Public Health & Policy

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score.

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided in...

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model.

The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has low accuracy and poor real-time performance. To overcome this gap, first, four-channel fusion images, including RGB and DSM (digital surface model), a...

Incidence, Etiology, Prevention and Management of Ureteroenteric Strictures after Robot-Assisted Radical Cystectomy: A Review of Published Evidence and Personal Experience.

Benign ureteroenteric anastomosis strictures (UESs) are one of many critical complications that may cause irreversible disability following robot-assi...

Machine learning and artificial intelligence: applications in healthcare epidemiology.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a ...

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs.

According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impa...

Detection and analysis of COVID-19 in medical images using deep learning techniques.

The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for ...

Spatialising urban health vulnerability: An analysisof NYC's critical infrastructure during COVID-19.

This paper examines how fragmentation of critical infrastructure impacts the spread of the coronavirus outbreak in New York City at the neighbourhood ...

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive health...

PASS: Protein Annotation Surveillance Site for Protein Annotation Using Homologous Clusters, NLP, and Sequence Similarity Networks.

Advances in genome sequencing have accelerated the growth of sequenced genomes but at a cost in the quality of genome annotation. At the same time, co...

The Role of Machine Learning and Artificial Intelligence for making a Digital Classroom and its sustainable Impact on Education during Covid-19.

During the Disease outbreak and in the future, there will be a lot of learning. Since the pandemic has interrupted global schooling, remote learning h...

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach.

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be chal...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims...

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method.

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and manageme...

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning.

While non-essential travel was canceled during the coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential. The objectives ...

Efficient and targeted COVID-19 border testing via reinforcement learning.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-es...

Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy.

Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who...

Postresection Period-Specific Hazard of Recurrence as a Framework for Surveillance Strategy in Patients with Hepatocellular Carcinoma: A Multicenter Outcome Study.

INTRODUCTION: In spite of the high frequency of recurrence of hepatocellular carcinoma (HCC) after resection, little evidence exists to directly help ...

The Importance of Close Follow-Up in Patients with Early-Grade Diabetic Retinopathy: A Taiwan Population-Based Study Grading via Deep Learning Model.

(1) Background: Diabetic retinopathy (DR) can cause blindness. Current guidelines on diabetic eye care recommend more frequent eye examinations for mo...

Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.

BACKGROUND: Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML al...

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).

BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps duri...