Prevention of medical errors

Comparing machine learning with case-control models to identify confirmed dengue cases.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like...

Evolution-Communication Spiking Neural P Systems.

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neuro...

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and manageme...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-ba...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the ...

Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children.

Violence against children is a global public health threat of considerable concern. At least half of all children worldwide experience violence every ...

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features.

The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In a...

Methodology minute: a machine learning primer for infection prevention and control.

The use of machine-learning and predictive modeling in infection prevention and control activities is increasing dramatically. In order for infection ...

Genome-wide association study-based deep learning for survival prediction.

Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and c...

Primary Prevention Trial Designs Using Coronary Imaging: A National Heart, Lung, and Blood Institute Workshop.

Coronary artery calcium (CAC) is considered a useful test for enhancing risk assessment in the primary prevention setting. Clinical trials are under c...

Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: A gain-scheduled approach.

This study is concerned with the state estimation issue for a kind of delayed artificial neural networks with multiplicative noises. The occurrence of...

Knowledge-Based Decision Support in Healthcare via Near Field Communication.

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support t...

Relating Mori's Uncanny Valley in generating conversations with artificial affective communication and natural language processing.

Human beings express affinity (Shinwa-kan in Japanese language) in communicating transactive engagements among healthcare providers, patients and heal...

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learn...

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and fin...

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.

In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients...

RAHM: Relation augmented hierarchical multi-task learning framework for reasonable medication stocking.

As an important task in digital preventive healthcare management, especially in the secondary prevention stage, active medication stocking refers to t...

Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities.

In this paper, the protocol-based remote state estimation problem is considered for a kind of delayed artificial neural networks. The random time-vary...

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advance...

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts.

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interac...