Infection Control

Utilization of Bioinorganic Nanodrugs and Nanomaterials for the Control of Infectious Diseases Using Deep Learning.

As one of the main causes of morbidity and mortality, viral infections have a major impact on the well-being and economics of every nation in the globe. The ability to predictably diagnose viral infections improves the provision of good healthcare as well as the control and prevention of these conditions. Nanomaterials have gained widespread usage in the medical industry recently due to the rapid ...

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrat...

The use of smart environments and robots for infection prevention control: A systematic literature review.

BACKGROUND: Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current us...

Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology.

How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve the patient experience are problems faced by all ...

Technical evaluation of robotic tele-cholecystectomy: a randomized single-blind controlled pilot study.

Although robotic telesurgery is growing in popularity, the benefits of telesurgery compared to local surgery are unclear. This study aimed to evaluate...

[Robots in visceral and thoracic surgery-Quo vadis?].

Robotic surgical systems are now an inherent part of the German hospital landscape. In recent years, there has been an enormous increase in installed ...

Cost drivers for benign hysterectomy within a health care system: Influence of patient, perioperative, and hospital factors.

OBJECTIVE: To identify patient, perioperative, and hospital factors that drive total hospital charges for benign hysterectomy.

Robot-assisted rehabilitation training improves knee function and daily activity ability in older adults following total knee arthroplasty.

To evaluate the effects of robot-assisted rehabilitation training on knee function and the daily activity ability of older adults following total knee...

The Efficacy of Urinary Continence in Patients Undergoing Robot-Assisted Radical Prostatectomy with Bladder-Prostatic Muscle Reconstruction and Bladder Neck Eversion Anastomosis.

Background and Objectives: To evaluate the efficacy of bladder-prostatic muscle reconstruction and bladder neck eversion anastomosis in the recovery o...

Multitask Deep Learning for Cost-Effective Prediction of Patient's Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data.

In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential since it is one of the essential measures in treating pati...

A propensity score matching study of totally robotic right hemicolectomy versus robot-assisted right hemicolectomy.

To compare and analyze the differences in safety, feasibility and short-term efficacy between robot-assisted right hemicolectomy (RARH) and totally ro...

Effect of Postoperative Pain Management after Robot-Assisted Radical Prostatectomy: A Study on Reducing Hospital Length of Stay and Medical Costs Using Japanese Nationwide Database.

Prostate cancer has a high incidence rate. Many articles reported its usefulness with the advent of robotic surgery in 2001. However, epidural analges...

Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital.

Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia,...

Analysis of Characteristic Factors of Nursing Safety Incidents in ENT Surgery by Deep Learning-Based Medical Data Association Rules Method.

It is of great significance to explore the characteristic factors of postoperative nursing safety events in patients with otolaryngology surgery and t...

Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire.

OBJECTIVE: A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest...

De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models.

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information i...

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction mo...

Application and Communication Optimization Technology of Unmanned Distribution Car under Deep Learning in Logistics Express of COVID-19.

This work aims to solve the problem that the daily necessities of urban residents cannot be delivered during coronavirus disease 2019 (COVID-19), ther...

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine.

The influx of hospital patients has become common in recent years. Hospital management departments need to redeploy healthcare resources to meet the m...

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical servic...