About

Yuan Zhang is associated with the Bronfenbrenner Center for Translational Research at Cornell University. The center assists faculty in developing translational research projects by providing support such as proposal preparation assistance, training, technical support, and help in brokering collaborative relationships. The center also offers workshops, an intensive summer institute, and talks on current research to facilitate translational research methods. While specific details about Yuan Zhang's research focus, background, or key contributions are not provided in the page text, their affiliation with the BCTR indicates involvement in translational research activities aimed at supporting faculty in their research endeavors.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Medicine
• Pediatrics
• Obstetrics
• Clinical psychology
• Internal medicine
• Algorithm
• Intensive care medicine
• Psychology
• Psychiatry

Selected publications

• FAIR-EC: A Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care (Preprint)

  2025-03-22

  preprintOpen access

  <sec> <title>BACKGROUND</title> The current landscape of Emergency Care (EC) is marked by high demand leading to issues such as Emergency Department boarding, overcrowding and subsequent delays that impact the quality and safety of patient care. Integrating data science into EC can enhance decision-making with predictive, preventative, personalized, and participatory approaches. However, gaps in adherence to fairness, accountability, interpretability, and responsibility are evident, particularly due to barriers in data-sharing, which often result in a lack of transparency and robust oversight in these applications. </sec> <sec> <title>OBJECTIVE</title> The Fair, Accountable, Interpretable and Responsible (FAIR)-EC collaboration adapts the existing FAIR principles to address emerging challenges as data science integrates with EC. This initiative aims to transform EC by establishing ethical artificial intelligence (AI) standards specifically tailored for this integration. By bridging the gap between EC professionals, data scientists and other stakeholders, the collaboration promotes international cooperation that leverages advanced data science techniques to enhance EC outcomes across different care settings. </sec> <sec> <title>METHODS</title> We propose a federated research design that enables analyses of extensive datasets from various global institutions without compromising patient privacy. This approach transforms epidemiological research with advanced data science techniques, emphasizing the harmonization of data for comprehensive analyses across different healthcare systems. </sec> <sec> <title>RESULTS</title> The FAIR-EC initiative has facilitated the collection and analysis of datasets from diverse geographical regions, enabling the examination of regional variations in EC practices. Initial projects have demonstrated promising outcomes, including the successful development of a federated scoring system and the adaptation of association studies and predictive models across various regions. These efforts highlight the feasibility of leveraging advanced data science techniques to address the complexities of EC while preserving patient privacy. </sec> <sec> <title>CONCLUSIONS</title> FAIR-EC integrates data science ethically and effectively into EC, addressing challenges like fragmented data, real-time handoffs, and public health crises. Its federated design harmonizes diverse data streams while preserving privacy, and its emphasis on ethical AI aligns with the dynamic nature of EC. Despite challenges in data variability and system complexity, FAIR-EC establishes a strong foundation for innovation in global EC. </sec>

  PublisherOA PDFDOI

• Machine learning applications related to suicide in military and Veterans: A scoping literature review

  Journal of Biomedical Informatics · 2025-05-13 · 1 citations

  article
  PublisherDOI

• Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems

  npj Health Systems · 2025-03-18 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Long COVID after SARS-CoV-2 during pregnancy in the United States

  Nature Communications · 2025-04-01 · 11 citations

  articleOpen access

  Pregnancy alters immune responses and clinical manifestations of COVID-19, but its impact on Long COVID remains uncertain. This study investigated Long COVID risk in individuals with SARS-CoV-2 infection during pregnancy compared to reproductive-age females infected outside of pregnancy. A retrospective analysis of two U.S. databases, the National Patient-Centered Clinical Research Network (PCORnet) and the National COVID Cohort Collaborative (N3C), identified 29,975 pregnant individuals (aged 18-50) with SARS-CoV-2 infection in pregnancy from PCORnet and 42,176 from N3C between March 2020 and June 2023. At 180 days after infection, estimated Long COVID risks for those infected during pregnancy were 16.47 per 100 persons (95% CI, 16.00-16.95) in PCORnet using the PCORnet computational phenotype (CP) model and 4.37 per 100 persons (95% CI, 4.18-4.57) in N3C using the N3C CP model. Compared to matched non-pregnant individuals, the adjusted hazard ratios for Long COVID were 0.86 (95% CI, 0.83-0.90) in PCORnet and 0.70 (95% CI, 0.66-0.74) in N3C. The observed risk factors for Long COVID included Black race/ethnicity, advanced maternal age, first- and second-trimester infection, obesity, and comorbid conditions. While the findings suggest a high incidence of Long COVID among pregnant individuals, their risk was lower than that of matched non-pregnant females.

  PublisherOA PDFDOI

• Machine Learning Applications Related to Suicide in Military and Veterans: A Scoping Literature Review

  ArXiv.org · 2025-05-18

  preprintOpen access

  Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in suicide prevention. Machine learning techniques have yielded promising results in this area recently. This study aims to assess and summarize current research and provides a comprehensive review regarding the application of machine learning techniques in assessing and predicting suicidal ideation, attempts, and mortality among members of military and veteran populations. A keyword search using PubMed, IEEE, ACM, and Google Scholar was conducted, and the PRISMA protocol was adopted for relevant study selection. Thirty-two articles met the inclusion criteria. These studies consistently identified risk factors relevant to mental health issues such as depression, post-traumatic stress disorder (PTSD), suicidal ideation, prior attempts, physical health problems, and demographic characteristics. Machine learning models applied in this area have demonstrated reasonable predictive accuracy. However, additional research gaps still exist. First, many studies have overlooked metrics that distinguish between false positives and negatives, such as positive predictive value and negative predictive value, which are crucial in the context of suicide prevention policies. Second, more dedicated approaches to handling survival and longitudinal data should be explored. Lastly, most studies focused on machine learning methods, with limited discussion of their connection to clinical rationales. In summary, machine learning analyses have identified a wide range of risk factors associated with suicide in military populations. The diversity and complexity of these factors also demonstrates that effective prevention strategies must be comprehensive and flexible.

  PublisherOA PDFDOI

• A qualitative Interview Study Investigating Patient, Health Professional, and Developer Perspectives on Real-World Implementation of Patient-Centered AI Systems

  npj Digital Medicine · 2025-11-07

  preprintOpen access

  Our objective was to triangulate patient, health professional, and developer perspectives for implementing patient-centered artificial intelligence (AI) systems. We conducted semi-structured interviews with patients (N = 18), health professionals (N = 8), and AI developers (N = 8). We created interview guides informed by frameworks in bioethics and health information informatics. We utilized a predictive algorithm for determining risk for postpartum depression as a use case to concretize our discussions. Our team analyzed transcripts from interview recordings using thematic, directed content analysis and the constant comparative process. Participants found mitigating potential harms caused by AI (e.g., bias, stigma, or patient anxiety) greatly important. They also believed that AI must provide clinical benefits by allowing health professionals and patients to easily take actions based on AI output. To take safe action, end users needed transparency to understand the AI's accuracy and predictors driving risk. Patient participants wanted health professionals to interpret AI output, but health professionals did not always feel they had the time or training to do so. Participants also raised concerns regarding how data quality may affect AI accuracy, who may be responsible for inappropriate actions taken based on AI, and issues regarding data security, privacy, and accessibility. Our results support real-world implementation of more patient-centered AI tools by: providing health professionals with competencies for discussing AI-based risks; engaging patients and health professionals throughout the development process; inclusively communicating AI output to health professionals and patients; and implementing multi-layer systems of AI governance.

  PublisherOA PDFDOI

• Supporting Personalized prEgnancy Care wIth Artificial inteLligence (SPECIAL): An Acceptability Study of a Personalized Educational Platform

  Studies in health technology and informatics · 2025-08-07

  articleOpen accessSenior author

  Postpartum depression (PPD) affects approximately 20% of pregnant individuals, yet half of these cases remain under-treated despite the availability of educational interventions. To address this gap, the Supporting Personalized prEgnancy Care wIth Artificial inteLligence (SPECIAL) project leverages Artificial Intelligence (AI) to deliver personalized health education materials for PPD prevention and management. This study evaluated the acceptability of a web-based prototype in SPECIAL, developed with patient input, using the Unified Theory of Acceptance of Use of Technology (UTAUT) framework. Survey data from 41 participants indicated high acceptance of this tool. Regression analysis showed that Social Influence (SI) is positively associated with the Behavioral Intention (BI) to use SPECIAL. Patient feedbacks informed further personalization of this prototype, and enhancement of peer-to-peer support features in patient-centered design process.

  PublisherDOI

• The impact of leadership on AI deployment study outcomes in healthcare: an integrative analysis

  npj Digital Medicine · 2025-11-28

  articleOpen accessSenior author

  Studies on AI deployment in healthcare demand interdisciplinary collaboration, making the team structure and leadership essential for guiding AI-driven innovation. Drawing on Upper Echelons Theory, a theory associating organizational outcomes with leadership expertise, we investigated how studies on AI outcomes in healthcare reflect the team structure and leadership. Study data were obtained from 105 studies globally in two literature reviews including 96 randomized clinical trials (RCT). We hypothesized that clinician-led AI deployment studies are more likely to have a significant impact, assuming that last authorship represents leadership. Our analysis using logistic regression controlled for AI- and workflow-related confounders, including AI types and origin, clinical settings, and region. We found that leadership background was significantly associated with AI impact, with clinical leadership having a higher likelihood of impact (OR = 7.793, p = 0.039). The finding maintained when analyzed within RCT only, revealing associations among leadership background, study design, and region.

  PublisherOA PDFDOI

• Aligning incentives: the importance of behavioral economic perspectives in AI adoption

  npj Health Systems · 2025-05-26

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Environment scan of generative AI infrastructure for clinical and translational science

  npj Health Systems · 2025-01-25 · 8 citations

  preprintOpen access

  This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

  PublisherOA PDFDOI

Recent grants

• Understanding and Predicting High-Need, High-Cost Patients among Older Adults

  NIH · $250k · 2020–2022

Frequent coauthors

• Jyotishman Pathak

  65 shared

• Peter Steel

  Weill Cornell Medicine

  57 shared

• Rochelle Joly

  Weill Cornell Medicine

  56 shared

• Tony Rosen

  NewYork–Presbyterian Hospital

  52 shared

• Alyssa Elman

  Presbyterian Hospital

  46 shared

• Alison Hermann

  42 shared

• Sunday Clark

  Pfizer (United States)

  39 shared

• Mark S. Lachs

  Presbyterian Hospital

  38 shared

Education

• B.S., Dietetics

  McGill University

• M.S., Human Nutrition

  McGill University