Anemia

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random F...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permanent disability and stigma. Described as a disease of poverty, the impact of socioeconomic indicators such as nutrition on LF remains largely unexplored. This cross-sectional study investigates nutritional predictors implicated in the progression of LF...

: Towards Autonomous Electronic Health Record Navigation.

Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality of care and increase clinician burnout. Despite th...

Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.

Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthc...

Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.

Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This resul...

Neuronal and therapeutic perspectives on empathic pain: A rational insight.

Empathy is the capacity to experience and understand the feelings of others, thereby playing a key role in a person's mental well-being essentially by...

Rapid Blood Clot Removal via Remote Delamination and Magnetization of Clot Debris.

Micro/nano-scale robotic devices are emerging as a cutting-edge approach for precision intravascular therapies, offering the potential for highly targ...

Closing the loop: autonomous intelligent control for hypoxia pre-acclimatization and high-altitude health management.

Hypobaric hypoxia at high altitudes threatens the health of high-altitude residents. The development of effective methods to guarantee the safety of f...

CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.

BACKGROUND: High altitude pulmonary edema (HAPE) poses a significant medical challenge to individuals ascending rapidly to high altitudes. Hypoxia-ind...

EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brai...

Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate p...

Living Microalgae-Based Magnetic Microrobots for Calcium Overload and Photodynamic Synergetic Cancer Therapy.

The combination of Ca overload and reactive oxygen species (ROS) production for cancer therapy offers a superior solution to the lack of specificity i...

Minimally invasive modalities for keratinocyte carcinomas-Part I: Diagnostics.

Keratinocyte carcinoma, including basal cell and squamous cell carcinoma, is the most prevalent form of cancer within the United States, with its inci...

Machine Learning Techniques for Simulating Human Psychophysical Testing of Low-Resolution Phosphene Face Images in Artificial Vision.

To evaluate the quality of artificial visual percepts generated by emerging methodologies, researchers often rely on labor-intensive and tedious human...

Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.

Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs...

Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.

This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akid...

Rapidly self-healing electronic skin for machine learning-assisted physiological and movement evaluation.

Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their ...

Predicting Type 2 diabetes onset age using machine learning: A case study in KSA.

The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid ...

Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health,...

Subjective recovery in professional soccer players: A machine learning and mediation approach.

Coaches often ask players to judge their recovery status (subjective recovery). We aimed to explore potential determinants of subjective recovery in 1...