Browsing by Subject "EHR data"

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    Evaluating Meta‐Learners to Analyze Treatment Heterogeneity in Survival Data: Application to Electronic Health Records of Pediatric Asthma Care in COVID‐19 Pandemic
    (Wiley, 2025) Bo, Na; Jeong, Jong-Hyeon; Forno, Erick; Ding, Ying; Pediatrics, School of Medicine
    An important aspect of precision medicine focuses on characterizing diverse responses to treatment due to unique patient characteristics, also known as heterogeneous treatment effects (HTE) or individualized treatment effects (ITE), and identifying beneficial subgroups with enhanced treatment effects. Estimating HTE with right-censored data in observational studies remains challenging. In this paper, we propose a pseudo-ITE-based framework for analyzing HTE in survival data, which includes a group of meta-learners for estimating HTE, a variable importance metric for identifying predictive variables to HTE, and a data-adaptive procedure to select subgroups with enhanced treatment effects. We evaluate the finite sample performance of the framework under various observational study settings. Furthermore, we applied the proposed methods to analyze the treatment heterogeneity of a written asthma action plan (WAAP) on time-to-ED (Emergency Department) return due to asthma exacerbation using a large asthma electronic health records dataset with visit records expanded from pre- to post-COVID-19 pandemic. We identified vulnerable subgroups of patients with poorer asthma outcomes but enhanced benefits from WAAP and characterized patient profiles. Our research provides valuable insights for healthcare providers on the strategic distribution of WAAP, particularly during disruptive public health crises, ultimately improving the management and control of pediatric asthma.
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    The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
    (Oxford University Press, 2021) Haendel, Melissa A.; Chute, Christopher G.; Bennett, Tellen D.; Eichmann, David A.; Guinney, Justin; Kibbe, Warren A.; Payne, Philip R. O.; Pfaff, Emily R.; Robinson, Peter N.; Saltz, Joel H.; Spratt, Heidi; Suver, Christine; Wilbanks, John; Wilcox, Adam B.; Williams, Andrew E.; Wu, Chunlei; Blacketer, Clair; Bradford, Robert L.; Cimino, James J.; Clark, Marshall; Colmenares, Evan W.; Francis, Patricia A.; Gabriel, Davera; Graves, Alexis; Hemadri, Raju; Hong, Stephanie S.; Hripscak, George; Jiao, Dazhi; Klann, Jeffrey G.; Kostka, Kristin; Lee, Adam M.; Lehmann, Harold P.; Lingrey, Lora; Miller, Robert T.; Morris, Michele; Murphy, Shawn N.; Natarajan, Karthik; Palchuk, Matvey B.; Sheikh, Usman; Solbrig, Harold; Visweswaran, Shyam; Walden, Anita; Walters, Kellie M.; Weber, Griffin M.; Zhang, Xiaohan Tanner; Zhu, Richard L.; Amor, Benjamin; Girvin, Andrew T.; Manna, Amin; Qureshi, Nabeel; Kurilla, Michael G.; Michael, Sam G.; Portilla, Lili M.; Rutter, Joni L.; Austin, Christopher P.; Gersing, Ken R.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    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.