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Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data.

BMC pregnancy and childbirth
BACKGROUND: Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models ...

Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes.

BMC gastroenterology
BACKGROUND: Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have show...

Machine learning based classification of presence utilizing psychophysiological signals in immersive virtual environments.

Scientific reports
In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, grap...

Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark.

Nature communications
The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can add...

Generative artificial intelligence in primary care: an online survey of UK general practitioners.

BMJ health & care informatics
OBJECTIVES: Following the launch of ChatGPT in November 2022, interest in large language model-powered chatbots has soared with increasing focus on the clinical potential of these tools. We sought to measure general practitioners' (GPs) current use o...

Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been...

Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.

Physiological measurement
. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to ...

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bila...

Insights Into Detecting Adult ADHD Symptoms Through Advanced Dual-Stream Machine Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Advancements in machine learning offer promising avenues for the identification of ADHD symptoms in adults, an endeavour traditionally encumbered by the intricacies of human behavioural patterns. In this paper, we introduce three innovative dual-stre...

Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and loosening, thereby improving the practicality of MPR-based gestural interfaces towards intell...