12/4/2023 0 Comments Information entropy ann arborGSO is supported by NIH grants P30ES017885 and U24CA210967. YL is supported by NCATS U01TR003528 and NLM 1R01LM013337. JK is supported by National Center for Advancing Translational Sciences (NCATS) UL1TR001857. For access to the data, please email ZSH is supported by National Institutes of Health (NIH) National Library of Medicine (NLM) T15 LM007092. Data may be made available to affiliated researchers given the MGB IRB approval. Therefore, it cannot currently be made available directly. The full patient-level dataset contains sensitive and potentially re-identifiable data. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The computer scripts used for distributed learning representation and for subsequent analysis are available at. Received: DecemAccepted: JPublished: July 25, 2023Ĭopyright: © 2023 Strasser et al. PLOS Digit Health 2(7):Įditor: Yuan Lai, Tsinghua University, CHINA (2023) A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework. Our augmented definitions can be used to identify potential long COVID patients from the structured data in the electronic health records.Ĭitation: Strasser ZH, Dagliati A, Shakeri Hossein Abad Z, Klann JG, Wagholikar KB, Mesa R, et al. Using this method, we obtained estimates of hospitalized COVID-19 patients exhibiting dyspnea, fatigue, or joint pain three months post-hospitalization. We validated the accuracy of our models by manual patient chart reviews. We built models using diverse electronic health record data (diagnosis, medication, procedure, and laboratory orders) gathered from several hospital systems to better identify patients showing potential signs of long COVID. Our study utilized a novel representation learning approach to navigate these challenges. This complexity was heightened prior to the introduction of the long COVID billing code since there was not a clear consensus for how to code patients with ongoing symptoms. Additionally, some health records may only hint at new or persistent symptoms through a new prescription, a procedure, or a laboratory order. For instance, different providers may emphasize different aspects of a patient’s condition such as shortness of breath versus the underlying cause of the symptom (i.e., COVID-19, congestive heart failure or chronic obstructive pulmonary disease). We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.Īnalyzing long COVID using the healthcare system’s electronic health records presents unique challenges due to variable coding practices by healthcare providers and medical coders. We estimated that 25 percent (CI 95%: 6–48), 11 percent (CI 95%: 6–15), and 13 percent (CI 95%: 8–17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We implemented the distributed representation learning technique to augment PASC definitions. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between Maand February 28, 2021. These improved definitions were then used for estimating PASC among hospitalized patients. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC).
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