Intensivists

Mortality prediction in ICU Using a Stacked Ensemble Model.

Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of various machine learning approaches in this field. Innovative algorithms play a crucial role in boosting the performance of models. This study uses a stacke...

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis.

BACKGROUND: Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO system.

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessmen...

Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients.

In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods us...

Highly Integrated Multi-Material Fibers for Soft Robotics.

Soft robots are envisioned as the next generation of safe biomedical devices in minimally invasive procedures. Yet, the difficulty of processing soft ...

Co-optimization of robotic design and skill inspired by human hand evolution.

During evolution of the human hand, evolutionary morphology has been closely related to behavior in complicated environments. Numerous researchers hav...

Multimodal multi-task deep neural network framework for kinase-target prediction.

Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding th...

Diagnostic performance of machine learning models using cell population data for the detection of sepsis: a comparative study.

OBJECTIVES: To compare the artificial intelligence algorithms as powerful machine learning methods for evaluating patients with suspected sepsis using...

The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms.

In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Cli...

Self-Supervised Multi-Modal Hybrid Fusion Network for Brain Tumor Segmentation.

Accurate medical image segmentation of brain tumors is necessary for the diagnosing, monitoring, and treating disease. In recent years, with the gradu...

Unraveling the complexities of urban fluvial flood hydraulics through AI.

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myria...

Emergence of MXene and MXene-Polymer Hybrid Membranes as Future- Environmental Remediation Strategies.

The continuous deterioration of the environment due to extensive industrialization and urbanization has raised the requirement to devise high-performa...

Prediction of drug-target interactions through multi-task learning.

Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced t...

In-sensor neural network for high energy efficiency analog-to-information conversion.

This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing...

Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital.

Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia,...

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep...

Applications of complex picture fuzzy soft power aggregation operators in multi-attribute decision making.

The major theme of this analysis is to suggest a new theory in the form of complex picture fuzzy soft (CPFS) information and to initiate their major a...

A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning.

In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. ...

Occupational health and safety risk assessment using a fuzzy multi-criteria approach in a hospital in Chennai, India.

Hospitals provide direct and indirect employment benefits to medical professionals. Accidents in hospitals often lead to disastrous consequences such...

Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism.

Commonly used nested entity recognition methods are span-based entity recognition methods, which focus on learning the head and tail representations o...