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765 results on '"Patzer, Rachel E"'

2. Researching COVID to enhance recovery (RECOVER) adult study protocol: Rationale, objectives, and design

Author

Horwitz, Leora I, Thaweethai, Tanayott, Brosnahan, Shari B, Cicek, Mine S, Fitzgerald, Megan L, Goldman, Jason D, Hess, Rachel, Hodder, SL, Jacoby, Vanessa L, Jordan, Michael R, Krishnan, Jerry A, Laiyemo, Adeyinka O, Metz, Torri D, Nichols, Lauren, Patzer, Rachel E, Sekar, Anisha, Singer, Nora G, Stiles, Lauren E, Taylor, Barbara S, Ahmed, Shifa, Algren, Heather A, Anglin, Khamal, Aponte-Soto, Lisa, Ashktorab, Hassan, Bassett, Ingrid V, Bedi, Brahmchetna, Bhadelia, Nahid, Bime, Christian, Bind, Marie-Abele C, Black, Lora J, Blomkalns, Andra L, Brim, Hassan, Castro, Mario, Chan, James, Charney, Alexander W, Chen, Benjamin K, Chen, Li Qing, Chen, Peter, Chestek, David, Chibnik, Lori B, Chow, Dominic C, Chu, Helen Y, Clifton, Rebecca G, Collins, Shelby, Costantine, Maged M, Cribbs, Sushma K, Deeks, Steven G, Dickinson, John D, Donohue, Sarah E, Durstenfeld, Matthew S, Emery, Ivette F, Erlandson, Kristine M, Facelli, Julio C, Farah-Abraham, Rachael, Finn, Aloke V, Fischer, Melinda S, Flaherman, Valerie J, Fleurimont, Judes, Fonseca, Vivian, Gallagher, Emily J, Gander, Jennifer C, Gennaro, Maria Laura, Gibson, Kelly S, Go, Minjoung, Goodman, Steven N, Granger, Joey P, Greenway, Frank L, Hafner, John W, Han, Jenny E, Harkins, Michelle S, Hauser, Kristine SP, Heath, James R, Hernandez, Carla R, Ho, On, Hoffman, Matthew K, Hoover, Susan E, Horowitz, Carol R, Hsu, Harvey, Hsue, Priscilla Y, Hughes, Brenna L, Jagannathan, Prasanna, James, Judith A, John, Janice, Jolley, Sarah, Judd, SE, Juskowich, Joy J, Kanjilal, Diane G, Karlson, Elizabeth W, Katz, Stuart D, Kelly, J Daniel, Kelly, Sara W, Kim, Arthur Y, Kirwan, John P, Knox, Kenneth S, Kumar, Andre, Lamendola-Essel, Michelle F, Lanca, Margaret, Lee-lannotti, Joyce K, Lefebvre, R Craig, and Levy, Bruce D

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Clinical Research, Emerging Infectious Diseases, Infectious Diseases, Infection, Good Health and Well Being
Abstract

Abstract: Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility– and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross– validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.

Published
2023

12. Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning

Author

Xu, Liyan, Hogan, Julien, Patzer, Rachel E., and Choi, Jinho D.

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant. We face the challenges of developing robust models on a small dataset where each document may consist of over 10K tokens with full of noise including tabular text and task-irrelevant sentences. We first experiment four types of encoders to empirically decide the best document representation, and then apply reinforcement learning to remove noisy text from the long documents, which models the noise extraction process as a sequential decision problem. Our results show that the old bag-of-words encoder outperforms deep learning-based encoders on this task, and reinforcement learning is able to improve upon baseline while pruning out 25% text segments. Our analysis depicts that reinforcement learning is able to identify both typical noisy tokens and task-specific noisy text., Comment: Accepted to the ACL Workshop on Biomedical Natural Language Processing, BioNLP 2020

Published
2020

16. Defining the Need for Causal Inference to Understand the Impact of Social Determinants of Health: A Primer on Behalf of the Consortium for the Holistic Assessment of Risk in Transplantation (CHART)

Author

Bhavsar, Nrupen A., Patzer, Rachel E., Taber, David J., Ross-Driscoll, Katie, Deierhoi Reed, Rhiannon, Caicedo-Ramirez, Juan C., Gordon, Elisa J., Matsouaka, Roland A., Rogers, Ursula, Webster, Wendy, Adams, Andrew, Kirk, Allan D., and McElroy, Lisa M.

Published
2023
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19. Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission

Author

Shin, Bonggun, Hogan, Julien, Adams, Andrew B., Lynch, Raymond J., Patzer, Rachel E., and Choi, Jinho D.

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretable features from unstructured data, they often require a large amount of training data. However, many tasks in medical domains inherently consist of small sample data with lengthy documents; for a kidney transplant as an example, data from only a few thousand of patients are available and each patient's document consists of a couple of millions of words in major hospitals. Thus, complex DL methods cannot be applied to these kinds of domains. In this paper, we present a comprehensive ensemble model using vector space modeling and topic modeling. Our proposed model is evaluated on the readmission task of kidney transplant patients and improves 0.0211 in terms of c-statistics from the previous state-of-the-art approach using structured data, while typical DL methods fail to beat this approach. The proposed architecture provides the interpretable score for each feature from both modalities, structured and unstructured data, which is shown to be meaningful through a physician's evaluation., Comment: 4 pages, IEEE BHI 2019

Published
2019

20. Stakeholders’ perspectives on transplant metrics: the 2022 Scientific Registry of Transplant Recipients’ consensus conference

Author

Snyder, Jon J., Schaffhausen, Cory R., Hart, Allyson, Axelrod, David A., Dils, Dorrie, Formica, Richard N., Jr., Gaber, A. Osama, Hunt, Heather F., Jones, Jennifer, Mohan, Sumit, Patzer, Rachel E., Pinney, Sean P., Ratner, Lloyd E., Slaker, Dirk, Stewart, Darren, Stewart, Zoe A., Van Slyck, Sean, Kasiske, Bertram L., Hirose, Ryutaro, and Israni, Ajay K.

Published
2023
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30. Influence of incomplete death information on cumulative risk estimates in US claims data.

Author

Barberio, Julie, Naimi, Ashley I, Patzer, Rachel E, Kim, Christopher, Hernandez, Rohini K, Brookhart, M Alan, Gilbertson, David, Bradbury, Brian D, and Lash, Timothy L

Subjects
CARDIOVASCULAR disease related mortality, MORTALITY risk factors, RISK assessment, CARDIOVASCULAR diseases, HEALTH insurance reimbursement, RESEARCH funding, TELMISARTAN, RAMIPRIL, AGE distribution, DESCRIPTIVE statistics, CARDIOVASCULAR diseases risk factors, LONGITUDINAL method, CONFIDENCE intervals
Abstract

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event—equivalent to other withdrawal reasons—rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Published
2024
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