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Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24.

OBJECTIVE: Previous studies have shown that excess weight (including obesity and overweight) can inc...

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

The combination of machine learning (ML) and electronic health records (EHR) data may be able to imp...

5FU-loaded PCL/Chitosan/FeO Core-Shell Nanofibers Structure: An Approach to Multi-Mode Anticancer System.

5-Fluorouracil (5FU) and FeO nanoparticles were encapsulated in core-shell polycaprolactone (PCL)/c...

Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?

BACKGROUND AND OBJECTIVE: Explainable Artificial Intelligence (XAI) has been identified as a viable ...

Impact of Early Exposure to Robotic Surgery Among Pre-clinical Medical Students on Career Choice and Simulation Skills.

We aimed to assess whether early exposure of medical students to robotic surgery training influences...

Robustifying Deep Networks for Medical Image Segmentation.

The purpose of this study is to investigate the robustness of a commonly used convolutional neural n...

Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence.

OBJECTIVE: The aim of this study was to evaluate an automatic, deep learning based method (Augmented...

Revisiting performance metrics for prediction with rare outcomes.

Machine learning algorithms are increasingly used in the clinical literature, claiming advantages ov...

Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in mu...

Predicting post-operative right ventricular failure using video-based deep learning.

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardi...

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Efficient, reliable and reproducible target volume delineation is a key step in the effective planni...

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.

Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantifi...

Explaining Black-Box Models for Biomedical Text Classification.

In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), a...

A geometry-guided deep learning technique for CBCT reconstruction.

Although deep learning (DL) technique has been successfully used for computed tomography (CT) recons...

Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability.

The growing interest in deep learning approaches to video surveillance raises concerns about the acc...

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.

Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the...

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to f...

AI-based language models powering drug discovery and development.

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, ...

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI).

Building-damage mapping using remote sensing images plays a critical role in providing quick and acc...

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