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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

BMC medical informatics and decision making
INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or...

An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.

Scientific reports
Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause severe impairment in everyday functioning. sMRI is a non-invasive neuroimaging technology that visualize...

External validation of artificial intelligence for detection of heart failure with preserved ejection fraction.

Nature communications
Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, pa...

Predicting Therapy Outcomes in Patients With Stress-Related Disorders: Protocol for a Predictive Modeling Study.

JMIR research protocols
BACKGROUND: While cognitive behavioral therapy has shown efficacy in treating stress-related disorders, such as adjustment disorder and exhaustion disorder, knowledge about factors contributing to treatment response is limited. Improved identificatio...

Psychometric properties and Turkish adaptation of the artificial intelligence attitude scale (AIAS-4): evidence for construct validity.

BMC psychology
Artificial intelligence (AI) attitude scales can be used to better evaluate the benefit and drawback cons of AI. This article consists of two different studies examining attitudes towards AI. In Study I (N = 370), the four-item Artificial Intelligenc...

Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.

Clinical oral investigations
OBJECTIVES: This study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this rela...

An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.

Frontiers in endocrinology
BACKGROUND: Diabetic foot ulcers (DFUs) constitute a significant complication among individuals with diabetes and serve as a primary cause of nontraumatic lower-extremity amputation (LEA) within this population. We aimed to develop machine learning (...

Prediction model of gastrointestinal tumor malignancy based on coagulation indicators such as TEG and neural networks.

Frontiers in immunology
OBJECTIVES: Accurate determination of gastrointestinal tumor malignancy is a crucial focus of clinical research. Constructing coagulation index models using big data is feasible to achieve this goal. This study builds various prediction models throug...

A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer.

PloS one
BACKGROUND: The unique anatomical characteristics and blood supply of the rectosigmoid junction confer particular significance to its physiological functions and clinical surgeries. However, research on the prognosis of rectosigmoid junction cancer (...