Pneumonia

Space-independent community and hub structure of functional brain networks.

Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network of inter-regional functional connections. The most costly connections in the network are long-distance correlations that, in the absence of underlying structural connections, are maintained by sustained energetic inputs. Here, we present a spatial modeling approach that amplifies contributions made b...

Explore the relationship between fish community and environmental factors by machine learning techniques.

In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental facto...

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.

BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network an...

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important ...

Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.

Because depression has high prevalence and cause enduring disability, it is important to predict onset of depression among community dwelling adults. ...

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation.

Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantage...

Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification.

Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and in...

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted ...

Rapid Assessment of Opioid Exposure and Treatment in Cities Through Robotic Collection and Chemical Analysis of Wastewater.

INTRODUCTION: Accurate data regarding opioid use, overdose, and treatment is important in guiding community efforts at combating the opioid epidemic. ...

pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. Thi...

Motivational interviewing and culture for urban Native American youth (MICUNAY): A randomized controlled trial.

To date, few programs that integrate traditional practices with evidence-based practices have been developed, implemented, and evaluated with urban Am...

An antifouling peptide-based biosensor for determination of Streptococcus pneumonia markers in human serum.

We report a peptide-based sensor that involves a multivalent interaction with L-ascorbate 6-phosphate lactonase (UlaG), a protein marker of Streptococ...

Survey on deep learning for pulmonary medical imaging.

As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to nat...

Synthesis and in vitro activity of asymmetric indole-based bisamidine compounds against Gram-positive and Gram-negative pathogens.

A series of new asymmetric bisamidine was designed, synthesized, and tested for their in-vitro antibacterial activity using a range of Gram-positive a...

Artificial intelligence applications for thoracic imaging.

Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural ...

The use of natural language processing to identify vaccine-related anaphylaxis at five health care systems in the Vaccine Safety Datalink.

PURPOSE: The objective was to develop a natural language processing (NLP) algorithm to identify vaccine-related anaphylaxis from plain-text clinical n...

NNTox: Gene Ontology-Based Protein Toxicity Prediction Using Neural Network.

With advancements in synthetic biology, the cost and the time needed for designing and synthesizing customized gene products have been steadily decrea...

Verification with the utility of an established rapid assessment of brain safety for newly developed vaccines.

In the twenty-first century, high contagious infectious diseases such as SARS (Severe Acute Respiratory Syndrome), MERS (Middle East Respiratory Syndr...

Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.

BACKGROUND: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approach...

Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection.

Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment ...