Surveillance

Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques.

A regression model is developed to predict survival time in months for lung cancer patients. It was previously shown that predictive models perform accurately for short survival times of less than 6 months; however, model accuracy is reduced when attempting to predict longer survival times. This study employs an approach for which regression models are used in combination with a classification mod...

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.

Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postm...

Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors.

Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system ...

Semi-supervised learning to improve generalizability of risk prediction models.

The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi...

[An online dynamic knowledge base in multiple languages on general medicine and primary care].

INTRODUCTION: The International Classification of Primary Care, Second version (ICPC-2) aligned with the 10th Revision of the International Classifica...

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal .

Nontyphoidal species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susce...

Postmarketing surveillance on clinical use of edoxaban in patients with nonvalvular atrial fibrillation (ETNA-AF-Japan): Three-month interim analysis results.

BACKGROUND: Direct oral anticoagulants are the first-line drugs for anticoagulation therapy in nonvalvular atrial fibrillation (NVAF). However, a real...

The current role of prostate multiparametric magnetic resonance imaging.

Prostate multi-parametric magnetic resonance imaging (mpMRI) has shown excellent sensitivity for Gleason ≥7 cancers, especially when their volume is ≥...

A prototype of knowledge-based patient safety event reporting and learning system.

BACKGROUND: Patient falls, the most common safety events resulting in adverse patient outcomes, impose significant costs and have become a great burde...

Development and flight performance of a biologically-inspired tailless flapping-wing micro air vehicle with wing stroke plane modulation.

The tailless flapping-wing micro air vehicle (FW-MAV) is one of the most challenging problems in flapping-wing design due to its lack of tail for inhe...

A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization.

We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories...

A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology.

BACKGROUND: Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering.

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the C...

Machine-learned epidemiology: real-time detection of foodborne illness at scale.

Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. T...

Introduction to Machine Learning in Digital Healthcare Epidemiology.

To exploit the full potential of big routine data in healthcare and to efficiently communicate and collaborate with information technology specialists...

Artificial Intelligence for infectious disease Big Data Analytics.

BACKGROUND: Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the...

Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting.

PURPOSE: To use a natural language processing and machine learning algorithm to evaluate inter-radiologist report variation and compare variation betw...

Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits.

The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact...

Predicting of anaphylaxis in big data EMR by exploring machine learning approaches.

Anaphylaxis is a life-threatening allergic reaction that occurs suddenly after contact with an allergen. Epidemiological studies about anaphylaxis are...

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence suppor...