AIMC Topic: Adult

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Disrupted rich-club network organization and individualized identification of patients with major depressive disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural br...

Using Machine Learning to Make Predictions in Patients Who Fall.

The Journal of surgical research
BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model base...

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Internal and emergency medicine
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 base...

A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea.

Medical & biological engineering & computing
A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess ...

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model.

The American journal of medicine
BACKGROUND: General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learnin...

Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension.

Sensors (Basel, Switzerland)
Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep lea...

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses.

Journal of orthopaedic surgery and research
OBJECTIVE: Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classif...