Abu-El-Rub, Noor
Mayo Clinic
Center for Medical Informatics and Enterprise Analytics, University of Kansas Medical Center, Kansas City, Kansas.
Citations 2
An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)
Liu, Sijia , Wen, Andrew , Wang, Liwei , Fu, Sunyang , Miller, Robert , Williams, Andrew E. , Harris, Daniel R. , Liu, Mei , Abu-el-rub, Noor , Schutte, Dalton, Zhang, Rui , Osborne, John D. , He, Yongqun , Topaloĝlu, Ümit , Hong, Stephanie , Schaffter, Thomas , Fuentes, Rafael , Xu, Hua , He, Huan , Fu, Sunyang , Williams, Andrew , Harris, Daniel , Kavuluru, Ramakanth , Liu, Mei , Zhang, Rui , Rouhizadeh, Masoud , Osborne, John D , He, Yongqun , Topaloglu, Umit , Saltz, Joel H , Schaffter, Thomas , Pfaff, Emily , Chute, Christopher G , Haendel, Melissa , Szolovits, Peter , Xu, Hua , Liu, Hongfang , Liu, Sijia , Wen, Andrew , Abu-El-Rub, Noor , He, Huan , Saltz, Joel , Chute, Christopher G. , Duong, Timothy Q. , Haendel, Melissa , Liu, Hongfang
Journal of the American Medical Informatics Association | August 8, 2023 | Volume: 30 | Issue: 12 | Pages: 2036-2040
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.
Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin
Piasecki, Thomas M. , Smith, Stevens S. , Baker, Timothy B. , Fiore, Michael C. , Adsit, Robert T. , Bolt, Daniel M. , Conner, Karen L. , Bernstein, Steven L. , Eng, Oliver S. , Lazuk, David , Gonzalez, Alec , Hayes-Birchler, Todd , Jorenby, Douglas E. , D’Angelo, Heather , Kirsch, Julie A. , Williams, Brian , Kent, Sean , Kim, Hanna , Lubanski, Stanley A. , Cai, Yuxin , Kashyap, Nitu , Mathew, Jomol , McMahan, Gabriel , Rolland, Betsy , Tindle, Hilary A. , Warren, Graham , Abu-el-rub, Noor , An, Lawrence C. , Boyd, Andrew D. , Brunzell, Darlene H., Carrillo, Victor A. , Chen, Li‐Shiun , Davis, James M. , Deshmukh, Vikrant , Dilip, Deepika , Ha, Patrick K. , Jose, Thulasee , Klass, Elizabeth , Lui, Michelle , Poon, Chester , Tong, Elisa K. , Wilson, Karen M. , Theobald, Wendy E. , Nolan, Margaret B , Piasecki, Thomas M , Smith, Stevens S , Baker, Timothy B , Fiore, Michael C , Adsit, Robert T , Bolt, Daniel M , Conner, Karen L , Bernstein, Steven L , Eng, Oliver , Lazuk, David , Gonzalez, Alec , Hayes-Birchler, Todd , Jorenby, Douglas E , D'Angelo, Heather , Kirsch, Julie A , Williams, Brian S , Kent, Sean , Kim, Hanna , Lubanski, Stanley A , Yu, Menggang , Suk, Youmi , Cai, Yuxin , Kashyap, Nitu , Mathew, Jomol , McMahan, Gabriel , Rolland, Betsy , Tindle, Hilary A , Warren, Graham W , Abu-El-Rub, Noor , An, Lawrence C , Boyd, Andrew D , Carrillo, Victor A , Chen, Li-Shiun , Davis, James M , Deshmukh, Vikrant G , Dilip, Deepika , Goldstein, Adam O , Ha, Patrick K , Iturrate, Eduardo , Jose, Thulasee , Khanna, Niharika , King, Andrea , Klass, Elizabeth , Lui, Michelle , Mermelstein, Robin J , Poon, Chester , Tong, Elisa , Wilson, Karen M , Theobald, Wendy E , Slutske, Wendy S , Nolan, Margaret , Yu, Menggang
Cancer Epidemiology, Biomarkers & Prevention | August 15, 2022 | Volume: 32 | Issue: 1 | Pages: 12-21
Abstract Background: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics. Methods: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined. Results: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46–1.70] and 1.04 (95% CI, 0.96–1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53–0.90). Conclusions: Current cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types. Impact: This study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3