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Susannah Rose, MSSW, PhD

Susannah
Rose
Associate Professor
Department of Biomedical Informatics
Core Faculty
Center for Bioethics & Society
Associate Professor
Department of Health Policy
2525 West End Avenue
Nashville
Tennessee
37203
susannah.rose@vumc.org

Dr. Susannah Rose is an Associate Professor in the Department of Biomedical Informatics and is Core Faculty in the Center for Biomedical Ethics and Society at Vanderbilt University Medical Center (VUMC) and Vanderbilt University (VU). She has a secondary appointment in the Department of Health Policy and is a Faculty Scholar in the Center for Health Services Research. Prior to VUMC/VU, she was a Full Professional Staff (faculty) member at Cleveland Clinic, and she served in many leadership roles, including as the Director of Research for the Center of Bioethics, the Director of Research for Safety, Quality and Patient Experience, and the Associate Chief Experience Officer. She was also an Assistant Professor at Cleveland Clinic’s Lerner College of Medicine and in the Department of Bioethics at Case Western Reserve University. Dr. Rose is also an Instructor at Harvard University’s T.H. Chan School of Public Health, where she teaches public health ethics, including artificial intelligence ethics.

Dr. Rose earned her Ph.D. from Harvard University's Health Policy Program (with a concentration in Ethics) in 2010. Dr. Rose was a National Institute of Mental Health (NIMH) pre-doctoral research fellow; a Harvard Edmond J. Safra Center for Ethics Graduate Fellow; a Safra post-doctoral lab fellow; and she was also a pre- and post-doctoral fellow at Massachusetts General Hospital, sponsored by the National Cancer Institute (NCI) through the Program in Cancer Research Outcomes Training (PCORT). Prior to her doctoral studies, she earned an MS in Bioethics from Union College/Albany Medical Center in 2006, and a MS in Social Work from Columbia University in 1998. Dr. Rose was a clinical social worker and researcher at Memorial Sloan-Kettering Cancer Center in New York City.

During her career, Dr. Rose has received multiple mentorship and teaching awards. She has published two books focused on helping patients and family members cope with cancer, and she has published and presented in academic venues on topics related to technology diffusion in healthcare, conflicts of interest in medicine, health policy ethics, and bioethics. Her publications have appeared in high-ranked peer-reviewed journals, such as: JAMA Internal Medicine, Journal of General Internal Medicine (JGIM), The Journal of Clinical Oncology (JCO), The New England Journal of Medicine (NEJM), PLOS One, American Journal of Bioethics (AJOB), and The Hastings Center Report, and her book chapters on health policy ethics and end of life care have been published by the Oxford University Press. She frequently presents at peer-reviewed national and international conferences, and is invited to speak at conferences all over the world, including the United States, Sweden, Germany and the Kingdom of Saudi Arabia.

Her current research focuses upon the ethics and impact of medical technology innovations, including artificial intelligence in healthcare, improving patient experience, and developing data-driven approaches to improve patient care. Dr. Rose’s research has been generously funded by multiple funding sources, including the National Institutes of Health (NIH), Harvard University’s Edmond J. Safra Center for Ethics, NIH’s Clinical & Translational Science Collaborative (CTSC) at Cleveland Clinic & Case Western Reserve University, and The Greenwall Foundation, in addition to internal Cleveland Clinic funding sources.

Mohammed Ali Al-Garadi, PhD

Mohammed
A
Al-Garadi
Research Assistant Professor
Department of Biomedical Informatics

Dr. Mohammed Al-Garadi is a Research Assistant Professor in the Department of Biomedical Informatics at Vanderbilt University Medical Center. He previously worked as a Postdoctoral Researcher at Emory University, focusing on natural language processing (NLP), machine learning (ML), deep learning and large language models (LLMs) for healthcare applications. His research focuses on extracting insights from unstructured healthcare data, particularly unstructured notes, using NLP and machine learning techniques. By developing modules and pipelines, he has created systems to efficiently process diverse healthcare data streams. He worked on NIH and CDC grants involving the application of NLP and machine learning to analyze large-scale clinical narratives and public health data. 

To date, Dr. Al-Garadi has authored and co-authored over 50 papers in high-impact scientific journals. Currently, Dr. Al-Garadi is exploring the potential of NLP, ML, and LLMs on unstructured EHR clinical notes for various healthcare applications. These include extracting, predicting, and detecting causes of death, postoperative infections, COPD exacerbations, kidney disease, peripheral artery disease, and tele-dermatology conditions and outcomes. He is working on projects supported by the NIH, Department of Veterans Affairs, and FDA.

Twitter: https://twitter.com/AliAlgaradi
Google Scholar

Peter J. Embí, MD, MS, FACP, FACMI, FAMIA, FIAHSI

Peter
Embí
Professor and Chair
Department of Biomedical Informatics
Senior Vice President for Research and Innovation
Professor of Medicine
Vanderbilt University Medical Center
Co-Director
ADVANCE
2525 West End Avenue
Nashville
Tennessee
37203
peter.embi@vumc.org

Peter Embí, MD, MS, serves as Professor and Chair of the Department of Biomedical Informatics (DBMI), Professor of Medicine, and Senior Vice President for Research and Innovation at Vanderbilt University Medical Center (VUMC), roles he began on Jan. 1, 2022. 

As an internationally recognized researcher, educator and leader in the field of biomedical informatics, Dr. Embí is a frequently invited presenter and lecturer and has authored more than 160 peer-reviewed research articles, abstracts, books and book chapters. His areas of interest include clinical informatics, research informatics, public health informatics, and data-driven learning health systems. He had held research grants from such agencies as the National Institutes of Health’s National Center for Advancing Translational Sciences, National Library of Medicine, National Institute for Drug Abuse and the Agency for Healthcare Research and Quality, as well as numerous nonprofit foundations and public health agencies. 

He earned a Bachelor of Science from the University of Florida, Gainesville, and his medical degree from the University of South Florida in Tampa. He completed residency, chief residency, an informatics fellowship and a Master of Science in Medical Informatics & Clinical Epidemiology at Oregon Health & Science University. He then completed his rheumatology and immunology fellowship training at the Cleveland Clinic before joining the faculty at the University of Cincinnati College of Medicine.

Prior to his move to VUMC, Dr. Embí served as President and CEO of the Regenstrief Institute, Professor and Associate Dean for Informatics and Health Services Research at the Indiana University School of Medicine, Associate Director at Indiana CTSI and Vice President for Learning Health Systems at IU Health. Previous positions included leadership roles at The Ohio State University, where he was interim chair of the Department of Biomedical Informatics, Associate Dean for Research Informatics for the College of Medicine and served as the nation’s first Chief Research Information Officer at The Ohio State University Wexner Medical Center. Prior to that, he served on the faculty of the University of Cincinnati College of Medicine, where he was the founding director of the UC Center for Health Informatics and director of Informatics for the Cincinnati Center for Clinical and Translational Science and Training. Dr. Embí also has experience in a variety of entrepreneurial activities, including co-inventing and co-founding health IT-based startups, partnering with companies to create and evaluate health care innovations, and developing programs that guide and enable other faculty to translate their discoveries into practice.

In recognition of his contributions to the field, Embí has been elected to Fellowship in the American College of Physicians (FACP), the American College of Medical Informatics (FACMI), the American Medical Informatics Association (FAMIA) and the International Academy of Health Sciences Informatics (FIAHSI). He has also served in numerous national leadership roles, including as the immediate past president and chair of the Board of Directors of the American Medical Informatics Association. He also has served on many national advisory boards, including service on the Board of Scientific Counselors to the National Library of Medicine and on the National Advisory Council for the Agency for Healthcare Research and Quality (AHRQ).

Follow Dr. Embí on Twitter here
Find Dr. Embí on LinkedIn here

See Dr. Embi's recent publications below:

Siru Liu, PhD

Siru
Liu
Assistant Professor
Department of Biomedical Informatics
Assistant Professor
Department of Computer Science
2525 West End Ave
siru.liu@vumc.org

Siru Liu, PhD, is an Assistant Professor in the Department of Biomedical Informatics at Vanderbilt University Medical Center and Assistant Professor in the Department of Computer Science at Vanderbilt University. Her research interests include: optimizing electronic health record features and functions, with an emphasis on clinical decision support, leveraging machine learning and large language model techniques to improve healthcare quality, and analyzing public perceptions using social and behavioral science. She holds a PhD in Biomedical Informatics from the University of Utah School of Medicine. Dr. Liu received NIH NLM K99/R00 Pathway to Independence Award for her research in optimizing CDS using explainable AI. She is featured as the Postdoc of the Year at Vanderbilt in 2023. She is recognized as a 2022 Google Cloud Research Innovator. She is a fellow in the NCI’s Multilevel Intervention Training Institute, a 2022-23 American Association of University Women International Fellow, and a scholar in the Women in AMIA (WIA) Leadership Program. She developed an explainable machine learning-based clinical decision support tool to predict new onsets of ICU delirium. This project was  a finalist in the 2022 AMIA Artificial Intelligence Evaluation Showcase. She serves as a member of the WIA Committee and the Diversity, Equity, and Inclusion (DEI) Committee in AMIA. She also served in the Scientific Program Committee at AMIA 2023 Annual Symposium and as an Area Chair in the Women in Machine Learning Workshop at NeurIPS.

Google Scholar: https://scholar.google.com/citations?user=JKXOENIAAAAJ&hl=en

Elliot Fielstein, PhD

Elliot
Fielstein
PhD
Associate Professor
Department of Biomedical Informatics
Associate Professor
Department of Psychiatry

Elliot M. Fielstein, Ph.D. is an Associate Professor of Biomedical Informatics in the School of Medicine at Vanderbilt University and Director of Clinical and Data Analytics in the Informatics Section of Mental Health Service at the Department of Veterans Affairs Central Office, Washington DC.  Dr. Fielstein is a neuropsychologist and informaticist and spent 15 years as a faculty member in the Vanderbilt University Department of Psychiatry before transferring to Biomedical Informatics.  While in Psychiatry, he founded the Neuropsychology Laboratory at the Nashville VAMC where he conducted neuropsychological assessment as well as Psychology and Psychiatry resident and fellow training.  He also participated part-time as a software engineer in the StarPanel electronic patient record systems where he developed an electronic cancer staging system utilizing the AJCC Cancer Staging system.  

 

At the Department of Veterans Affairs Central Office, he has worked in the Chief Business Office supporting VA Compensation and Pension (C&P) where he developed a national C&P examination database system and electronic exam review system.  Currently, he works in VA Mental Health Service Informatics Section focused on development of national mental health performace measures and coordination and building of business applications for the National Center for PTSD and other program offices.  His operations and research activities and interests are in mental health informatics including natural language processing, clinical terminologies and decision support in postraumatic stress disorder and traumatic brain injury.

 

Dr. Fielstein received a Ph.D. in Clinical Psychology from the University of Vermont, and completed fellowships in Clinical Neuropsychology at the Neurosurgical Section, Department of Surgery, University of Alabama-Birmingham School of Medicine, and in Medical Informatics at the Department of Biomedical Informatics at Vanderbilt University School of Medicine.

615-936-7208
elliot.m.fielstein@vumc.org
FielsteinElliotPhD

Colin G. Walsh, MD, MA, FACMI, FAMIA, FIAHSI

Colin
G.
Walsh
MD, MA
Associate Professor
Department of Biomedical Informatics
Associate Professor
Department of Medicine
Associate Professor
Department of Psychiatry

Dr. Colin G. Walsh is a practicing internist and clinical informatician who joined Vanderbilt University as Assistant Professor of Biomedical Informatics, Medicine, and Psychiatry in early 2015. His research is focused in predictive analytics applied to vulnerable populations, clinical workflow, and decision support at the point-of-care. 

His foci of research and operational work are: 1) machine learning/data science applied to use-cases in mental health; 2) utilization optimization and quality improvement; 3) an analytics approach to value-based healthcare. 

As Founder and Principal Investigator of the Walsh Lab, he is mentoring multiple trainees ranging from undergraduates to informatics PhD candidates to practicing clinical sub-specialists. 

After undergraduate training in mechanical engineering at Princeton University, Dr. Walsh attended medical school at the University of Chicago. He completed residency and chief residency in internal medicine at Columbia University Medical Center. He studied machine learning and data science in the domain of hospital readmission risk prediction at Columbia University under research mentor, Dr. George Hripcsak, in fellowship in Biomedical Informatics funded by the National Library of Medicine.

At Vanderbilt, he continues to develop clinically-grounded predictive models using data science approaches on structured and unstructured clinical data. Examples of active projects range from:

  1. Machine learning + natural language processing approaches to predict and phenotype risks of suicidality 
  2. Analytics approaches to support Value-Based Healthcare 
  3. Visual Analytics + Machine Learning Approaches to predict healthcare utilization to support interventions in Quality and Clinical Improvement
  4. Algorithms that identify and predict unnecessary healthcare service utilization in Choosing Wisely

Google Scholar: https://scholar.google.com/citations?user=FHBxu1EAAAAJ&hl=en

https://www.walshscience.com
Follow Dr. Walsh on Twitter: @CWalshMD

 

615-936-5684
2525 West End Ave.
Nashville, TN
37203
colin.walsh@vumc.org
WalshColinG.MD, MA

Paul Harris, PhD, FACMI, FIAHSI

Paul
Harris
PhD, FACMI, FIAHSI
Professor
Department of Biomedical Informatics
Professor
Department of Biomedical Engineering
Professor
Department of Biostatistics
Vice President for Research Informatics
VUMC Office of Research Informatics

Paul Harris, PhD, is professor of biomedical informatics and biomedical engineering with extensive experience working in the field of clinical and translational research informatics. He serves as Vice President for Research Informatics and director of the Vanderbilt University Medical Center Office of Research Informatics. He is very active in the NIH Clinical and Translational Science Award (CTSA) informatics community. In addition to supporting the Vanderbilt University research enterprise, Dr. Harris devised and created REDCap (www.projectredcap.org), a data collection platform that has seen widespread adoption by more than 3600 institutional partners and over 1 million end-users across 131 countries. He also created and runs a national program (www.researchmatch.org) designed to match individuals wishing to volunteer for studies and researchers recruiting patients for studies and trials.  ResearchMatch is serving approximately 150,000 research volunteers and 165 research institutions.  He has extensive experience teaching principles and applied methods for research data management in the form of short courses, symposia, and MOOC format).

Phone
615-322-6688
2525 West End Avenue
Nashville, TN
Tennessee
37203
paul.a.harris@vanderbilt.edu
HarrisPaulPhD

You Chen, PhD, FAMIA

You
Chen
PhD
Associate Professor
Department of Biomedical Informatics

You Chen, Ph.D., is an Associate Professor of Biomedical Informatics at Vanderbilt University Medical Center. He is the director of the Optimization of Health ProcEsses and Networks Laboratory (OHPENLab), which was established to address the growing need for care coordination research and development for the health information technology sector. He is also the co-director of the Health Information Privacy Laboratory (HIPLab).

Dr. Chen’s research is funded through various grants from the National Institutes of Health (NIH), and National Science Foundation (NSF) to construct methodologies and technologies that optimize healthcare process via learning of healthcare systems. 

Dr. Chen’s research foci include medical data mining and machine learning, multi-site based transfer learning, clinical workflow mining, care team identification, disease progression path modeling, predictive analytics, disease profiling and personalized medicine, hospital readmission and patient length of stay analytics, natural language processing, blockchain technology, and health information security and privacy. Dr. Chen’s research has been incorporated into a variety of clinical setting including neonatal care, maternal care, and critical illness patient care. 

Dr. Chen holds a doctoral degree in computer science from the Chinese Academy of Sciences. After his graduation, he has been continuously involving in the field of biomedical informatics at Vanderbilt. His ultimate goal in the field is to leverage medical data (e.g., electronic medical records, genetic data, and public findings), health information technology (e.g., mobile health, wearable devices, Restful API, OAuth) and computational modeling (e.g., graph neural networks, network analysis, temporal modeling) to build patient-centered care to improve their care quality and reduce their healthcare cost.

Phone
2525 West End Avenue
Nashville
Tennessee
37203

Research Directions

Discovering Virtual Provider Interaction Networks in the EHR: Interpretation and Impact on Patient Outcomes

The research goal of this project is to create data mining models (e.g., social network analysis models), statistical models (e.g., logistical regressions), and interpretation strategies (e.g., surveys and focus group interviews) to i) learn interaction networks of care providers from EHR audit logs and EHR medical data; ii) identify interaction patterns contributing to the improved patient outcomes; and iii) translate effective interaction patterns into actionable criteria. We have been doing many types of research to learn virtual care teams and clinical workflows and measure their relationships with patient outcomes (length of stay).

Selected works

Chen Y, Lorenzi NM, Sandberg WS, Wolgast K, Malin BA. Identifying collaborative care teams through electronic medical record utilization patterns. Journal of the American Medical Informatics Association. 2017 Apr 1;24(e1):e111-20.  This work was reported by VUMC reporter at http://news.vumc.org/2016/10/27/study-tracks-makeup-of-vumc-collaborative-care-teams/

Chen Y, Patel MB, McNaughton CD, Malin BA. Interaction patterns of trauma providers are associated with length of stay. Journal of the American Medical Informatics Association. 2018 Feb 22;25(7):790-9. Editor’s choice paper.

Chen Y, Lorenzi N, Nyemba S, Schildcrout JS, Malin B. We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records. International journal of medical informatics. 2014 Jul 1;83(7):495-506.

 

TeamWAS: Team-Wide Association Study - discovering care teams to satisfy a patient’s medical needs

EHR data are rich resources in terms of patients’ medical data and providers’ activities.  By applying data mining and machine learning, these databases can reveal variations of a patient’s medical needs, as well as corresponding care teams dealing with such variations. The goal of this research is to leverage advanced informatics approaches to align care teams with a patient’s medical needs, which will empower healthcare organizations to choose the most appropriate team for a specific patient. The complete story can be found at https://researchfeatures.com/2018/05/30/using-medical-records-improve-care/

Selected works

Chen Y, Kho AN, Liebovitz D, Ivory C, Osmundson S, Bian J, Malin BA. Learning bundled care opportunities from electronic medical records. Journal of biomedical informatics. 2018 Jan 1;77:1-10.

Chen Y, Xie W, Gunter CA, Liebovitz D, Mehrotra S, Zhang H, Malin B. Inferring clinical workflow efficiency via electronic medical record utilization. In AMIA annual symposium proceedings 2015 (Vol. 2015, p. 416). American Medical Informatics Association.

Yan C, Chen Y, Li B, Liebovitz D, Malin B. Learning clinical workflows to identify subgroups of heart failure patients. In AMIA Annual Symposium Proceedings 2016 (Vol. 2016, p. 1248). American Medical Informatics Association.

Discovering Risk Factors and Progression Paths of Perinatal Morbidity

Maternal and neonatal morbidity and mortality have been rising in the United States. To improve pregnancy and neonatal outcomes, we aim to leverage advanced data mining (e.g., word embeddings) and machine learning algorithms (e.g., neural networks) along with data in EHRs to identify risk factors (e.g., lifestyle choices, social determinants) and progression paths of perinatal morbidity.  Specially, we have been working on the identification of risk factors or progression pathways for preterm birth, neonatal encephalopathy, and several maternal morbidity.

Selected works

Li T, Gao C, Yan C, Osmundson S, Malin BA, Chen Y. Predicting neonatal encephalopathy from maternal data in electronic medical records. AMIA Summits on Translational Science Proceedings. 2018;2017:359.

Gao C, Yan C, Osmundson S, Malin BA, Chen Y. A deep learning approach to predict neonatal encephalopathy from electronic health records. The 7th IEEE International Conference on Healthcare Informatics. 2019; In press

Gao C, Osmundson S, Yan X, Malin BA, Chen Y. Leveraging electronic health records to learn the progression path for severe maternal morbidity. MedInfo 2019. In press

Gao C, Osmundson S, Yan X, Malin BA, Chen Y. Learning to identify severe maternal morbidity from electronic health records. MedInfo 2019. In press

Zuber C, Chen Y. A temporal pattern discovery algorithm for predicting neonatal mortality in NICU postoperative patients. MedInfo 2019. In press

 

Transfer learning – developing a common informatics approach to harmonize medical concepts across healthcare organizations

To improve the generalizability and reproducibility of phenotypes learned from EHR data of each individual site, we propose to develop a data mining and machine learning based framework to align phenotypes learned from each site into a common phenotypic space. It will then leverage the common phenotypic space to characterize the medical needs of a patient in each site. 

Selected works

Chen Y, Ghosh J, Bejan CA, Gunter CA, Gupta S, Kho A, Liebovitz D, Sun J, Denny J, Malin B. Building bridges across electronic health record systems through inferred phenotypic topics. Journal of biomedical informatics. 2015 Jun 1;55:82-93. Editor’s choice paper.

Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y. A machine learning-based framework to identify type 2 diabetes through electronic health records. International journal of medical informatics. 2017 Jan 1;97:120-7.

Discovering Effective Hidden Coordination in EHRs to Improve Patient Safety and Outcome in the Neonatal Intensive Care Unit

It has been suggested that virtual care coordination via electronic health record systems (EHRs) can resolve many current neonatal care-related challenges such as poor interpersonal communication, as well as insufficient provider-to-provider handoffs, all of which have the potential to increase medical errors and extend the length of stay. We developed a network-based approach to model virtual care team structures in the neonatal intensive care unit and measure their relationships with non-routine events and length of stay.

Selected works

Kim C, Lehmann C, Schildcrout J, Hatch D, France D, Chen Y. Learning provider interaction networks in the neonatal intensive care unit and measuring their relationship with length of stay. AMIA. Clinical Informatics. 2019. In press.

Application of Blockchain in Health Security and Privacy

Access to accurate and complete medication histories across healthcare institutions enables effective patient care. Health institutions currently rely on centralized systems for sharing medication data. However, there is a lack of efficient mechanisms to ensure that medication histories transferred from one institution to another are accurate, secure and trustworthy. we introduce a decentralized medication management system (DMMS) that leverages the advantages of blockchain to manage medication histories.

Selected works

Li P, Nelson SD, Malin BA, Chen Y. DMMS: A Decentralized Blockchain Ledger for the Management of Medication Histories. Blockchain in Healthcare Today. 2019 Jan 4:2(38).  https://doi.org/10.30953/bhty.v2.38

Learning Opportunities for Drug Repositioning via GWAS and PheWAS Findings

The first goal of this project is to leverage Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) findings to discover new clinical targets for existing drugs. The second goal of this project is to learn disease relational patterns from electronic medical records, and then validate if such associations are genetic related.

Selected works

Yin W, Gao C, Xu Y, Li B, Ruderfer DM, Chen Y. Learning Opportunities for Drug Repositioning via GWAS and PheWAS Findings. AMIA Summits on Translational Science Proceedings. 2018;2017:237.

Insider Threat in the Healthcare Setting

Insider threats in electronic medical record systems are increasingly relied upon to manage sensitive information. Healthcare organizations, for example, have adopted EHRs to enable timely access to patient personal data.  However,  the detail and sensitive nature of the information in EHRs make them attractive to numerous adversaries. This is a concern because the unauthorized dissemination of information from such systems can be catastrophic to both the managing agencies and the individuals to whom the information corresponds. We rely upon social network analysis technologies to detect or prevent patient information from being illegally accessed by hospital employees. We designed community-based anomaly detection systems to detect anomalous insiders insider actions.

Selected works

Chen Y, Nyemba S, Malin B. Detecting anomalous insiders in collaborative information systems. IEEE transactions on dependable and secure computing. 2012 May;9(3):332-44.

Chen Y, Nyemba S, Zhang W, Malin B. Specializing network analysis to detect anomalous insider actions. Security informatics. 2012 Dec;1(1):5.

Chen Y, Malin B. Detection of anomalous insiders in collaborative environments via relational analysis of access logs. In Proceedings of the first ACM conference on Data and application security and privacy 2011 Feb 21 (pp. 63-74). ACM.

Chen Y, Nyemba S, Malin B. Auditing medical records accesses via healthcare interaction networks. In AMIA Annual Symposium Proceedings 2012 (Vol. 2012, p. 93). American Medical Informatics Association.

you.chen@vumc.org
ChenYouPhD

Nunzia Bettinsoli Giuse, MD, FACMI

Nunzia
Bettinsoli
Giuse
MD, FACMI
Professor
Department of Biomedical Informatics
Vice President
Knowledge Management
Professor
Department of Medicine
Adjunct Professor, Meharry Medical College

Dr. Giuse is a pioneering researcher in multiple areas of informatics including medical knowledgebase acquisition and innovative approaches to information provision. She has developed a nationally and internationally recognized training program that focuses on the development of skills that promote true integration of information scientists into key informatics and medical center initiatives.  Dr. Giuse is a leader in patient communication and health literacy research on personalized education strategies for health information and precision medicine.

615-936-1385
2209 Garland Avenue
Nashville, TN
37232
nunzia.giuse@vumc.org
GiuseNunziaBettinsoli MD

Glenn Gobbel, DVM, PhD, MS

Glenn
Gobbel
DVM, PhD, MS
Research Associate Professor
Department of Biomedical Informatics
Research Associate Professor
Department of Medicine

Dr. Gobbel is a Research Associate Professor of Biomedical Informatics with a secondary appointment in Medicine at Vanderbilt University.  He is also a Health Services Research Scientist within the Tennessee Valley Health System (TVHS) VA.  Dr. Gobbel earned his Doctor of Veterinary Medicine (DVM) from the University of Florida and his PhD in biophysics from the University of California, San Francisco.  After working at UCSF and the University of Pittsburgh as a basic researcher in the fields of radiation biology for over 15 years, where his work focused on the pathophysiology of brain injury and mechanisms of repair, Dr. Gobbel completed a fellowship in Medical Informatics at TVHS and also earned an MS in Biomedical Informatics from Vanderbilt University.  His current research work focuses on the development of near-real time natural language processing (NLP) tools for extraction of medical information from clinical documents.  A key interest of Dr. Gobbel is the generation of algorithms that improve the performance and enhance the clinical utility and usability of NLP tools.

Dr. Gobbel has successfully competed for extramural funding from the National Institute of Neurological Disorders (NINDS) and the National Cancer Institute (NCI) in the roles of principal investigator and project leader.  Funded research grants have included awards under the First Award (R29), Exploratory/Development Grant Award (R21), and Program Project Award (P01; Project leader) programs as well as a number of foundation awards.  He is currently a co-investigator on a number of nationally funded projects involving the use of NLP for population health informatics.

615-322-6641
glenn.t.gobbel@vumc.org
GobbelGlennDVM, PhD, MS