Posts Tagged ‘Deep Learning’
Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit.
Link: https://doi.org/10.1093/jamia/ocab252 Authors: Oh, Wonsuk; Jayaraman, Pushkala; Sawant, Ashwin S; Chan, Lili; Levin, Matthew A; Charney, Alexander W; Kovatch, Patricia; Glicksberg, Benjamin S; Nadkarni, Girish N Abstract: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes,…
Read MoreMulti-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction.
Link: https://doi.org/10.1038/s43856-023-00240-w Authors: Vaid, Akhil; Argulian, Edgar; Lerakis, Stamatios; Beaulieu-Jones, Brett K; Krittanawong, Chayakrit; Klang, Eyal; Lampert, Joshua; Reddy, Vivek Y; Narula, Jagat; Nadkarni, Girish N; Glicksberg, Benjamin S Abstract: Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to…
Read MoreAutoencoders for sample size estimation for fully connected neural network classifiers.
Link: https://doi.org/10.1038/s41746-022-00728-0 Authors: Gulamali, Faris F; Sawant, Ashwin S; Kovatch, Patricia; Glicksberg, Benjamin; Charney, Alexander; Nadkarni, Girish N; Oermann, Eric Abstract: Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired…
Read MoreCross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors.
Link: https://doi.org/10.1161/CIRCULATIONAHA.122.059675 Authors: Thibord, Florian; Klarin, Derek; Brody, Jennifer A; Chen, Ming-Huei; Levin, Michael G; Chasman, Daniel I; Goode, Ellen L; Hveem, Kristian; Teder-Laving, Maris; Martinez-Perez, Angel; Aïssi, Dylan; Daian-Bacq, Delphine; Ito, Kaoru; Natarajan, Pradeep; Lutsey, Pamela L; Nadkarni, Girish N; de Vries, Paul S; Cuellar-Partida, Gabriel; Wolford, Brooke N; Pattee, Jack W; Kooperberg, Charles;…
Read MoreEnhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation.
Link: https://doi.org/10.1016/j.cvdhj.2022.07.074 Authors: Honarvar, Hossein; Agarwal, Chirag; Somani, Sulaiman; Vaid, Akhil; Lampert, Joshua; Wanyan, Tingyi; Reddy, Vivek Y; Nadkarni, Girish N; Miotto, Riccardo; Zitnik, Marinka; Wang, Fei; Glicksberg, Benjamin S Abstract: Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural…
Read MoreFederated Learning in Risk Prediction: A Primer and Application to COVID-19-Associated Acute Kidney Injury.
Link: https://doi.org/10.1159/000525645 Authors: Gulamali, Faris F; Nadkarni, Girish N Abstract: Modern machine learning and deep learning algorithms require large amounts of data; however, data sharing between multiple healthcare institutions is limited by privacy and security concerns. Federated learning provides a functional alternative to the single-institution approach while avoiding the pitfalls of data sharing. In cross-silo…
Read MoreAutomated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis.
Link: https://doi.org/10.2215/CJN.16481221 Authors: Vaid, Akhil; Jiang, Joy J; Sawant, Ashwin; Singh, Karandeep; Kovatch, Patricia; Charney, Alexander W; Charytan, David M; Divers, Jasmin; Glicksberg, Benjamin S; Chan, Lili; Nadkarni, Girish N Abstract: Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes…
Read MoreElectroencephalography at the height of a pandemic: EEG findings in patients with COVID-19.
Link: https://doi.org/S1388-2457(22)00190-0 Authors: Tantillo, Gabriela B; Jetté, Nathalie; Gururangan, Kapil; Agarwal, Parul; Marcuse, Lara; Singh, Anuradha; Goldstein, Jonathan; Kwon, Churl-Su; Dhamoon, Mandip S; Navis, Allison; Nadkarni, Girish N; Charney, Alexander W; Young, James J; Blank, Leah J; Fields, Madeline; Yoo, Ji Yeoun Abstract: To characterize continuous video electroencephalogram (VEEG) findings of hospitalized COVID-19 patients. We…
Read MoreIncidence, prevalence and mortality of chronic periaortitis: a population-based study.
Link: https://doi.org/10.55563/clinexprheumatol/0v8l4j Authors: Koster, Matthew J; Ghaffar, Umar; Duong, Stephanie Q; Crowson, Cynthia S; Burke, Michelle M; Viers, Boyd R; Potretzke, Aaron M; Bjarnason, Haraldur; Warrington, Kenneth J Abstract: To evaluate the epidemiology, presentation and outcomes of patients with chronic periaortitis from 1998 through 2018. An inception cohort of patients with incident chronic periaortitis from…
Read MoreDevelopment of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening.
Link: https://doi.org/10.1093/ehjdh/ztab101 Authors: Somani, Sulaiman S; Honarvar, Hossein; Narula, Sukrit; Landi, Isotta; Lee, Shawn; Khachatoorian, Yeraz; Rehmani, Arsalan; Kim, Andrew; De Freitas, Jessica K; Teng, Shelly; Jaladanki, Suraj; Kumar, Arvind; Russak, Adam; Zhao, Shan P; Freeman, Robert; Levin, Matthew A; Nadkarni, Girish N; Kagen, Alexander C; Argulian, Edgar; Glicksberg, Benjamin S Abstract: Clinical scoring systems…
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