Impact of COVID-19 pandemic on alcohol-related hepatitis admissions: Analysis of nationwide data 2016-2020
CONCLUSIONS: A sharp rise in the cases of ARH between 2019-2020 was noted, which aligned with the COVID-19 pandemic. Not only did total hospitalization increase, but an increase in mortality was also noted, reflecting higher severity in the patients admitted during the COVID-19 pandemic.PMID:37315782 | PMC:PMC10259164 | DOI:10.1016/j.amjms.2023.06.002 (Source: The American Journal of the Medical Sciences)
Source: The American Journal of the Medical Sciences - June 14, 2023 Category: General Medicine Authors: Aalam Sohal Hunza Chaudhry Jay Patel Nimrat Dhillon Isha Kohli Dino Dukovic Marina Roytman Kris V Kowdley Source Type: research
Comparing Trends Between 2004 and 2019 in Intact and Ruptured Abdominal, Thoracic, and Thoracoabdominal Aortic Aneurysms Spanning ICD9 and ICD10 Within the NIS Database
Given the changes in intervention guidelines and the growing popularity of endovascular treatment for aortic aneurysms since the turn of the millennium, we used the Nationwide Inpatient Sample to examine the trends in abdominal aortic aneurysm (AAA), thoracoabdominal aortic aneurysm (TAAA), and thoracic aortic aneurysm (TAA) rupture presentations and intact repairs along with the treatment methods (endovascular vs open). Analyses of aortic aneurysm trends spanning the change in coding from International Classification of Diseases, 9th or 10th edition, has yet to be studied. (Source: Journal of Vascular Surgery)
Source: Journal of Vascular Surgery - May 23, 2023 Category: Surgery Authors: Patrick D. Conroy, Vinamr Rastogi, Yoel Solomon, Kirsten Dansey, Hence J.M. Verhagen, Marc L. Schermerhorn Tags: Poster Competition Source Type: research
A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning
Stud Health Technol Inform. 2023 May 18;302:609-610. doi: 10.3233/SHTI230217.ABSTRACTUsing electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).PMID:37203760 | DOI:10.3233/SHTI230217 (Source: Studies in Health Technology and Informatics)
Source: Studies in Health Technology and Informatics - May 19, 2023 Category: Information Technology Authors: Ali Amirahmadi Mattias Ohlsson Kobra Etminani Olle Melander Jonas Bj örk Source Type: research
