Surveillance

Ambiguous and Incomplete: Natural Language Processing Reveals Problematic Reporting Styles in Thyroid Ultrasound Reports.

OBJECTIVE: Natural language processing (NLP) systems convert unstructured text into analyzable data. Here, we describe the performance measures of NLP to capture granular details on nodules from thyroid ultrasound (US) reports and reveal critical issues with reporting language.

The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.

This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (Pu...

Acute Kidney Injury in a Tertiary Care Center of South India.

BACKGROUND AND OBJECTIVE: Data regarding the epidemiology and outcomes of acute kidney injury (AKI) from our part of the world are limited. The irking...

Understanding the potential of emerging digital technologies for improving road safety.

Each year, 1.35 million people are killed on the world's roads and another 20-50 million are seriously injured. Morbidity or serious injury from road ...

Quantitative analysis of fragrance allergens in various matrixes of cosmetics by liquid-liquid extraction and GC-MS.

Fragrances are the most common chemicals in cosmetics to which people expose every day. However, the unwanted allergic reactions such as contact derma...

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science.

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, c...

Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments.

Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Ne...

A weakly supervised model for the automated detection of adverse events using clinical notes.

With clinical trials unable to detect all potential adverse reactions to drugs and medical devices prior to their release into the market, accurate po...

Knowledge graph analysis and visualization of AI technology applied in COVID-19.

With the global outbreak of coronavirus disease (COVID-19) all over the world, artificial intelligence (AI) technology is widely used in COVID-19 and ...

AI in predicting COPD in the Canadian population.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Cana...

Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychol...

Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system.

BACKGROUND: Artificial intelligence (AI) has made considerable progress in image recognition, especially in the analysis of endoscopic images. The ava...

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

OBJECTIVES: This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers...

Characteristics of publicly available skin cancer image datasets: a systematic review.

Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total numb...

The ensemble learning model is not better than the Asian modified CKD-EPI equation for glomerular filtration rate estimation in Chinese CKD patients in the external validation study.

OBJECTIVE: To assess the clinical practicability of the ensemble learning model established by Liu et al. in estimating glomerular filtration rate (GF...

Risk of bias assessment in preclinical literature using natural language processing.

We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the proce...

The regulatory and modulatory roles of TRP family channels in malignant tumors and relevant therapeutic strategies.

Transient receptor potential (TRP) channels are one primary type of calcium (Ca) permeable channels, and those relevant transmembrane and intracellula...

A Natural Language Processing-Based Approach for Identifying Hospitalizations for Worsening Heart Failure Within an Integrated Health Care Delivery System.

IMPORTANCE: The current understanding of epidemiological mechanisms and temporal trends in hospitalizations for worsening heart failure (WHF) is based...

Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions.

OBJECTIVE: To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and repo...

Machine learning in the prediction of medical inpatient length of stay.

Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be diffi...