Infectious Disease

Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.

Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and severity check of lung diseases. The epidemic of COVID-19 brought out by the novel coronavirus has triggered a critical need for artificial intelligence assistance in diagnosing and controlling the disease to reduce its effects on people and global ...

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System.

In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction me...

Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19.

Accurate epidemiological models are necessary for governments, organizations, and individuals to respond appropriately to the ongoing novel coronaviru...

Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis.

Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evo...

COVID-19 IgG/IgM patterns, early IL-6 elevation and long-term radiological sequelae in 75 patients hospitalized due to interstitial pneumonia followed up from 3 to 12 months.

BACKGROUND: COVID-19 pandemic resulted in about 165 million infections and 3.4 million deaths all over the world across 15 months. The most severe cli...

A computational framework to solve the nonlinear dengue fever SIR system.

This study is relevant to present the numerical form of the nonlinear dengue fever SIR system are presented using the artificial neural networks along...

Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm.

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not...

Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization.

"No-shows", defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the ...

Parsing Immune Correlates of Protection Against SARS-CoV-2 from Biomedical Literature.

After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) ...

On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020...

Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage.

Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care meas...

Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.

To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we addre...

Data and Model Biases in Social Media Analyses: A Case Study of COVID-19 Tweets.

During the coronavirus disease pandemic (COVID-19), social media platforms such as Twitter have become a venue for individuals, health professionals, ...

First-line drug resistance profiling of : a machine learning approach.

The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis...

A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records.

Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imagi...

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVI...

Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of spp.

While MALDI-TOF mass spectrometry (MS) is widely considered as the reference method for the rapid and inexpensive identification of microorganisms in ...

Deploying Machine Learning Models Using Progressive Web Applications: Implementation Using a Neural Network Prediction Model for Pneumonia Related Child Mortality in The Gambia.

BACKGROUND: Translating research outputs into practical tools for medical practitioners is a neglected area and could have a substantial impact. One o...

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomic...

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.

OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical d...