Computational Health Sciences

Dr. Rizvi, 2024, MN-LHS K Award Recipient

Dr. Rubina Rizvi is one of two recipients of the Minnesota Learning Health System (MN-LHS) Mentored Career Development Award (2024).

The key objective of MN-LHS is to enable Minnesota as a learning health system (LHS) by building a robust workforce with LHS competencies, real world experience, and a support network to be successful. The MN-LHS program receives support through:

•    AHRQ/PCORI P30 Learning Health System Embedded Scientist Training and Research (LHS E-STaR) grant (LEaRN: LHS E-STaR of the North, P30HS029744) (2024-8)
•    Internal UMN support of Office of Academic Clinical Affairs (OACA), Clinical Translational Science Institute (CTSI), and CLHSS (a collaboration between the Medical School and School of Public Health)
•    MN-LHS hub (partner clinical) sites

With her K award, she will be leading the EQUIP (EQuitable Use of PatIent Portals) project to understand current patient portal (PtPl) utilization, identify the gaps in its usage, and explore and test new strategies(s) to enhance patient adoption and sustained use of patient portals. The knowledge gained would help heighten the access, use, and experience with PtPls, a step towards equitable health care delivery to all. 

WCCO-TV - Dr. Zhang leads efforts to limit heart issues related to breast cancer treatment using AI

Dr. Zhang was interviewed by CBS News (WCCO) on his research on AI for cardiotoxicity prediction for breast cancer patients.

Detecting synergistic effects of pharmacological and non-pharmacological interventions for Alzheimer's disease and related dementia

Increasing evidence demonstrates that non-pharmacological interventions (NPI), such as sleep, diet, dietary supplements, aerobic exercise, aromtherapy, light therapy, cognitive training, are potentially modifiable and thus offer alternative opportunities for [Alzheimer's disease and related dementia] prevention.

All of Us Risk Modeling

Individualized medicine requires risk prediction tools to guide the use of interventions and preventative measures.

U of M researchers receive $1.2M to study role of AI in breast cancer treatment

University of Minnesota-based researchers were recently awarded $1.2 million over the next four years from the National Cancer Institute (NCI).

Research Spotlight: Dr. Zhang Received an R01 Renewal Developing Novel AI Approach to Mine Effective & Safe Use of Dietary Supplements

Founding Division Chief of Computational Health Sciences, Dr. Rui Zhang, to develop first-of-its-kind informatics framework for reducing improper dietary supplement use.

Dr. Zhang Received a New R01 to Research Drug Repurposing for Alzheimer's Disease

Funded by the National Institute on Aging, a new research project will aim to explore non-pharmacological interventions, such as sleep, diet, dietary supplements, etc., for Alzheimer's Disease and related dementia prevention.

Current members:

 
Ying Liu, PhD, Research Scientist, [email protected]

Yu Hou, PhD, Research Scientist, [email protected]

Xiaoyi Chen, PhD, Research Scientist, [email protected]
 

Shuang Wang, PhD, Postdoctoral Associate  [email protected]

PhD, Computer Science, Hong Kong Polytechnic University 
 

Jia Li, PhD, Postdoctoral Associate,  [email protected]

PhD, Computer Science, University of Minnesota

Jun Wang, PhD, Postdoctoral Associate. [email protected]

PhD, Computer Science, City University of Hong Kong
 

 Yiran Song, PhD, Postdoctoral Associate. [email protected]

PhD Computer Science, Xi’an Jiaotong University
 

PhD, Postdoctoral Associate. [email protected]

PhD, Signal and Information Processing, Soochow University

Min Zeng, PhD, Postdoctoral Associate,

PhD, Computer Science, Hongkong Science and Technology University
 

 

Jeremy Yeung, Research Programmer, [email protected]

Master, Data Science, UC Berkely
 

Yongkang Xiao, PhD student in Health Informatics, [email protected]   
 

Huixue Zhou, PhD student in Health Informatics, [email protected]
 

Zaifu Zhan, PhD student in Electronic and Computer Engineering, [email protected]

Yifan Wu, PhD student in Bioinformatics and Computational Biology, [email protected]

MS, Electrical and Computer Engineering, Rice University
 

Research Assistant, Yanwei Jin, MS student in Biostatistics and Health Data Science, [email protected] 
 

Alumni:

Mousumi Roy, PhD, Research Scientist, [email protected] 
 

Sicheng Zhou, PhD student, [email protected]

Currently AI Scientist at GE Healthcare
 

Dalton Schutte, Lead AI engineer, [email protected]
 

Mingchen Li, Research Programmer, [email protected]   
 

Han Yang, PhD student, [email protected]

• Omics-based analyses
• Mendelian randomization
• Human-computer interactions
• Mixed method studies
• Natural language processing
• Knowledge graph and discovery
• Machine learning, predictive modeling
• Terminology and ontology

• Clinical Document Annotator (temp/casual position)
  • Apply here: https://hr.myu.umn.edu/jobs/ext/359211
• Health AI Scientist
  • Apply here: https://hr.myu.umn.edu/jobs/ext/359189
• Health AI Engineer
  • Apply here: https://hr.myu.umn.edu/jobs/ext/355261
• Associate Professor, Computational Health Sciences
  • Apply here: https://hr.myu.umn.edu/jobs/ext/352774
• Assistant Professor, Computational Health Sciences
  • Apply here:  https://hr.myu.umn.edu/jobs/ext/352773
• Natural Language Processing Research Programmer (Researcher 4)
  • Apply here: https://hr.myu.umn.edu/jobs/ext/350909
• Postdoctoral Associate
  • Apply here: https://hr.myu.umn.edu/jobs/ext/360487 

• Mentees have worked with our faculty and participated in our cutting-edge research projects
• Mentorship opportunities are open to medical students, residents and fellows, postdoctoral associates, graduate, and undergraduate students;
• We have previous and ongoing collaboration with internal units (e.g., School of Public Health, Department of Computer Science, Institute for Health informatics, School of Nursing, College of Pharmacy) and external institutions (e.g., Mayo Clinic, University of Florida, Yale University, etc)
• Contact our faculty or Leah Harman ([email protected]) for potential collaborations

Dr. Rizvi, 2024, MN-LHS K Award Recipient

Dr. Rubina Rizvi is one of two recipients of the Minnesota Learning Health System (MN-LHS) Mentored Career Development Award (2024).

The key objective of MN-LHS is to enable Minnesota as a learning health system (LHS) by building a robust workforce with LHS competencies, real world experience, and a support network to be successful. The MN-LHS program receives support through:

•    AHRQ/PCORI P30 Learning Health System Embedded Scientist Training and Research (LHS E-STaR) grant (LEaRN: LHS E-STaR of the North, P30HS029744) (2024-8)
•    Internal UMN support of Office of Academic Clinical Affairs (OACA), Clinical Translational Science Institute (CTSI), and CLHSS (a collaboration between the Medical School and School of Public Health)
•    MN-LHS hub (partner clinical) sites

With her K award, she will be leading the EQUIP (EQuitable Use of PatIent Portals) project to understand current patient portal (PtPl) utilization, identify the gaps in its usage, and explore and test new strategies(s) to enhance patient adoption and sustained use of patient portals. The knowledge gained would help heighten the access, use, and experience with PtPls, a step towards equitable health care delivery to all. 

A New Computational Framework for Learning from Imbalanced Biomedical Data

Advances in cancer prevention, diagnosis, and treatment have dramatically improved long-term survival of those diagnosed with breast cancer. However, this success has been tempered by a parallel increased incidence of chronic conditions in breast cancer survivors, in particular cardiovascular disease (CVD), due at least in part to cardiotoxic treatment regimens. Current evidence-based guidelines for preventing and controlling CVD in breast cancer survivors are broad, and we lack clear guidance for assessing individualized risks of cardiovascular events. Existing CVD risk prediction models focus on the general population and rely only on a limited number of variables. The adoption and integration of electronic health record (EHR) systems has provided a wealth of information about individual characteristics at the point of care, including unstructured clinical narratives, imaging data, and structured clinical variables. However, the real-world EHR data is highly imbalanced including the fraction of patients with CVD outcomes and the uniform distribution of time for the CVD development since BC diagnosis. Our overarching goal is to develop solid computational and theoretical foundations for learning from imbalanced real-world data, with an emphasis on BC-CVD outcome risk prediction. Specifically, we will develop a computational framework for imbalanced classification and imbalanced regression tasks on the CVD risk prediction among BC survivors using multimodal EHR data. The successful implementation of this project would lay a computational foundation for imbalanced learning and can provide more accurate tools for predicting BC CVD outcomes. Check details for this NCI R01 here (PI: Rui Zhang/Ju Sun/ Ying Cui).

Mining minority enriched AllofUs data for innovative ethnic specific risk prediction modeling

Advancement of health equity requires evidence and tools tailored for minority groups. The shift towards individualized precision medicine requires risk prediction tools to guide prevention and intervention. Due to the genetic heterogeneity and social economic disparity, risk factors may disproportionately impact race/ethnicity (R/E) groups. Overall risk prediction constructed from predominantly white populations can perform poorly on other ethnic groups, leading to mis-diagnosis, over-treatment and other adverse health consequences. Efforts on developing R/E-specific risk prediction at local healthcare systems are limited by the small sample size caused by inadequate representability of minority populations. To address the gap and to advance precision medicine for non-white patients, it is crucial to harness minority enriched clinical data and develop risk models transferable to point of care. The All of Us (AoU) program offers a wealth of comprehensive multi-modal data on whole genome sequencing (WGS), real-world electronic health records (EHR) and patient reported outcomes (PRO) with enhanced minority participation, providing the common evidence base for learning general R/E-specific risk patterns and training risk models for minority populations at local healthcare systems. In this proposal, we develop innovative methods for risk modeling in AoU data tailored for minority populations and its validation on external healthcare data. We will showcase the proposed methods in two use cases: 1) rheumatoid arthritis (RA) genome-wide association study (GWAS) at Mass General Brigham (MGB) focusing on the genetic risk factors; 2) cancer cardiotoxicity prediction study at M Health Fairview (MHF) focusing on clinical and social determinants of health (SDoH) risk factors. In Aim 1, we integrate risk factor and disease onset outcome data across WGS, EHR and PRO in AoU data to construct the risk prediction model that yields better risk prediction accuracy, risk factor identification and fairness across R/E groups. In Aim 2, we design privacy preserving algorithms to validate the generalizability risk modeling from AoU data on external healthcare data and establish the transfer learning strategy to adapt AoU risk models for local healthcare systems. We intend for the methods to facilitate development of risk modeling using AoU data with focus on minority populations, as well as toe demonstrate the potential impact of the AoU program on improving care at local healthcare. Check details of this NIH R21 (PI: Rui Zhang/Jinhua Wang/Ju Hou).

Detecting synergistic effects of pharmacological and non-pharmacological interventions for Alzheimer’s disease or related dementia (AD/ADRD)

AD/ADRD is a multifactorial and heterogeneous disorder. Most current studies focus on drug interventions for AD/ADRD and currently none of pharmacological intervention (PI) discovery research has been translated into effective treatments. However, increasing evidence demonstrates that non-pharmacological interventions (NPI), such as sleep, diet, dietary supplements, aerobic exercise, aromatherapy, light therapy, cognitive training, are potentially modifiable and thus offer alternative opportunities for AD/ADRD prevention. Thus, the objective of this project is to develop translational informatics approaches to aggregate, standardize and discover the effects of drug and NPI candidates on AD/ADRD using multi-modal data resources (i.e., biomedical literature, EHR, clinical trials) followed by animal model validation. To achieve our goal, we propose the following aims: 1) constructing a comprehensive Pharmacological And Non- Pharmacological Interventions for Alzheimer's Disease Knowledge Graph (PANIA-KG) from biomedical literature and other knowledge bases; 2) detecting, understanding, and visualization of drug repurposing signals of PIs, NPIs, and their synergistical effects for AD/ADRD using the PANIA-KG; 3) re-ranking and validating individual and synergistical drug repurposing signals using multimodal data sources and animal models. The successful completion of this project will deliver a comprehensive NPI knowledge graph, novel informatics approaches, ranked list of drug and NPI candidates, and validated synergistic intervention using multi-modal data sources. The generated approaches, PANIA-KG and ranked lists can further our clinical investigations and clinical trial design which focuses on synergistic effects of drug and NPIs for AD/ADRD. Dr. Rui Zhang serves as PI and collaborates with University of Texas (MPI: Dr. Hua Xu) as a subaward. (Funding Agency: NIH/NIA 1R01AG078154-01, Project period: 07/2022-06/2027)

Evaluation of the SCALED (SCaling AcceptabLE cDs) Approach for the Implementation of Interoperable CDS system for Venous Thromboembolism Prevention

The project is to provide guideline concordant care to patients with traumatic brain injury with appropriate anticoagulation treatment. Our overall goal is to develop, scale, evaluate, and maintain an interoperable Clinical Practice Guideline-on-Fast Healthcare Interoperability Resources  (CPG-on-FHIR) system for COVID-19 venous thromboembolism (VTE) prevention. To investigate, we will conduct a hybrid type 2 randomized stepped wedge trial with multilevel clustering (4 healthcare systems and 9 total sites) across our heterogeneous (both in setting and EHR platform) collaborative CDS network (UMN, Indiana University, Geisinger Health, and UC-Davis). Dr. Rubina Rizvi as a co-investigator, is leading the usability evaluation of the interoperable CDS system based upon the CDS 5 Rights Framework. We plan to complete a rapid cycle CDS evaluation to optimize SMART-on-FHIR workflow integration by conducting a user-driven simulation and expert-driven heuristic usability optimization. Dr. Rizvi is also working closely to help evaluate implementation strategies guided by the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework using a mixed-methods approach. (Funding agency: AHRQ 1R18HS028583-01A1, Project period: 08/2022-07/2025)

An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions

The objective of this project is to develop an informatics framework to enable the discovery of DSIs by creating a DS terminology and mining scientific evidence from the biomedical literature. Towards these objectives, we propose the following specific aims: (1) Compile a comprehensive DS terminology using online resources; and (2) Discover potential DSIs from the biomedical literature. The successful accomplishment of this project will deliver a novel informatics paradigm and resources for identifying most clinically significant DSI signals and their biological mechanisms. One of the outcomes is the integrated Dietary Supplement Knowledge base (iDISK). (Funding Agency: NIH/NCCIH R01AT009457, Project Dates: 04/01/2017-03/31/2022)

2025

1. Ong JCL, Ning Y, Collins GS, Bitterman DS, Ashley BM, Chang RT, Denniston AK, Freyer O, Gilbert S, Liang Z, Lim JCW, Liu M, Liu X, Longhurst CA, Ma Y, Qiu Y, Sarkar R, Sheng B, Singh K, Tan ISK, Tham YCT, Thirunavukarasu AJ, Ting DSW, Vogel S, Zhang R, Zhao J, Chapman WW, Shah NH, Moons KGM, Wong TY, Liu N. An International Initiative for Generative AI Regulation: Partnership for Oversight, Leadership, and Accountability in Regulating Intelligent Systems – Generative Models in Medicine (POLARIS-GM). Natural Medicine. 2025
2. Zhou S, Xu Z, Zhang M, Xu C, Guo Y, Zhan Z, Ding S, Wang J, Xu K, Fang Y, Xia L, Yeung J, Zha D, Melton GB, Lin M, Zhang R. Large language models for disease diagnosis: a scoping review. npj Artificial Intelligence. 2025.
3. Zhou H, Gu H, Liu X, Zhou K, Liang M, Xiao Y, Govindan S, Chawla P, Yang J, Meng X, Li H, Zhang B, Luo L, Chen W, Han Y, et al. The efficiency vs accuracy trade-off: optimizing RAG-enhanced LLM recommender systems using multi-head early exit. ACL. 2025.
4. Han Y, Li M, Zhou H, Zhao Y, Fang Q, Zhou S, Zhang R. One LLM is not enough: harnessing the power of ensemble learning for medical question answering. JMIR. 2025.
5. Xian X, Wang G, Bi X, Zhang R, Srinivasa J, Kundu A, Fleming C, Hong M, Ding J. On the vulnerability of applying retrieval-augmented generation within knowledge-intensive application domains. ICML. 2025.
6. Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, Singer M, Raja K, Huang J, He H, Du J, Zhang R, Zheng J, Adelman R, Lu Z, et al. A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations. Nature Communications. 16(3280). 2025. https://doi.org/10.1038/s41467-025-56989-2
7. Thakkar V, Silverman G, Kc A, Ingraham N, Jones E, King S, Melton G, Zhang R, Tignanelli C. A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2. PLOS ONE. In press.
8. Ranallo P, Melton G, Zhang R, Cimino J, Krueger R. A preliminary ontological model for assessment instruments. AMIA Informatics Summit. In press.
9. Liu Y, Zhang R. Identifying dietary supplements related effects from social media by ChatGPT. AMIA Informatics Summit. In press.
10. Zhou S, Lin M, Ding S, Wang J, Melton G, Zou J, Zhang R. Explainable differential diagnosis with dual-inference large language models. npj Health Systems. 2(12). 2025. https://doi.org/10.1038/s44401-025-00015-6
11. Zhang R, Zou J, Beecy A, Zhang Y, Bian J, Melton-Meaux G, Tao C. Making shiny objective illuminating: the promise and challenges of large language models in U.S. health systems. npj Health Systems. 2025.
12. Hou Y, Bishop J, Liu H, Zhang R. Improving dietary supplement information retrieval: development of a retrieval-augmented generation (RAG) system with large language models. Journal of Medical Internet Research. 27:e67677. 2025.
13. Raja S, Chowdhury S, Buchner V, He Z, Zhang R, Jiang X, Yang P, Cerhan J, Zong N. Synoptic reporting by summarizing cancer pathology reports using large language models. npj Health Systems. In press.
14. Zhan Z, Li M, Zhou H, Zhang R. Towards better multi-task learning: a framework for optimizing dataset combinations in large language models. NAACL. 2025.
15. Chen Y, Feng Y, Zhang X, Gifford KA, Elmanzalawi Y, Samuels J, Albaugh VL, English WJ, Flynn CR, Yu D, Zhang R, Ikramuddin S. Bariatric surgery is associated with reduced incidence of mild cognitive impairment and Alzheimer’s disease and related dementias: a retrospective cohort study. Annals of Surgery Open. 6(1):e541. March 2025. https://doi.org/10.1097/AS9.0000000000000541
16. Idnay B, Xu Z, Adams WG, Adibuzzaman M, Anderson NR, Bahroos N, Bell DS, Bumgardner C, Campion T, Castro M, Cimino JJ, Cohen IG, Dorr D, Elkin PL, Fan JW, et al. Environment scan of generative AI infrastructure for clinical and translational science. npj Health Systems. 2(1):4. 2025 Jan 25. https://doi.org/10.1038/s44401-024-00009-w
17. Li M, Kiligluco H, Xu H, Zhang R. BiomedRAG: a retrieval-augmented large language model for biomedicine. Journal of Biomedical Informatics. 162:104769. 2025 Jan 13. https://doi.org/10.1016/j.jbi.2024.104769
18. Trujeque J, Dudley A, Mesfin N, Ingraham N, Ortiz I, Bangerter A, Chakraborty A, Shutte D, Yeung J, Liu Y, Woodward-Abel A, Bromley E, Zhang R, Brenner L, Simonetti J. Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records. Journal of the American Medical Informatics Association. 32(1):113–118. January 2025.
19. Yan Y, Hou Y, Xiao Y, Zhang R, Wang Q. KNowNEt: guided health information seeking from LLMs via knowledge graph integration. IEEE Transactions on Visualization and Computer Graphics. 31(1):547–557. 2025 Jan. https://doi.org/10.1109/TVCG.2024.3456364
20. Zhan Z, Zhou S, Li M, Zhou H, Xiao Y, Zhang R. RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements. Journal of the American Medical Informatics Association. ocaf002. 2025 Jan 11. https://doi.org/10.1093/jamia/ocaf002
21. Austin RR, Jantraporn R, Schulz C, Zhang R. Navigating online health information: assessing the quality and readability of dietary and herbal supplements for chronic musculoskeletal pain. Topics in Pain Management. 40(7). 2025.
    Rizvi RF, Faisal S, Sussman M, Mendlick P, Brown S, Lindemann E, Keiser J, Karla M, Switzer S, Melton-Meaux GB, Tignanelli CJ. Leveraging dual usability methods to evaluate clinical decision support among traumatic brain injury patients. JMIR Human Factors. 2025.
22. Olson C, Thatipelli S, Schneberger P, Lunos S, Boman K, Melton GB, Adam P, Allen M, Rizvi RF. Exploring end-users’ patient portal usage leveraging a state fair platform. MedInfo. Accepted 2025.
23. Thatipelli S, Loth M, Rizvi RF, Hudelson C, Lindemann E, Kasal T, Warsamea L, Ninkovica I, Markowitz R, Shorts S, Melton GB. Early perspectives on utilization of a clinical decision support tool: a mixed-methods study. MedInfo. Accepted 2025.
24. Chen J, Su L, Li Y, Lin M, Peng Y, Sun C, Zhang R. A multimodal approach for few-shot biomedical named entity recognition in low-resource languages. Journal of Biomedical Informatics. 2025.
25. Lin M, Wang S, Ding Y, Zhao L, Wang F, Peng Y. An empirical study of using radiology reports and images to improve intensive care unit mortality prediction. JAMIA Open. 2025.
26. Yu T, Wang H, Yee NS, Ma F, Lin M, Liu H, et al. Towards collaborative fairness in federated learning under imbalanced covariate shift. KDD. 2025.
27. Tang F, Liu C, Xu Z, Hu M, Huang Z, Xue H, Chen Z, Peng Z, Yang Z, Zhou S, Li W, Li Y, Song W, Su S, Feng W, et al. Seeing far and clearly: mitigating hallucinations in MLLMs with attention causal decoding. CVPR. 2025.

2024: 

1. Li M, Ling C, Zhang R, Zhao L. A condensed transition graph framework for zero-shot link prediction with large language models. ICDM. 2024.
2. Xiao Y, Zhang S, Zhou H, Li M, Yang H, Zhang R. FuseLinker: leveraging LLM’s pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs. Journal of Biomedical Informatics. 2024. https://doi.org/10.1016/j.jbi.2024.104730
3. Hou Y, Cui E, Ikramuddin S, Zhang R. Association of physical activity from wearable devices and chronic disease risk: insights from the All of Us Research Program. medRxiv. 2024 Nov 12. https://doi.org/10.1101/2024.11.11.24317124
4. Yan Y, Hou Y, Xiao Y, Zhang R, Wang Q. Guided health-related information seeking from LLMs via knowledge graph integration. IEEE VIS. 2024.
5. Zhou S, Zha D, Shen X, Huang X, Zhang R, Chung K. Denoising-aware contrastive learning for noisy time series. IJCAI. 2024.
6. Zhou H, Li M, Xiao Y, Yang H, Zhang R. LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction. J Am Med Inform Assoc. 31(9):2010–2018. September 2024.
7. He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer’s disease patients using electronic health record data. Alzheimers Dement (Amst). 16(3):e12613. 2024.
8. Fu S, Wang L, He H, Wen A, Zong N, Kumari A, Liu F, Zhou S, Zhang R, Li C, Wang Y, Sauver J, Liu H, Sohn S. An error taxonomy for advancing systematic error analysis in multi-site EHR-based clinical concept extraction. J Am Med Inform Assoc. In press.
   Yang H, Zhou S, Rao Z, Zhao C, Cui E, Shenoy C, Blaes A, Pamidimukkala N, Hou Y, Wang J, Hou J, Zhang R. Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program. J Am Med Inform Assoc. 31(12):2800–2810. December 2024.
9. Sarker A, Zhang R, Wang Y, Xiao Y, Das S, Schutte D, Oniani D, Xie Q, Xu H. Natural language processing for digital health in the era of large language models. Yearb Med Inform. 33(01):229–240. 2024.
10. Li M, Zhou H, Yang H, Zhang R. RT: a retrieving and chain-of-thought framework for few-shot medical named entity recognition. J Am Med Inform Assoc. ocae095. 2024 May 6. https://doi.org/10.1093/jamia/ocae095
    Peng L, Luo X, Zhou S, Chen J, Sun J, Zhang R. An in-depth evaluation of federated learning on biomedical natural language processing for information extraction. npj Digital Medicine. 7(1):127. 2024 May 15. https://doi.org/10.1038/s41746-024-01126-4
11. Austin R, Jantraporn R, Schulz C, Zhang R. Navigating online health information: assessing the quality and readability of dietary and herbal supplements for chronic musculoskeletal pain. CIN: Computers, Informatics, Nursing. 2024.
12. Skoufis N, Zhang R, Chen Y. Measuring associations between community-level social determinants of health and bariatric surgery weight loss outcomes. Stud Health Technol Inform. 310:1317–1321. 2024 Jan 25. https://doi.org/10.3233/SHTI231178
13. Luo Y, Hooshangnejad H, Feng X, Huang G, Zhang R, Chen Q, Ding K. False positive reduction in pulmonary cancer detection based on GPT-4V. Medical Imaging with Deep Learning. 2024.
14. Sun B, Yew P, Chi C, Song M, Zhang R, Straka R. Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured EHR data. J Am Med Inform Assoc Open. 2023 in press.
    Dauner D, Leal E, Adam T, Zhang R, Farley J. Evaluation of four machine learning models for signal detection. Therapeutic Advances in Drug Safety. 2023 accepted.
15. Zhou H, Austin R, Lu S, Silverman G, Zhou Y, Kilicoglu H, Xu H, Zhang R. Complementary and integrative health information in the literature: its lexicon and named entity recognition. J Am Med Inform Assoc. 31(2):426–434. 2024.
16. Ranard BL, Park S, Lusczek Y, Zhang Y, Alwan F, Celi LA, Lusczek ER. Minimizing bias when using artificial intelligence in critical care medicine. Journal of Critical Care. Accepted March 2024. https://doi.org/10.1016/j.jcrc.2024.154796
17. Ranallo P. The Observational Medical Outcomes Program (OMOP) common data model: an invaluable resource for advancing translational research in mood disorders. Biological Psychiatry. 95. 2024. https://www.biologicalpsychiatryjournal.com/article/S0006-3223(24)00127-6

2023:

1. Su C, Hou Y, Wang F, Zhang R. Protocol to implement a computational pipeline for biomedical discovery based on a biomedical knowledge graph. STAR Protocols. 4(4):102666. 2023.
2. Zhou S, Wang N, Wang LW, Sun J, Blaes A, Liu H, Zhang R. A cross-institutional evaluation on breast cancer phenotyping NLP algorithms on electronic health records. Computational and Structural Biotechnology Journal. 22:32–40. 2023.
3. Knoll B, Gunderson M, Rajamani G, Wick EC, Colley A, Lindemann E, Rizvi R, Diethelm M, Hultman G, Pierce L, Zhang R, Melton G. Studies in health technology and informatics. 310:609–613. 2023.
4. Silverman G, Rajamani G, Ingraham N, Glover J, Sahoo H, Usher M, Zhang R, Ikramuddin F, Melnik T, Melton G, Tignanelli C. A symptom-based natural language processing surveillance pipeline for post-COVID-19 patients. Stud Health Technol Inform. 310:860–864. 2023.
5. Zhou H, Silverman G, Niu Z, Silverman J, Evans R, Austin R, Zhang R. Extracting complementary and integrative health approaches in electronic health records. Journal of Healthcare Informatics Research. 7(3):277–290. 2023.
6. Gao Z, Ryu S, Zhou W, Adams K, Hassan M, Zhang R, Blaes A, Wolfson J, Sun J. Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors’ physical activity and quality of life. Journal of Sport and Health Science. 12(6):705–714. 2023.
7. Hooshangnejad H, Chen Q, Feng X, Zhang R, Farjam R, Voong R, Hales R, Du Y, Jia X, Ding K. DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer. Frontiers in Oncology. In press. 2023.
8. Hooshangnejad H, Chen Q, Feng X, Zhang R, Ding K. deepPERFECT: novel deep learning CT synthesis method for expeditious pancreatic cancer radiotherapy. Cancers. 15(11):3061. 2023.
9. Su C, Hou Y, Wang F, Zhang R, et al. Biomedical discovery through the integrative Biomedical Knowledge Hub (iBKH). iScience. 26(4):106460. 2023. https://doi.org/10.1016/j.isci.2023.106460
10. Keloth VK, Banda JM, Gurley M, Heider PM, Kennedy G, Liu H, Miller T, Natarajan K, Patterson OV, Peng Y, Raja K, Reeves RM, Rouhizadeh M, Shi J, Wang X, et al. Representing and utilizing clinical textual data for real-world studies: an OHDSI approach. Journal of Biomedical Informatics. 104343. 2023. https://doi.org/10.1016/j.jbi.2023.104343
11. Hou Y, Yeung J, Xu H, Su C, Wang F, Zhang R. From answers to insights: unveiling the strengths and limitations of ChatGPT and biomedical knowledge graphs. medRxiv. 2023 June 12. https://doi.org/10.1101/2023.06.09.23291208
12. Skoufis N, Zhang R, Chen Y. Measuring associations between community-level social determinants of health and bariatric surgery weight loss outcomes. Stud Health Technol Inform. In press. 2023.
13. Tariq R, Malik S, Roy M, Islam M, Rasheed U, Bian J, Zheng K, Zhang R. Assessing ChatGPT for text summarization, simplification and extraction tasks. IEEE International Conference on Healthcare Informatics. In press. 2023.
14. Silverman G, Rajamani G, Ingraham N, Glover J, Sahoo H, Niu Z, Usher M, Zhang R, Ikramuddin F, Melnik T, Melton G, Tignanelli C. A symptom-based natural language processing surveillance pipeline for post-COVID-19 patients. Stud Health Technol Inform. In press. 2023.
15. Dauner D, Zhang R, Adam T, Leal E, Heitlage V, Farley J. Performance of subgrouped proportional reporting ratios in the US Food and Drug Administration (FDA) Adverse Event Reporting System. Expert Opinion on Drug Safety. Accepted 2023.
    Braaten JA, Dillon BS, Wothe JK, Olson CP, Lusczek ER, Sather KJ, Beilman GJ, Brunsvold ME. Extracorporeal membrane oxygenation patient outcomes following restrictive blood transfusion protocol. Critical Care Explorations. 5(12):e1020. 2023. https://doi.org/10.1097/CCE.0000000000001020
16. Castro-Pearson S, Samorodnitsky S, Yang K, Lotfi-Emran S, Ingraham NE, Bramante CJ, Jones EK, Greising S, Yu M, Steffen B, Svensson J, Åhlberg E, Österberg B, Wacker D, Guan W, et al. Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies. Scientific Reports. 13(1). 2023. https://doi.org/10.1038/s41598-023-46343-1
17. Yang K, Kang Z, Guan W, Lotfi-Emran S, Mayer ZJ, Guerrero CR, Steffen BT, Puskarich MA, Tignanelli CJ, Lusczek ER, Safo SE. Developing a baseline metabolomic signature associated with COVID-19 severity: insights from prospective trials encompassing 13 U.S. centers. Metabolites. 13(11). 2023. https://doi.org/10.3390/metabo13111107
18. Xie F, Ning Y, Liu M, et al. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols. 4(2):102302. 2023.

2022:

1. Steffen BT, Pankow JS, Norby FL, Lutsey PL, Demmer RT, Guan W, Pankratz N, Li A, Liu G, Matushita K, Tin A, Tang W. Proteomics analysis of genetic liability of abdominal aortic aneurysm identifies plasma neogenin and kit ligand: The Atherosclerosis Risk in Communities Study. ATVB. Accepted for publication.
2. Steffen BT, Tang W, Lutsey PL, Demmer RT, Selvin E, Matushita K, Morrison AC, Guan W, Rooney MR, Pankratz N, Couper D, Norby FL, Pankow JS. Proteomic analysis of diabetes genetic risk scores identifies complement C2 and neuropilin-2 as predictors of type 2 diabetes: The Atherosclerosis Risk in Communities (ARIC) Study. Diabetologia. 2023 Jan;66(1):105-115.
3. Steffen BT, Pankow JS, Lutsey PL, Demmer RT, Misialek JR, Guan W, Cowan LT, Coresh J, Norby FL, Tang W. Proteomic Profiling Identifies Novel Proteins for Genetic Risk of Severe COVID-19: the Atherosclerosis Risk in Communities Study. Hum Mol Genet. 2022; ddac024.
4. Rizvi RF;  VanHouten C; Willis Van; Rosario B; South B; Sands-Lincoln M; Brotman D; Lenert J; Jane Snowdon J;  Jackson Gretchen Understanding a Care Management System’s Role in Influencing a Transitional Aged Youth Program’s Practice – A Mixed Methods Study (Preprint) 2022-05-17 | Preprint DOI: 10.2196/preprints.39646
5. Emani S, Rui A, Rocha HAL, Rizvi RF, Juaçaba SF, Jackson GP, Bates DW. Physicians' Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle-Income Countries. JMIR Cancer. 2022 Apr 7;8(2):e31461. doi: 10.2196/31461. PMID: 35389353; PMCID: PMC9030908.
6. Melnik T, Thompson JA, Vasilakes J, Annis T, Zhou S, Schutte D, Melton GB, Pleasants S, Zhang R. Semi-automated clinical content curation of COVID-19 chatbot remote patient monitoring system. 2022 AMIA Symposium. Accepted
7. Blount, D., Zhang, R., Blaes, A., & Gao, Z. Effects of health wearables on cancer survivors’ health outcomes: A meta-analysis. International Journal of Physical Activity and Health. 2022 (in press)
8. Melnik T, Thompson J, Vasilakes J, Annis T, Zhou S, Schutte D, Melton G, Pleasants S, Zhang R. Semi-automated clinical content curation with COVID-19 remote patient monitoring. AMIA Annual Symposium. 2022. (in press)
9. Barrett L, Xing A, Sheffler J, Steidley E, Adam T, Zhang R, He Z. Assessing the use of prescription drugs and dietary supplements in obese respondents in the National Health and Nutrition Examination Survey. PLoS One. 2022;17(6):e0269241. doi: 10.1371/journal.pone.0269241. eCollection 2022. PMID: 35657782; PMCID: PMC9165812.
10. Nian Y, Hu X, Zhang R, Feng J, Du J, Li F, Bu L, Zhang Y, Chen Y, Tao C. Mining on Alzheimer's diseases related knowledge graph to identify potential AD-related semantic triples for drug repurposing. BMC Bioinformatics. 2022 Sep 30;23(Suppl 6):407. doi: 10.1186/s12859-022-04934-1. PMID: 36180861; PMCID: PMC9523633.
11. Schutte D, Vasilakes J, Bompelli A, Zhou Y, Fiszman M, Kilicoglu H, Bishop J, Adam T, Zhang R. Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature. J Biomed Inform. 2022 Jul;131:104120. doi: 10.1016/j.jbi.2022.104120. Epub 2022 Jun 13. PMID: 35709900; PMCID: PMC9335448.
12. Shen Z, Schutte D, Yi Y, Bompelli A, Yu F, Wang Y, Zhang R. Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision. BMC Med Inform Decis Mak. 2022 Jul 7;22(Suppl 1):88. doi: 10.1186/s12911-022-01819-4. PMID: 35799294; PMCID: PMC9261217.
13. Singh E, Bompelli A, Wan R, Bian J, Pakhomov S, Zhang R. A conversational agent system for dietary supplements use. BMC Med Inform Decis Mak. 2022 Jul 7;22(Suppl 1):153. doi: 10.1186/s12911-022-01888-5. PMID: 35799177; PMCID: PMC9264487.
14. Zhou S, Wang N, Wang L, Liu H, Zhang R. CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records. J Am Med Inform Assoc. 2022 Jun 14;29(7):1208-1216. doi: 10.1093/jamia/ocac040. PMID: 35333345; PMCID: PMC9196678.
15. Kiogou SD, Chi CL, Zhang R, Ma S, Adam TJ. Clinical data cohort quality improvement: The case of the medication data in the University of Minnesota's Clinical Data Repository. AMIA Annu Symp Proc. 2022 May 23;2022:293-302. PMID: 35854717; PMCID: PMC9285162.

2021:

1. Steffen BT, Guan W, Ding J, Nomura SO, Weir NL, Tsai MY. Plasma omega-3 and saturated fatty acids are differentially related to pericardial adipose tissue volume across race/ethnicity: The Multi-Ethnic Study of Atherosclerosis. Eur J Clin Nutr. 2021 Aug;75(8):1237-1244.
2. Kim JS, Steffen BT, Podolanczuk AJ, Kawut SM, Noth I, Raghu G, Michos ED, Hoffman EA, Axelsson GT, Gudmundsson G, Gudnason V, Gudmundsson EF, Murphy RA, Dupuis J, Xu H, Vasan RS, O'Connor GT, Harris WS, Hunninghake GM, Barr RG, Tsai MY, Lederer DJ. Associations of Omega-3 Fatty acids with Interstitial Lung Disease and Lung Imaging Abnormalities Among Adults. Am J Epidemiol. 2021 Jan 4;190(1):95-108.
3. Garg PK, Guan W, Karger AB, Steffen BT, Budoff M, Tsai MY. Lipoprotein (a) and risk for calcification of the coronary arteries, mitral valve, and thoracic aorta: The Multi-Ethnic Study of Atherosclerosis. J Cardiovasc Comput Tomogr. 2021 Mar-Apr;15(2):154-160.
4. Rizvi RF, VanHouten C, Bright TJ, McKillop MM, Alevy S, Brotman D, Sands-Lincoln M, Snowdon J, Robinson BJ, Staats C, Jackson GP, Kassler WJ. The Perceived Impact and Usability of a Care Management and Coordination System in Delivering Services to Vulnerable Populations: Mixed Methods Study. J Med Internet Res. 2021 Mar 12;23(3): e24122. doi: 10.2196/24122. PMID: 33709928; PMCID: PMC7998322.
5. Sun Z., Rizvi RF, South B, Scheufele E, Draulis K, Feldman H. Extraction of Ambiguous Phrases Found Within Adverse Drug Event Mentions Using an Artificial Intelligence Annotation tool. Poster abstract, AMIA Annual Symposium 2021.
6. Rizvi RF, Craig KJT, Hekmat R, Reyes F, South B, Rosario B, Kassler WJ, Jackson GP. Effectiveness of non-pharmaceutical interventions related to social distancing on respiratory viral infectious disease outcomes: A rapid evidence-based review and meta-analysis. SAGE Open Med. 2021 Jun 6;9:20503121211022973. doi: 10.1177/20503121211022973. PMID: 34164126; PMCID: PMC8188982.
7. Thomas Craig KJ, Rizvi RF, Willis VC, Kassler WJ, Jackson GP. Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review. JMIR Public Health Surveill. 2021 Oct 6;7(10): e32468. doi: 10.2196/32468. PMID: 34612841; PMCID: PMC8496751.
8. Emani S, Rui A, Rocha HAL, Rizvi RF, Juaçaba SF, Jackson GP, Bates DW. Physicians' Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle-Income Countries. JMIR Cancer. 2022 Apr 7;8(2):e31461. doi: 10.2196/31461. PMID: 35389353; PMCID: PMC9030908.
9. Tao D, Wei D, Rizvi RF, Pandita D, Alghamdi B, Kukhareva P, Sordo M. Gender-Specific Career Development Issues in Biomedical Informatics within Professional Organizations. Panel, AMIA Annual Symposium, 2021.
10. Silverman G, Sahoo H, Ingraham N, Lupei M, Pusharich M, Usher M, Dries J, Finzel R, Murray E, Sartori J, Simon G, Zhang R, Melton G, Pakhomov S. NLP methods for extraction of symptoms from unstructured data for use in prognostic COVID-19 analytic models. Journal of Artificial Intelligence Research. 2021(72):429-474. https://doi.org/10.1613/jair.1.12631.
11. Bompelli A^#, Wang Y#,  Wan R, Singh E, Zhou Y, Xu L, Oniani D, Kshatriya BSA, Balls-Berry JE, and Zhang R. Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review. Health Data Science. 2021:Article ID 9759016. https://doi.org/10.34133/2021/9759016.
12. Wang Y, Zhao Y, Schutte D, Bian J, Zhang R. Deep learning models in detection of dietary supplement adverse event signals from Twitter. JAMIA Open. 2021 Oct;4(4):ooab081. doi: 10.1093/jamiaopen/ooab081. eCollection 2021 Oct. PMID: 34632323; PMCID: PMC8497875.
13. Sahoo HS, Silverman GM, Ingraham NE, Lupei MI, Puskarich MA, Finzel RL, Sartori J, Zhang R, Knoll BC, Liu S, Liu H, Melton GB, Tignanelli CJ, Pakhomov SVS. A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification. JAMIA Open. 2021 Aug 7;4(3):ooab070. doi: 10.1093/jamiaopen/ooab070. PMID: 34423261; PMCID: PMC8374371.
14. Zhang R #, Hristovski D#, Schutte D #, Kastrin A#, Fiszman M, Kilicoglu H. Drug repurposing for COVID-19 via knowledge graph completion. J Biomed Inform. 2021 Mar;115:103696. doi: 10.1016/j.jbi.2021.103696. Epub 2021 Feb 8. PMID: 33571675; PMCID: PMC7869625.
15. Fan Y, Zhou S, Li Y, Zhang R. Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text. J Am Med Inform Assoc. 2021 Mar 1;28(3):569-577. doi: 10.1093/jamia/ocaa218. PMID: 33150942; PMCID: PMC7936508.
16. He X^#, Zhang R^#, Alpert J, Zhou S, Adam T, Raisa A, Peng Y, Zhang H, Guo Y, Bian J. When text simplication is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information? JAMIA Open. 2021 Jan;4(1): ooab026. https://doi.org/10.1093/jamiaopen/ooab026.