EDBT 2026 Demo / reviewers in the wild / expert

Andrew E. Williams

dblp:248/7682 · also Andrew Williams 0005 · DBLP profile ↗
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9ranked-venue papers
0as first author
6since 2021 · last 2025
0000-0002-0692-412XORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 9 · 6 since 2021
YearPublicationVenuePosition
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.19
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.7
2023 Representing and utilizing clinical textual data for real world studies: An OHDSI approach
Vipina Kuttichi Keloth, Juan M. Banda, Michael J. Gurley, Paul M. Heider, Georgina Kennedy, Timothy A. Miller, Karthik Natarajan, Olga V. Patterson, Yifan Peng 0002, Kalpana Raja, Ruth M. Reeves, Masoud Rouhizadeh, Jianlin Shi, Yanshan Wang, Wei-Qi Wei, Andrew E. Williams, Rui Zhang 0028, Rimma Belenkaya, Christian G. Reich, Clair Blacketer, Patrick B. Ryan, George Hripcsak, Noémie Elhadad, Hua Xu 0001
 J. Biomed. Informatics19
2022 Feasibility of Linking Area Deprivation Index Data to the OMOP Common Data Model
Xinzhuo Jiang, Maura Beaton, Jake Gillberg, Andrew E. Williams, Karthik Natarajan
 AMIA4
2021 Ten principles for data sharing and commercialization
abstract
Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.
Curtis L. Cole, Soumitra Sengupta, Sarah Collins Rossetti, David K. Vawdrey, Michael Halaas, Thomas M. Maddox, Geoff Gordon, Trushna Dave, Philip R. O. Payne, Andrew E. Williams, Deborah Estrin
 J. Am. Medical Informatics Assoc.10
2021 The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
abstract
OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
Melissa A. Haendel, Christopher G. Chute, Tellen D. Bennett, David Eichmann, Justin Guinney, Warren A. Kibbe, Philip R. O. Payne, Emily R. Pfaff, Peter N. Robinson, Joel H. Saltz, Heidi Spratt, Christine Suver, John Wilbanks, Adam B. Wilcox, Andrew E. Williams, Chunlei Wu, Clair Blacketer, Robert L. Bradford, James J. Cimino, Marshall Clark, Evan W. Colmenares, Patricia A. Francis, Davera Gabriel, Alexis Graves, Raju Hemadri, Stephanie S. Hong, George Hripcsak, Dazhi Jiao, Jeffrey G. Klann, Kristin Kostka, Adam M. Lee, Harold P. Lehmann, Lora Lingrey, Robert T. Miller, Michele Morris, Shawn N. Murphy, Karthik Natarajan, Matvey Palchuk, Usman Sheikh, Harold R. Solbrig, Shyam Visweswaran, Anita Walden, Kellie M. Walters, Griffin M. Weber, Xiaohan Tanner Zhang, Richard L. Zhu, Benjamin R. C. Amor, Andrew T. Girvin, Amin Manna, Nabeel Qureshi, Michael G. Kurilla, Samuel G. Michael, Lili M. Portilla, Joni L. Rutter, Christopher P. Austin, Kenneth R. Gersing
 J. Am. Medical Informatics Assoc.15
2020 Data Quality Assessment of Laboratory Data
Vojtech Huser, Clair Blacketer, Karthik Natarajan, Robert T. Miller, Andrew E. Williams, Selva Muthu Kumaran Sathappan, José D. Posada, Nigam H. Shah
 AMIA5
2020 Characterizing database granularity using SNOMED-CT hierarchy
Anna Ostropolets, Christian G. Reich, Patrick B. Ryan, Chunhua Weng, Anthony Molinaro, Frank J. DeFalco, Jitendra Jonnagaddala, Siaw-Teng Liaw, Hokyun Jeon, Rae Woong Park, Matthew E. Spotnitz, Karthik Natarajan, Kristin Kostka, George Argyriou, Robert T. Miller, Andrew E. Williams, Evan P. Minty, José D. Posada, George Hripcsak
 AMIA16
2019 HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model
abstract
Systematic application of observational data to the understanding of impacts of cancer treatments requires detailed information models allowing meaningful comparisons between treatment regimens. Unfortunately, details of systemic therapies are scarce in registries and data warehouses, primarily due to the complex nature of the protocols and a lack of standardization. Since 2011, we have been creating a curated and semi-structured website of chemotherapy regimens, HemOnc.org. In coordination with the Observational Health Data Sciences and Informatics (OHDSI) Oncology Subgroup, we have transformed a substantial subset of this content into the OMOP common data model, with bindings to multiple external vocabularies, e.g., RxNorm and the National Cancer Institute Thesaurus. Currently, there are >73,000 concepts and >177,000 relationships in the full vocabulary. Content related to the definition and composition of chemotherapy regimens has been released within the ATHENA tool (athena.ohdsi.org) for widespread utilization by the OHDSI membership. Here, we describe the rationale, data model, and initial contents of the HemOnc vocabulary along with several use cases for which it may be valuable.
Jeremy L. Warner, Dmitry Dymshyts, Christian G. Reich, Michael J. Gurley, Harry Hochheiser, Zachary H. Moldwin, Rimma Belenkaya, Andrew E. Williams, Peter C. Yang
 J. Biomed. Informatics8