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A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.
J Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
View Article and Find Full Text PDFAn Atlas of Anatomical Variants of the Human Talus.
J Morphol
January 2025
Dpto. de Medicina Interna, Hospital Universitario de Canarias, Universidad de La Laguna, La Laguna, Spain.
Anatomical variants can be used effectively to identify relationships between individuals in kinship analysis and they may be useful during surgical procedures. These procedures can be better implemented when the cause, appearance and location are understood. Clear representations and definitions of anatomical traits are necessary.
View Article and Find Full Text PDFSTOUT V2.0: SMILES to IUPAC name conversion using transformer models.
J Cheminform
December 2024
Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany.
Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge.
View Article and Find Full Text PDFDeveloping a Charlson Comorbidity Index for the American Indian Population Using the Epidemiologic Data from the Strong Heart Study.
J Racial Ethn Health Disparities
December 2024
Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Background: The Charlson Comorbidity Index (CCI) is a frequently used mortality predictor based on a scoring system for the number and type of patient comorbidities health researchers have used since the late 1980s. The initial purpose of the CCI was to classify comorbid conditions, which could alter the risk of patient mortality within a 1-year time frame. However, the CCI may not accurately reflect risk among American Indians because they are a small proportion of the US population and possibly lack representation in the original patient cohort.
View Article and Find Full Text PDFTherapy response monitoring in blood plasma from esophageal adenocarcinoma patients using cell-free DNA methylation profiling.
Sci Rep
December 2024
Translational Oncogenomics and Bioinformatics Lab, Center for Medical Biotechnology, VIB-UGent & CRIG, Technologiepark-Zwijnaarde 75, 9052, Ghent, Belgium.
Esophageal adenocarcinoma (EAC) is an aggressive cancer characterized by a high risk of relapse post-surgery. Current follow-up methods (serum carcinoembryonic antigen detection and PET-CT) lack sensitivity and reliability, necessitating a novel approach. Analyzing cell-free DNA (cfDNA) from blood plasma emerges as a promising avenue.
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