Hospital-Based Medicine

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models.

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy.

Advances in AI and machine learning for predictive medicine.

The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunitie...

Clinical Integration of Artificial Intelligence for Breast Imaging.

This article describes an approach to planning and implementing artificial intelligence products in a breast screening service. It highlights the impo...

Artificial intelligence in head and neck surgery: Potential applications and future perspectives.

Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wi...

The learning curve of robot-assisted laparoscopic pyeloplasty in children.

To explore the learning curve of robot-assisted laparoscopic pyeloplasty (RALP) in children. The clinical data, surgical information, and postoperativ...

An ingestible self-propelling device for intestinal reanimation.

Postoperative ileus (POI) is the leading cause of prolonged hospital stay after abdominal surgery and is characterized by a functional paralysis of th...

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban ro...

Recommendations for using artificial intelligence in clinical flow cytometry.

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by...

Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques.

Real-time flood forecasting is one of the most pivotal measures for flood management, and real-time error correction is a critical step to guarantee t...

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the vari...

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ...

Effect of insurance status on perioperative outcomes after robotic pancreaticoduodenectomy: a propensity-score matched analysis.

The influence of Medicaid or being uninsured is prevailingly thought to negatively impact a patient's socioeconomic and postoperative course, yet litt...

Outcomes of robot-assisted versus video-assisted mediastinal mass resection during the initial learning curve.

To compare the learning curve of mediastinal mass resection between robot-assisted surgery and thoracoscopic surgery. Retrospective perioperative data...

Predicting reoperation and readmission for head and neck free flap patients using machine learning.

BACKGROUND: To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstructio...

Machine learning decision support model for discharge planning in stroke patients.

BACKGROUND/AIM: Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore whic...

Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.

Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in ...

Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury.

AIM: This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic b...

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs.

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Allevia...

The weight of BMI in impacting postoperative and oncologic outcomes in pancreaticoduodenectomy is attenuated by a robotic approach.

This study was undertaken to observe the effect of body mass index (BMI) on perioperative outcomes and survival when comparing robotic vs 'open' pancr...