Public Health & Policy

Deep graph neural network-based prediction of acute suicidal ideation in young adults.

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network mode...

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify man...

Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study.

In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for di...

Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step...

Medical code prediction via capsule networks and ICD knowledge.

BACKGROUND: Clinical notes record the health status, clinical manifestations and other detailed information of each patient. The International Classif...

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.

BACKGROUND: Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection ...

Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China.

Epidemiological studies have revealed the associations of air pollutants and meteorological factors with a range of mental health conditions. However,...

Platform for Healthcare Promotion and Cardiovascular Disease Prevention.

This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple po...

Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability.

The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, ...

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR.

The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and i...

Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics.

Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately ...

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures...

Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.

The classification of driving styles plays a fundamental role in evaluating drivers' driving behaviors, which is of great significance to traffic safe...

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective,...

Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniqu...

Flexible Photodriven Actuator Based on Gradient-Paraffin-Wax-Filled TiCT MXene Film for Bionic Robots.

Due to their high flexibility and adaptability, bionic robots have great potential in applications such as healthcare, rescue, and surveillance. The f...

The predictive skill of convolutional neural networks models for disease forecasting.

In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning mo...

EagleEye: A Worldwide Disease-Related Topic Extraction System Using a Deep Learning Based Ranking Algorithm and Internet-Sourced Data.

Due to the prevalence of globalization and the surge in people's traffic, diseases are spreading more rapidly than ever and the risks of sporadic cont...

Machine learning to advance the prediction, prevention and treatment of eating disorders.

Machine learning approaches are just emerging in eating disorders research. Promising early results suggest that such approaches may be a particularly...

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate a...