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Impact of the COVID-19 Pandemic on the Severity and Early Postoperative Outcomes of Acute Appendicitis.
Cureus
August 2023
Department of Digestive Surgery, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, VNM.
Background The coronavirus disease 2019 (COVID-19) pandemic caused changes in surgical practice. For acute appendicitis (AA), measures to control the pandemic might hinder patients from seeking medical care timely, resulting in increasing severity, postoperative complications, and mortality. This study aimed to investigate whether the COVID-19 pandemic had a negative impact on the severity and postoperative outcomes of patients with AA.
View Article and Find Full Text PDFSurgical gestures as a method to quantify surgical performance and predict patient outcomes.
NPJ Digit Med
December 2022
Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
How well a surgery is performed impacts a patient's outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue "gestures" is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers.
View Article and Find Full Text PDFA multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids.
Sci Data
June 2022
Texas A&M University, Department of Electrical and Computer Engineering, College Station, 77840, USA.
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids.
View Article and Find Full Text PDFInterpretability and fairness evaluation of deep learning models on MIMIC-IV dataset.
Sci Rep
May 2022
Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA.
Article Synopsis
- Recent advancements in large healthcare datasets have enhanced the research of deep learning models, but raise concerns about interpretability, fairness, and biases, especially when human lives are involved.
- The study focuses on the MIMIC-IV dataset to analyze in-hospital mortality prediction models, revealing issues with model interpretability and biases, particularly regarding demographic features affecting fairness in predictions.
- Key findings indicate that while certain interpretability methods can highlight important features for predictions, they also show reliance on demographic factors, leading to disparate treatment and unfair predictions across different patient groups based on ethnicity, gender, and age.
DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning.
AMIA Annu Symp Proc
April 2022
Computer Science Department, University of Southern California, Los Angeles, CA, U.S.A.
Burn wounds are most commonly evaluated through visual inspection to determine surgical candidacy, taking into account burn depth and individualized patient factors. This process, though cost effective, is subjective and varies by provider experience. Deep learning models can assist in burn wound surgical candidacy with predictions based on the wound and patient characteristics.
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