AIMC Topic: Cohort Studies

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Results of robotic TAPP and conventional laparoscopic TAPP in an outpatient setting: a cohort study in Switzerland.

Langenbeck's archives of surgery
PURPOSE: Recently, robotic surgery has been increasingly performed in hernia surgery. Although feasibility and safety of robot-assisted inguinal hernia repair in an inpatient setting have been already shown, its role in outpatient hernia surgery has ...

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

Frontiers in immunology
High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed depending on fluorescence hybridization (FISH), an expensive and laborious analysis. In order to identify H...

Pressure Injury Prediction Model Using Advanced Analytics for At-Risk Hospitalized Patients.

Journal of patient safety
OBJECTIVE: Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI.

Short-term oncological and surgical outcomes of robot-assisted radical prostatectomy: A retrospective multicenter cohort study in Japan (the MSUG94 group).

Asian journal of endoscopic surgery
INTRODUCTION: We evaluated oncological outcomes of patients undergoing robot-assisted radical prostatectomy (RARP) for prostate cancer (PCa) and their perioperative complications in Japan. We investigated clinical and pathological covariates to predi...

Propensity-Matched Analysis of the Short-Term Outcome of Robot-Assisted Minimally Invasive Esophagectomy Versus Conventional Thoracoscopic Esophagectomy in Thoracic Esophageal Cancer.

World journal of surgery
BACKGROUND: In this matched-cohort study, we investigated the short-term outcome of robot-assisted minimally invasive esophagectomy (RAMIE) compared with conventional minimally invasive thoracoscopic esophagectomy (MIE) in esophageal cancer patients.

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.

Digestive diseases and sciences
BACKGROUND: Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.

JMIR mHealth and uHealth
BACKGROUND: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. How...

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithm...

Brain Functional Connectivity Analysis via Graphical Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and c...

Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.

PLoS neglected tropical diseases
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the hi...