AIMC Topic: Cohort Studies

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Robot-Assisted Radical Prostatectomy Associated with Decreased Persistent Postoperative Opioid Use.

Journal of endourology
Minimally invasive surgery offers reduced pain and opioid use postoperatively compared with open surgery, but large-scale comparative studies are lacking. We assessed the incidence of persistent opioid use after open and robot-assisted radical prost...

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

Scientific reports
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...

Prediction of admission in pediatric emergency department with deep neural networks and triage textual data.

Neural networks : the official journal of the International Neural Network Society
Emergency department (ED) overcrowding is a global condition that severely worsens attention to patients, increases clinical risks and affects hospital cost management. A correct and early prediction of ED's admission is of high value and a motivatio...

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

Arthritis research & therapy
BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRS...

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

European radiology
OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs ...

Early prediction of circulatory failure in the intensive care unit using machine learning.

Nature medicine
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monit...