillo, A., Sena, A. G., Prats-Uribe, A., Ahmed, W. U., Alghoul, H., Alshammari, T. M., Alser, O., Areia, C., Burn, E., Casajust, P., Dawoud, D., DuVall, S. L., Falconer, T., Fernández-Bertolín, S., Golozar, A., Gong, M., Lai, L. Y., , Lane, J. C., et al. (2021). Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom. International Journal of Obesity, 45(11), 2347-2357.
• Roel, E., Pistillo, A., Recalde, M., Sena, A. G., Fernández-Bertolín, S., Aragón, M., Puente, D., Ahmed, W. U., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Blacketer, C., Carter, W., Casajust, P., Culhane, A. C., Dawoud, D., DeFalco, F., DuVall, S. L., , Falconer, T., et al. (2021). Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain. Cancer epidemiology, biomarkers & prevention, 30(10), 1884-1894.
• Tan, E. H., Sena, A. G., Prats-Uribe, A., You, S. C., Ahmed, W. U., Kostka, K., Reich, C., Duvall, S. L., Lynch, K. E., Matheny, M. E., Duarte-Salles, T., Bertolin, S. F., Hripcsak, G., Natarajan, K., Falconer, T., Spotnitz, M., Ostropolets, A., Blacketer, C., Alshammari, T. M., , Alghoul, H., et al. (2021). COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries. Rheumatology (Oxford, England), 60(SI), SI37-SI50.
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  Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.
• Tolentino, D. A., Subbian, V., & Gephart, S. M. (2021). Applying Computational Ethnography to Examine Nurses' Workflow Within Electronic Health Records. Nursing research, 70(2), 132-141.
• Villa-Zapata, L., Carhart, B. S., Horn, J. R., Hansten, P. D., Subbian, V., Gephart, S., Tan, M., Romero, A., & Malone, D. C. (2021). Serum potassium changes due to concomitant ACEI/ARB and spironolactone therapy: A systematic review and meta-analysis. American Journal of Health-system Pharmacy, 78(24), 2245-2255.
• Wong, A. A., Marrone, N. L., Fabiano-Smith, L., Beeson, P. M., Franco, M. A., Subbian, V., & Lozano, G. I. (2021). Engaging Faculty in Shifting Toward Holistic Review: Changing Graduate Admissions Procedures at a Land-Grant, Hispanic-Serving Institution. American Journal of Speech-Language Pathology, 30(5), 1925-1939.
• Zhang, T., Mosier, J., & Subbian, V. (2021). Identifying Barriers to and Opportunities for Telehealth Implementation Amidst the COVID-19 Pandemic by Using a Human Factors Approach: A Leap Into the Future of Health Care Delivery?. JMIR human factors, 8(2), e24860.
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  The extensive uptake of telehealth has considerably transformed health care delivery since the beginning of the COVID-19 pandemic and has imposed tremendous challenges to its large-scale implementation and adaptation. Given the shift in paradigm from telehealth as an alternative mechanism of care delivery to telehealth as an integral part of the health system, it is imperative to take a systematic approach to identifying barriers to, opportunities for, and the overall impact of telehealth implementation amidst the current pandemic. In this work, we apply a human factors framework, the Systems Engineering Initiative for Patient Safety model, to guide our holistic analysis and discussion of telehealth implementation, encompassing the health care work system, care processes, and outcomes.
• Alper, B. S., Richardson, J. E., Lehmann, H. P., & Subbian, V. (2020). It is time for computable evidence synthesis: The COVID-19 Knowledge Accelerator initiative. Journal of the American Medical Informatics Association : JAMIA, 27(8), 1338-1339.
• Essay, P., Balkan, B., & Subbian, V. (2020). Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset. JMIR Medical Informatics, 8(8), e19892.
• Essay, P., Mosier, J., & Subbian, V. (2020). Rule-Based Cohort Definitions for Acute Respiratory Failure: Electronic Phenotyping Algorithm. JMIR Medical Informatics, 8(4), e18402. doi:https://doi.org/10.2196/18402
• Gutruf, P., Utzinger, U., & Subbian, V. (2020). Moving from Pedagogy to Andragogy in Biomedical Engineering Design: Strategies for Lab-at-Home and Distance Learning. Biomedical Engineering Education.
• Kim, A. A., Rachid Zaim, S., & Subbian, V. (2020). Assessing reproducibility and veracity across machine learning techniques in biomedicine: A case study using TCGA data. International Journal of Medical Informatics, 141, 104148. doi:https://doi.org/10.1016/j.ijmedinf.2020.104148
• Kuziemsky, C. E., Hunter, I., Gogia, S. B., Lyenger, S., Kulatunga, G., Rajput, V., Subbian, V., John, O., Kleber, A., Mandirola, H. F., Florez-Arango, J., Al-Shorbaji, N., Meher, S., Udayasankaran, J. G., & Basu, A. (2020). Ethics in Telehealth: Comparison between Guidelines and Practice-based Experience - The Case for Learning Health Systems. Yearbook of Medical Informatics, 29(1), 44-50.
• Lane, J., Weaver, J., Kostka, K., Duarte-Salles, T., Abrahao, M., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Biedermann, P., Banda, J. M., Burn, E., Casajust, P., Fister, K., Hardin, J., Hester, L., Hripcsak, G., Kaas-Hansen, B. S., Khosla, S., , Kolovos, S., et al. (2020). Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study. Rheumatology, keaa771.
• Miller, D. C., Beamer, P., Billheimer, D., Subbian, V., Sorooshian, A., Campbell, B. S., & Mosier, J. M. (2020). Aerosol Risk with Noninvasive Respiratory Support in Patients with COVID-19. Journal of the American College of Emergency Physicians Open. doi:https://doi.org/10.1002/emp2.12152
• Petersen, C., & Subbian, V. (2020). Special Section on Ethics in Health Informatics. Yearbook of Medical Informatics, 29(1), 77-80.
• Subbian, V., Solomonides, A., Clarkson, M., Rahimzadeh, V. N., Petersen, C., Schreiber, R., DeMuro, P. R., Dua, P., Goodman, K. W., Kaplan, B., Koppel, R., Lehmann, C. U., Pan, E., & Senathirajah, Y. (2021). Ethics and Informatics in the Age of COVID-19: Challenges and Recommendations for Public Health Organization and Public Policy. Journal of the American Medical Informatics Association (JAMIA), 28(1). doi:https://doi.org/10.1093/jamia/ocaa188
• Villa Zapata, L., Hansten, P. D., Horn, J. R., Boyce, R. D., Gephart, S., Subbian, V., Romero, A., & Malone, D. C. (2020). Evidence of Clinically Meaningful Drug-Drug Interaction with Concomitant Use of Colchicine and Clarithromycin. Drug Safety, 43, 661–668. doi:https://doi.org/10.1007/s40264-020-00930-7
• Villa Zapata, L., Hansten, P. D., Panic, J., Horn, J. R., Boyce, R. D., Gephart, S., Subbian, V., Romero, A., & Malone, D. C. (2020). Risk of Bleeding with Exposure to Warfarin and Nonsteroidal Anti-Inflammatory Drugs: A Systematic Review and Meta-Analysis. Thrombosis and haemostasis, 120(7), 1066-1074.
• Essay, P., Shahin, T. B., Balkan, B., Mosier, J., & Subbian, V. (2019). The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database. JMIR Medical Informatics, 7(1), e13006.
• Galvin, H. K., Petersen, C., Subbian, V., & Solomonides, A. (2019). Patients as Agents in Behavioral Health Research and Service Provision: Recommendations to Support the Learning Health System. Applied Clinical Informatics, 10(5), 841-848. doi:https://doi.org/10.1055/s-0039-1700536
• Ortiz, J. B., Sukhina, A., Balkan, B., Harootunian, G., Adelson, P. D., Lewis, K. S., Oatman, O., Subbian, V., Rowe, R. K., & Lifshitz, J. (2020). Epidemiology of Pediatric Traumatic Brain Injury and Hypothalamic-Pituitary Disorders in Arizona. Frontiers in Neurology, 10, 1410. doi:https://dx.doi.org/10.3389%2Ffneur.2019.01410
• Walsh, C. G., Chaudhry, B., Dua, P., Goodman, K. W., Kaplan, B., Kavuluru, R., Solomonides, A., & Subbian, V. (2020). Stigma, Biomarkers, and Algorithmic Bias: Recommendations for Precision Behavioral Health with Artificial Intelligence. JAMIA Open.
• Petersen, C., Berner, E. S., Embi, P. J., Fultz, H. K., Goodman, K. W., Koppel, R., Lehmann, C. U., Lehmann, H., Maulden, S. A., McGregor, K. A., Solomonides, A., Subbian, V., Terrazas, E., & Winkelstein, P. (2018). AMIA's Code of Professional and Ethical Conduct 2018. Journal of the American Medical Informatics Association, 25(11), 1579-1582.
• Rowe, R. K., Harrison, J. L., Morrison, H., Subbian, V., Murphy, S. M., & Lifshitz, J. (2018). Acute Post-traumatic Sleep may define Vulnerability to a Second Traumatic Brain Injury in Mice. Journal of Neurotrauma.
• Schmitz, H., Howe, C. L., Armstrong, D. G., & Subbian, V. (2018). Leveraging Mobile Health Applications for Biomedical Research and Citizen Science: A Scoping Review. Journal of the American Medical Informatics Association, 25(12), 1685-1695.
• Shahin, T. B., Vaishnav, K. V., Watchman, M., Subbian, V., Larson, E., Chnari, E., & Armstrong, D. G. (2017). Tissue Augmentation with Allograft Adipose Matrix For the Diabetic Foot in Remission. Plastic and reconstructive surgery. Global open, 5(10), e1555.
• Tenenbaum, J. D., Bhuvaneshwar, K., Gagliardi, J. P., Fultz Hollis, K., Jia, P., Ma, L., Nagarajan, R., Rakesh, G., Subbian, V., Visweswaran, S., Zhao, Z., & Rozenblit, L. (2017). Translational bioinformatics in mental health: open access data sources and computational biomarker discovery. Briefings in bioinformatics.
• Subbian, V., Ratcliff, J. J., Korfhagen, J. J., Hart, K. W., Meunier, J. M., Shaw, G. J., Lindsell, C. J., & Beyette, F. R. (2016). A Novel Tool for Evaluation of Mild Traumatic Brain Injury Patients in the Emergency Department: Does Robotic Assessment of Neuromotor Performance Following Injury Predict the Presence of Postconcussion Symptoms at Follow‐up?. Academic Emergency Medicine, 23(4), 382-392.
• Duran, C., Subbian, V., Giovanetti, M. T., Simkins, J. R., & Beyette Jr, F. (2015). Experimental desktop 3D printing using dual extrusion and water-soluble polyvinyl alcohol. Rapid Prototyping Journal, 21(5), 528-534.
• Subbian, V., Ratcliff, J. J., Meunier, J. M., Korfhagen, J. J., Beyette, F. R., & Shaw, G. J. (2015). Integration of new technology for research in the emergency department: feasibility of deploying a robotic assessment tool for mild traumatic brain injury evaluation. IEEE journal of translational engineering in health and medicine, 3, 1-9.

Proceedings Publications

• Budinoff, H., Subbian, V., & Lopez, F. (2022). Integrating Asset-based Practices into Engineering Design Instruction. In 2022 ASEE Annual Conference & Exposition.
• Subbian, V., Shaw, L., & Halpin, C. (2022). Ethical Decision-Making Frameworks for Engineering Education: A Cross-Disciplinary Review. In 2022 ASEE Annual Conference & Exposition.
• Budinoff, H. D., & Subbian, V. (2021). Asset-based Approaches to Engineering Design Education: A Scoping Review of Theory and Practice. In 2021 ASEE Annual Conference.
• Subbian, V., Franco, M., & Lozano, G. (2019, 6). STEM Servingness at Hispanic-Serving Institutions. In ASEE Annual Conference & Exposition.
• Alvarez, A. M., Johnson, P. C., Shaw, L. R., Zawada, S., Franco, M. A., & Subbian, V. (2018, April). Beyond Ramps and Signs: Rethinking Support Structures for Engineering Students with Disabilities. In ASEE Collaborative Network for Engineering and Computing Diversity Conference.
• Lozano, G. I., Franco, M. A., & Subbian, V. (2018, April). Transforming STEM Education in Hispanic Serving Institutions in the United States: A Consensus Report. In Available at SSRN: https://ssrn.com/abstract=3238702.
• Vezino, B., Alvarez, A. M., Hempel, B., Loera, C. J., Davidson, S., Boyd, S., & Subbian, V. (2018, June). Re-envisioning the Role of the Engineering Education Chapter at a Research-I Institution: Lessons from a Cross-disciplinary Model. In ASEE Annual Conference.
• Subbian, V., Niu, N., & Purdy, C. (2016, 6). Inclusive and Evidence-based Instruction in Software Testing Education. In Annual ASEE Conference.
• Subbian, V., Purdy, C., & Beyette, F. R. (2016). UnLecture on Software Engineering Ethics. In Infusing Ethics into the Development of Engineers: Exemplary Education Activities and Programs, 17-18.
• Subbian, V., Bucks, G., & Heikenfeld, J. (2015, 6). Inverting Instruction in a Semiconductor Devices Course: A Case Study of a Flipped Electrical Engineering Classroom. In Annual ASEE Conference.
• Ratcliff, J. J., Meunier, J. M., Unruh, D., Korfhagen, J. J., Subbian, V., Hart, K. W., Beyette, F. R., Shaw, G. J., Lindsell, C. J., & Bogdanov, V. Y. (2014, 5). A Potential Novel Biomarker of Injury Observed through Activation of the Coagulation Cascade in Mild Traumatic Brain Injury. In Academic Emergency Medicine, 21, S254-S255.
• Simkins, J. R., Subbian, V., & Beyette, F. R. (2014, 7). A programmable point-of-care device for external CSF drainage and monitoring. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2161-2164.
• Subbian, V., & Purdy, C. (2014, 6). A Hybrid Design Methodology for an Introductory Software Engineering Course with Integrated Mobile Application Development. In Annual ASEE Conference.
• Subbian, V., & Purdy, C. (2014, 6). UnLecture: Bridging the Gap between Computing Education and Software Engineering Practice. In Annual ASEE Conference.
• Subbian, V., Beyette, F., & Purdy, C. (2014, 6). UnLecture: A Novel Active Learning Based Pedagogical Strategy for Engineering Courses. In Annual ASEE Conference.
• Subbian, V., Meunier, J. M., Korfhagen, J. J., Ratcliff, J. J., Shaw, G. J., & Beyette, F. R. (2014, 7). Quantitative assessment of post-concussion syndrome following mild traumatic brain injury using robotic technology. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 5353-5356.
• Voss, T. J., Subbian, V., & Beyette, F. R. (2014, 7). Feasibility of energy harvesting techniques for wearable medical devices. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 626-629.
• Wright, K., Sherman, M., Cargile, R., & Subbian, V. (2014, 3). CincySTEM Urban Initiative: Designing pathways to science and engineering disciplines through project-based learning. In Integrated STEM Education Conference (ISEC), 2014 IEEE, 1-5.
• Zirger, B., Rutz, E., Boyd, D., Tappel, J., & Subbian, V. (2014, 3). Creating pathways to higher education: a cross-disciplinary MOOC with graduate credit. In Integrated STEM Education Conference (ISEC), 2014 IEEE, 1-5.
• Al-Deneh, Z., Ambekar, D., Dao, T., Dziech, A. L., Subbian, V., & Beyette, F. R. (2013, 7). Experimental validation of a microcontroller-based wireless device to quantify head impacts. In Circuits and Systems (MWSCAS), 2013 IEEE 56th International Midwest Symposium on, 392-395.
• Ambekar, D., Al-Deneh, Z., Dao, T., Dziech, A. L., Subbian, V., & Beyette, F. R. (2013, 7). Development of a point-of-care medical device to measure head impact in contact sports. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 4167-4170.
• Subbian, V. (2013, 3). Role of MOOCs in integrated STEM education: A learning perspective. In Integrated STEM Education Conference (ISEC), 2013 IEEE, 1-4.
• Subbian, V., & Beyette, F. R. (2013, 10). Developing a new advanced microcontrollers course as a part of embedded systems curriculum. In Frontiers in Education Conference, 2013 IEEE, 1462-1464.
• Subbian, V., & Purdy, C. (2013, 3). Redesigning an advanced embedded systems course: A step towards interdisciplinary engineering education. In Integrated STEM Education Conference (ISEC), 2013 IEEE, 1-4.
• Subbian, V., Wilsey, P. A., & Beyette, F. R. (2013, 11). Heuristic evaluation of user interface for point-of-care diagnosis and rehabilitation of mild Traumatic Brain Injury. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, 1250-1253.
• Subbian, V., Wilsey, P. A., & Beyette, F. R. (2013, 7). Design and development of a robotic system for clinical assessment of motor deficits. In Circuits and Systems (MWSCAS), 2013 IEEE 56th International Midwest Symposium on, 1275-1277.
• Subbian, V., Wilsey, P. A., & Beyette, F. R. (2013, 7). Development and evaluation of hardware for point-of-care assessment of upper-limb motor performance. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 938-940.
• Subbian, V., Beyette, F. R., & Wilsey, P. A. (2012, 7). Design and usability of a medical computing system for diagnosis of mild traumatic brain injury. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 996-999.