Umit Topaloglu

EDBT 2026 Demo / reviewers in the wild / expert

dblp:32/4989 · DBLP profile ↗

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23ranked-venue papers

1as first author

7since 2021 · last 2025

0000-0002-3241-8773ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 21 · 7 since 2021Human-computer interaction and ubiquitous computing · 2Artificial intelligence and machine learning · 1 · 1 first-authorSecurity and privacy · 1

Year	Publication	Venue	Position
2025	National COVID Cohort Collaborative data enhancements: a path for expanding common data models abstract OBJECTIVE: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include long COVID specialty clinic indicator; Admission, Discharge, and Transfer transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation. MATERIALS AND METHODS: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way. RESULTS: As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline. DISCUSSION: The use of common data models is critical to the success of N3C; however, these data models cannot account for all needs. Project-driven data enhancement is required. This should be done in a standardized way in alignment with common data model specifications. Our approach offers a useful pathway for enhancing data to improve fit for purpose. CONCLUSION: In this initiative, we rapidly produced project-specific data modeling guidance and documentation in support of long COVID research while maintaining a commitment to terminology standards and harmonized data. Kellie M. Walters, Marshall Clark, Sofia Dard, Stephanie S. Hong, Elizabeth Kelly, Kristin Kostka, Adam M. Lee, Robert T. Miller, Michele Morris, Matvey Palchuk, Emily R. Pfaff, Adam B. Wilcox, Alexis Graves, Alfred Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A Sharathkumar, Benjamin R. C. Amor, Benjamin Bates, Brian Hendricks, Caleb Alexander, Carolyn T. Bramante, Cavin Ward-Caviness, Charisse R. Madlock-Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David Eichmann, Diego Mazzotti, Don Brown, Eilis A. Boudreau, Elaine L. Hill, Elizabeth Zampino, Emily Carlson Marti, Evan French, Farrukh M. Koraishy, Federico Mariona, Fred W. Prior, George Sokos, Greg Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar Mehta, Hythem Sidky, J. W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Johanna Loomba, John Buse, Jomol P. Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili M. Portilla, Mariam Deacy, Mark M. Bissell, Mary Emmett, Mary Morrison Saltz, Melissa A. Haendel, Meredith C. B. Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter N. Robinson, Philip R. O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Saiju Pyarajan, Samuel G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang	J. Am. Medical Informatics Assoc.	120
2024	Adapting the open-source Gen3 platform and kubernetes for the NIH HEAL IMPOWR and MIRHIQL clinical trial data commons: Customization, cloud transition, and optimization abstract OBJECTIVE: This study aims to provide the decision-making framework, strategies, and software used to successfully deploy the first combined chronic pain and opioid use data clinical trial data commons using the Gen3 platform. MATERIALS AND METHODS: The approach involved adapting the open-source Gen3 platform and Kubernetes for the needs of the NIH HEAL IMPOWR and MIRHIQL networks. Key steps included customizing the Gen3 architecture, transitioning from Amazon to Google Cloud, adapting data ingestion and harmonization processes, ensuring security and compliance for the Kubernetes environment, and optimizing performance and user experience. RESULTS: The primary result was a fully operational IMPOWR data commons built on Gen3. Key features include a modular architecture supporting diverse clinical trial data types, automated processes for data management, fine-grained access control and auditing, and researcher-friendly interfaces for data exploration and analysis. DISCUSSION: The successful development of the Wake Forest IDEA-CC data commons represents a significant milestone for chronic pain and addiction research. Harmonized, FAIR data from diverse studies can be discovered in a secure, scalable repository. Challenges remain in long-term maintenance and governance, but the commons provides a foundation for accelerating scientific progress. Key lessons learned include the importance of engaging both technical and domain experts, the need for flexible yet robust infrastructure, and the value of building on established open-source platforms. CONCLUSION: The WF IDEA-CC Gen3 data commons demonstrates the feasibility and value of developing a shared data infrastructure for chronic pain and opioid use research. The lessons learned can inform similar efforts in other clinical domains. Meredith C. B. Adams, Colin Griffin, Hunter Adams, Stephen Bryant, Robert W. Hurley, Umit Topaloglu	J. Biomed. Informatics	6
2023	An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C) abstract Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts. Sijia Liu 0002, Andrew Wen, Liwei Wang 0010, Sunyang Fu, Robert T. Miller, Andrew E. Williams, Daniel R. Harris, Ramakanth Kavuluru, Noor Abu-El-Rub, Dalton Schutte, Rui Zhang 0028, Masoud Rouhizadeh, John D. Osborne, Yongqun He, Umit Topaloglu, Stephanie S. Hong, Joel H. Saltz, Thomas Schaffter, Emily R. Pfaff, Christopher G. Chute, Tim Duong, Melissa A. Haendel, Rafael Fuentes, Peter Szolovits, Hua Xu 0001	J. Am. Medical Informatics Assoc.	17
2022	VOC-alarm: mutation-based prediction of SARS-CoV-2 variants of concern abstract SUMMARY: Mutation is the key for a variant of concern (VOC) to overcome selective pressures, but this process is still unclear. Understanding the association of the mutational process with VOCs is an unmet need. Motivation: Here, we developed VOC-alarm, a method to predict VOCs and their caused COVID surges, using mutations of about 5.7 million SARS-CoV-2 complete sequences. We found that VOCs rely on lineage-level entropy value of mutation numbers to compete with other variants, suggestive of the importance of population-level mutations in the virus evolution. Thus, we hypothesized that VOCs are a result of a mutational process across the globe. Results: Analyzing the mutations from January 2020 to December 2021, we simulated the mutational process by estimating the pace of evolution, and thus divided the time period, January 2020-March 2022, into eight stages. We predicted Alpha, Delta, Delta Plus (AY.4.2) and Omicron (B.1.1.529) by their mutational entropy values in the Stages I, III, V and VII with accelerated paces, respectively. In late November 2021, VOC-alarm alerted that Omicron strongly competed with Delta and Delta plus to become a highly transmissible variant. Using simulated data, VOC-alarm also predicted that Omicron could lead to another COVID surge from January 2022 to March 2022. AVAILABILITY AND IMPLEMENTATION: Our software implementation is available at https://github.com/guangxujin/VOC-alarm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Vithal Madhira, Umit Topaloglu, Guangxu Jin	Bioinform.	5
2021	Pragmatic Patient Reported Outcomes via EHR Patient Portal vs. Telephone: A Pilot Randomized Controlled Trial among Adults with Epilepsy and Anxiety or Depression Symptoms Heidi M. Munger Clary, Beverly M. Snively, Umit Topaloglu, Pamela Duncan, James Kimball, Halley Alexander, Gretchen A. Brenes	AMIA	3
2021	COVID-19 Mortality Prediction among Patients with Cancer Using a Large National Cohort Noha Sharafeldin, Vithal Madhira, Katie R. Bradwell, Qianqian Song 0002, Benjamin Bates, Yu R. Shao, Jing Su 0003, Alfred Anzalone, Timothym Bergquist, Sarah Cutrona, Ben S. Gerber, Peter N. Robinson, Justin Guinney, Umit Topaloglu	AMIA	15
2021	Federated Learning in Healthcare is the Future, But the Problems Are Contemporary Mustafa Y. Topaloglu, Elisabeth M. Morrell, Umit Topaloglu	WEBIST	3
2020	Understanding facilitators and barriers to development and adoption of EHR-to-eCRF technology in multi-site clinical research Bhargav Adagarla, Maryam Y. Garza, Karan R. Kumar, Umit Topaloglu, Anita Walden, Kanecia Zimmerman, Meredith Nahm, Eric L. Eisenstein	AMIA	4
2020	Extraction of Diagnosis codes from Health Reports using available NLP tools for a performant implementation in an Academic Medical Center Arnav Bhandari, Michael Horvath, Greg Kucera, Umit Topaloglu	AMIA	4
2020	Evaluating the Coverage of the HL7® FHIR® Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies Maryam Y. Garza, Michael W. Rutherford, Sahiti Myneni, Susan H. Fenton, Anita Walden, Umit Topaloglu, Eric L. Eisenstein, Karan R. Kumar, Kanecia Zimmerman, Mitra Rocca, Sam Hume, Meredith Nahm	AMIA	6
2020	Leveraging Single-cell Data through Graph-based Artificial Intelligence Qianqian Song 0002, Umit Topaloglu, Jing Su 0003, Wei Zhang 0297	AMIA	2
2019	Developing Biomedical Informatics Capability to Support Learning Health System: Challenges and Potential Solutions Martin S. Kohn, Umit Topaloglu, Metin Nafi Gürcan, Brian J. Wells, Ajay Dharod	AMIA	2
2015	An Analytics Approach for Adverse Drug Event Discovery Mohammadreza Rezaie, Kenji Yoshigoe, Umit Topaloglu	AMIA	3
2014	Social network analysis of biomedical research collaboration networks in a CTSA institution Jiang Bian 0001, Mengjun Xie, Umit Topaloglu, Teresa Hudson, Hari Eswaran, William R. Hogan	J. Biomed. Informatics	3
2013	Understanding biomedicai research collaborations through social network analysis: A case study abstract A recent surge of research on social networks and their characteristics has attracted an increasing amount of interests from the community of biomedicine and biomedical informatics. Social network analysis (SNA) methods have been regarded as an effective tool to assess inter- and intra-institution research collaborations in the Clinical Translational Science Award (CTSA) community. In this paper, we present a case study of SNA on the research collaboration networks (RCNs) at the University of Arkansas for Medical Sciences (UAMS) - a CTSA institution. We have applied graph theoretical analyses to the RCNs prior to and after the CTSA award at UAMS. By virtue of quantitative measures, we have obtained valuable insights into the network dynamics and topological characteristics of the research environment. Moreover, through observing the temporal evolution of the RCNs at UAMS, we are able to demonstrate the effectiveness of the CTSA program and its important role in promoting trans-disciplinary collaborative research within an institution. Jiang Bian 0001, Mengjun Xie, Umit Topaloglu, Teresa Hudson, William R. Hogan	BIBM	3
2013	SIM: A smartphone-based identity management framework and its application to Arkansas trauma image repository abstract Secure and convenient user identity management is particularly important to the success of EMR, EHR, and PHR systems. Unfortunately, widely-used identity management mechanisms that solely rely on username/password are inadequate to meet the strong security and privacy requirements for protecting sensitive user information and medical data. Two-factor authentication approaches that are more convenient and user friendly than existing solutions have been given top priority in the healthcare sector where the majority of healthcare practitioners and patients are not tech-savvy. In this paper, we present a smartphone-based identity management framework-SIM-to enhance the security and usability of user identity management in healthcare information systems. SIM leverages the popularity and computational power of smartphone. Within the SIM framework, a person employs a smartphone to centrally store and manage her identity credentials and authenticates herself to healthcare applications using two-factor authentication without typing any identity credentials. Moreover, SIM provides patients with a patient-controlled authorization mechanism to help patients manage the accesses to their PHRs in a secure and convenient manner. Using an existing EMR system-Arkansas Trauma Image Repository-as an example, we demonstrate that SIM can be applied to a real-world healthcare information system to enhance its protection of user credentials and sensitive information. Mengjun Xie, Umit Topaloglu, Thomas Powell 0003, Jiang Bian 0001	BIBM	2
2009	Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images abstract Color is the most critical information for assessing histological images. However, in literature, there is no standard color space in which a particular color points are represented for computer vision tasks. In this paper, we evaluated 11 color models with three different learning schemas for their performance in classifying tumor-related colors. The color models we studied are CIELAB, CIELUV, CIEXYZ, CMY, CMYK, HSL, HSV, Hunter-LAB, NRGB, RGB, and SCT. With 11 color models, prediction accuracies of three well-known classifiers, namely SVMs, C4.5, and Naive Bayes, are statistically compared on a large dataset of 3494 Hematoxylin and Eosin (HE) stained histopathologic images. Surprisingly, experiment results show that in contrast to general assumptions, there is no single model that is better than others in every case. However, C4.5 outperformed other two classifiers by obtaining average F-measure of 0.9989. Of 11 color models, we suggest the pair of C4.5-SCT as the most accurate classification framework for tumor identification in HE stained histological images. Mutlu Mete, Umit Topaloglu	CIBCB	2
2009	Design of a Lattice-based Access Control Scheme abstract We survey the literature for access control schemes in a user hierarchy. Some schemes have already been shown to be insecure or incorrect. Many schemes assume very restrictive subordinating relationships existing in a hierarchy where users are grouped into partially ordered relationships without taking resources into consideration. We believe that a practical access control scheme should support access control in a lattice where users and resources are both together grouped into partially ordered relationships. In this paper, we present a scheme to achieve this goal. We also study existing schemes for their efficiency and performance. Based on the results of the study, we design an efficient scheme to support dynamic key management. Chia-Chu Chiang, Coskun Bayrak, Remzi Seker, Umit Topaloglu, Rustu Murat Demirer, Nasrola Samadi, Suleyman Tek, Jiang Bian 0001, GuangXu Zhou	SMC	4
2009	Automatic identification of angiogenesis in double stained images of liver tissue abstract BACKGROUND: To grow beyond certain size and reach oxygen and other essential nutrients, solid tumors trigger angiogenesis (neovascularization) by secreting various growth factors. Based on this fact, several researches proposed that density of newly formed vessels correlate with tumor malignancy. Vessel density is known as a true prognostic indicator for several types of cancer. However, automated quantification of angiogenesis is still in its primitive stage, and deserves more intelligent methods by taking advantages accruing from novel computer algorithms. RESULTS: The newly introduced characteristics of subimages performed well in identification of region-of-angiogenesis. The proposed technique was tested on 522 samples collected from two high-resolution tissues. Having 0.90 overall f-measure, the results obtained with Support Vector Machines show significant agreement between automated framework and manual assessment of microvessels. CONCLUSION: This study introduces a new framework to identify angiogenesis to measure microvessel density (MVD) in digitalized images of liver cancer tissues. The objective is to recognize all subimages having new vessel formations. In addition to region based characteristics, a set of morphological features are proposed to differentiate positive and negative incidences. Mutlu Mete, Leah Hennings, Horace J. Spencer III, Umit Topaloglu	BMC Bioinform.	4
2008	Off-the-Record Secure Chat Room Jiang Bian 0001, Remzi Seker, Umit Topaloglu, Coskun Bayrak	WEBIST (1)	3
2008	Secure mobile agent execution in virtual environment Umit Topaloglu, Coskun Bayrak	Auton. Agents Multi Agent Syst.	1
2006	A Child's Story to Illustrate Automated Reasoning Systems Using Opportunity and History James D. Jones, Hemant Joshi, Umit Topaloglu, Eric Nelson	ISI	3
2006	"Who Stole the Bat?" Deception Detection on the Basis of Actions abstract In work very relevant to national defense and homeland security, this paper describes software that performs symbolic reasoning (in particular., logical inference) to detect deception on the basis of actions. This is in sharp contrast with present approaches that detect deception based on physiological factors, as well as on verbal and non-verbal cues. Our approach attempts to model agents and their actions. This is achieved in a logic programming framework using a theory of agents, a theory of actions, and a theory of reasoning with respect to time. As a test case, a children's mystery is analyzed and implemented. The software correctly reasons about who the potential suspects are, and ultimately, correctly identifies the chief culprit. Further, it can correctly introspect with regard to previously held beliefs. Our approach is novel in that it attempts to identify deception on the basis of actions, and it does so in a high level, symbolic reasoning framework James D. Jones, Hemant Joshi, Umit Topaloglu, Eric Nelson	SMC	3