--- title: "Science — Vironix" description: "Explore Vironix research in clinical AI, chronic disease risk prediction, synthetic health data, and proactive virtual care." image: "https://www.vironix.ai/_astro/open_graph_vironix.uvCNQF08_27tYOt.webp" canonical: "https://www.vironix.ai/science" source: "https://www.vironix.ai/science" --- # Our commitment to science Our team, comprised of esteemed scientists and technologists, is dedicated to pioneering work that stands at the forefront of healthcare and AI. Our research is constantly published in peer-reviewed journals, underscoring our commitment to contributing valuable insights to the scientific community. We take pride in being respected members of the academic world, continuously pushing the boundaries to further scientific efforts and improve patient care through our AI-enabled platform ## Our research focus At the heart of our efforts is the integration of artificial intelligence (AI) into clinical settings, with a particular emphasis on managing and improving outcomes for patients with chronic illnesses. We are committed to developing AI algorithms that not only analyze complex health data but also provide actionable insights for early intervention. Our work aims to shift the paradigm from reactive to proactive management in chronic disease care, focusing on prediction, prevention, and personalized treatment plans to fundamentally transform patient outcomes. ## Recent work ### Differentially Private Time Series EHR Generation (DP-TimeGAN) In collaboration with the University of Oxford, we published a novel, differentially private model for generating realistic synthetic longitudinal health records to safely unlock restricted medical data for research. Results - Generates realistic synthetic EHRs with mathematical privacy guarantees. - Validation showed model output maintained the critical patterns clinicians rely on and clinical reviewers could not distinguish synthetic data from real-world records. - [Published at ML4H 2025 and NeurIPS 2025 (TS4H Workshop)](https://arxiv.org/abs/2512.00434). - Major step toward building clinical AI systems that are private by design and ready for real-world deployment and impact. ### SIAM Mathematical Problems in Industry Workshop 2026 Vironix was a problem presenter at the [SIAM MPI workshop conducted at Drexel University](https://www.siam.org/conferences-events/workshops/2026-mathematical-problems-in-industry-workshop/). We presented a problem on developing disease degradation models for chronic digestive conditions, such as Inflammatory Bowel Disease, Irritable Bowel Syndrome, and Gastroesophageal Reflux Disease. Highlights - Created mechanistic models to predict disease load and symptom flares. - Developed ML models to feed patient-specific parameters into the mechanistic models. - Final report to be published Fall 2026. ### Oxford Collaboration & Gastroenterology Expansion Partnered with the University of Oxford to develop our first chronic digestive disease models, expanding our clinical scope into gastroenterology alongside our foundational mathematics research. Highlights - Built our first predictive models for flare-up focused specifically on chronic digestive diseases. - Work complements modeling work conducted at the SIAM Math Problems in Industry Workshop. - Research will be published as a MMSc thesis Fall 2026. ### Synthetic Electronic Health Record Generation (ScoEHR) In collaboration with Oxford University, ScoEHR was developed as a new approach to synthetic data generation in medical context to overcome issues with past methods using GANs. Results - First diffusion based synthetic EHR generation model. - The state-of-the-art deep learning model for EHR generation. - Published at MLHC 2023 (acceptance rate of 33.6%). ## Publications - [ Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease Benjamin D. Ballyk, Ankit Gupta, Sujay Konda, Kavitha Subramanian, Chris Landon, Ahmed Ammar Naseer, Georg Maierhofer, Sumanth Swaminathan, Vasudevan Venkateshwaran Proceedings of Machine Learning Research · 2025 ](https://arxiv.org/abs/2512.00434) - [ Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk Stratification Zachary Dana, Ahmed Ammar Naseer, Botros Toro, Sumanth Swaminathan Machine Learning for Health · 2024 ](https://arxiv.org/abs/2411.10754) - [ ScoEHR: Generating Synthetic Electronic Health Records using Continuous-time Diffusion Models Naseer, Ahmed Ammar; Walker, Benjamin; Landon, Christopher; Ambrosy, Andrew; Fudim, Marat; Wysham, Nicholas; Toro, Botros; Swaminathan; Sumanth; Lyons, Terry Proceedings of Machine Learning Research · 2023 ](https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/64d1aa32ed57852af9c0ad60/1691462195379/ID145_Research+Paper_2023.pdf) - [ Ordinal Classification in Machine Learning: A Case Study with Chronic Kidney Disease Mahajan, Vaibhav Master's Thesis in Mathematical Modelling and Scientific Computing, University of Oxford · 2023 ](https://sigma.two12.co/view/652c521531b0b9001972260a) - [ Classifying Asthma Health Deterioration Using Synthetic Patient Data Generation and Associated Machine-learning Predictions Derived From Global Clinical Characteristic Data Iyer, S., Swaminathan, Sumanth, Landon, Chris, Wysham, N., Ramanathan, S., Toro, B., Naseer, A. American Thoracic Society 2023 International Conference · 2023 ](https://www.researchgate.net/publication/370455887_Classifying_Asthma_Health_Deterioration_Using_Synthetic_Patient_Data_Generation_and_Associated_Machine-learning_Predictions_Derived_From_Global_Clinical_Characteristic_Data) - [ A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations Morrill, James & Qirko, Klajdi & Kelly, Jacob & Ambrosy, Andrew & Toro, Botros & Smith, Ted & Wysham, Nicholas & Fudim, Marat & Swaminathan, Sumanth Journal of Cardiovascular Translational Research · 2022 ](https://www.researchgate.net/publication/354192517_A_Machine_Learning_Methodology_for_Identification_and_Triage_of_Heart_Failure_Exacerbations) - Real-Time Clinical Assessment and Temporal Predictions of CompEx Events Toro, B.; Morrill, James; Qirko, K.; Jauhiainen, Alexandra; Psallidas, Ioannis; Necander, Sofia; Forsman, Henrik; Da Silva, Carla; Swaminathan, Sumanth · 2021 - [ Vironix: remote screening, detection, and triage of viral respiratory illness via cloud-enabled, machine-learned APIs Swaminathan, Sumanth; Toro, Botros; Wysham, Nicholas; Mark, Nicholas ERS International Congress 2021 abstracts · 2021 ](https://www.researchgate.net/publication/356546841_Vironix_remote_screening_detection_and_triage_of_viral_respiratory_illness_via_cloud-enabled_machine-learned_APIs) - [ Vironix: Remote Screening, Monitoring, and Triage of Viral Respiratory Illness Swaminathan, Sumanth; Toro, Botros; Morrill, James; Berryman, Anna; Wysham, Nicholas; Mark, Nicholas; Ramanathan, Sriram; Konda, Vinay; Iyer, Shreyas; Landon, Chris Chest · 2021 ](https://www.researchgate.net/publication/355295674_VIRONIX_REMOTE_SCREENING_MONITORING_AND_TRIAGE_OF_VIRAL_RESPIRATORY_ILLNESS) - [ Utilization of the signature method to identify the early onset of sepsis from multivariate physiological time series in critical care monitoring James H Morrill, Andrey Kormilitzin, Alejo J Nevado-Holgado, Sumanth Swaminathan, Samuel D Howison, Terry J Lyons Critical Care Medicine · 2020 ](https://www.ingentaconnect.com/content/wk/ccm/2020/00000048/00000010/art00017) - [ A Patient Feedback Driven, Stacked Machine-Learning Approach to At-Home COPD Triage Swaminathan, Sumanth; Morrill, James; Qirko, Klajdi; Smith, Ted; Wysham, Nicholas; Toro, Botros · 2020 ](https://www.researchgate.net/publication/346734068_A_Patient_Feedback_Driven_Stacked_Machine-Learning_Approach_to_At-Home_COPD_Triage) - [ A digital therapy for proactively managing exacerbations and delivering therapeutic benefit to patients with moderate to severe asthma Swaminathan, Sumanth; Gerber, Anthony N; Qirko, Klajdi; Wysham, Nicholas ERS International Congress 2019 · 2019 ](https://www.researchgate.net/publication/337576661_A_digital_therapy_for_proactively_managing_exacerbations_and_delivering_therapeutic_benefit_to_patients_with_moderate_to_severe_asthma) - [ A machine learning approach to triaging patients with chronic obstructive pulmonary disease Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, Kappel G, Gerber AN PLoS One · 2017 ](https://pubmed.ncbi.nlm.nih.gov/29166411/) ## Scientific Collaborations ![Oxford](https://www.vironix.ai/_astro/oxlogomaths.uzt6ymsB.png) ![KEHub](https://www.vironix.ai/_astro/kehub-logo.3ejtQkqa.jpg) ![TVC](https://www.vironix.ai/_astro/TVC_building.DaAmef5G.png) ![University of Delaware](https://www.vironix.ai/_astro/university-of-delaware-vector-logo.dV2x9wzW.png) ![Landon Pediatric Foundation](https://www.vironix.ai/_astro/Landon_Pediatric_Foundation.CAY6lupD.png) ![University of Louisville](https://www.vironix.ai/_astro/Louisville.BI4FV1O1.png) ```json { "@context": "https://schema.org", "@type": "Organization", "@id": "https://www.vironix.ai/#organization", "name": "Vironix Health", "alternateName": "Vironix", "url": "https://www.vironix.ai", "logo": "https://www.vironix.ai/_astro/Vironix_Logo_Primary_300.CFX8xheF.png", "description": "Vironix is a digital health platform that enables healthcare providers to provide AI-enabled virtual care services to their patients, including remote patient monitoring, chronic care management, remote therapeutic monitoring, and more.", "sameAs": [ "https://www.linkedin.com/company/vironix-health" ] } { "@context": "https://schema.org", "@type": "WebSite", "@id": "https://www.vironix.ai/#website", "url": "https://www.vironix.ai", "name": "Vironix Health", "publisher": { "@id": "https://www.vironix.ai/#organization" }, "inLanguage": "en-US" } ```