Prescriptions

Revolutionizing cardiovascular disease classification through machine learning and statistical methods.

BACKGROUND: Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible...

Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, with epidural hematoma (EDH) being a severe consequence. This study focuses on identifying factors predicting EDH volume changes in TBI patients and developing a machine learning (ML) model to predict EDH expansion.MethodsThe study includes patients with traumatic EDH between 2019 and 2021. Data were...

Differential impact of CD34+ cell dose for different age groups in allogeneic hematopoietic cell transplantation for acute leukemia: a machine learning-based discovery.

Allogeneic hematopoietic cell transplantation (allo-HCT) presents a potentially curative treatment for hematologic malignancies yet carries associated...

Learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver.

Constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the , desi...

Gender disparities in rural motorcycle accidents: A neural network analysis of travel behavior impact.

Rural road accidents involving motorcycle riders present a formidable challenge to road safety globally. This study offers a comprehensive gender-base...

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction.

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capt...

Novel design of fractional cholesterol dynamics and drug concentrations model with analysis on machine predictive networks.

Within the intricate fabric of human physiology, cholesterol, a lipid present in cell membranes exerts a discernible effect on the concentration of th...

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based comput...

Deep multiple instance learning on heterogeneous graph for drug-disease association prediction.

Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug-disease associations (DDAs) for existing d...

Basketball robot object detection and distance measurement based on ROS and IBN-YOLOv5s algorithms.

With the combination of artificial intelligence and robotics technology, more and more professional robots are entering the public eye. Basketball rob...

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

BACKGROUND: Prostate cancer is the second leading cause of death among American men. If detected and treated at an early stage, prostate cancer is oft...

Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.

The blood-brain barrier (BBB) selectively regulates the passage of chemical compounds into and out of the central nervous system (CNS). As such, under...

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that...

Deep learning pipeline for accelerating virtual screening in drug discovery.

In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle...

Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer.

Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemi...

Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.

To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical ...

Understanding and modeling human-AI interaction of artificial intelligence tool in radiation oncology clinic using deep neural network: a feasibility study using three year prospective data.

Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely...

Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.

The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review...

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival ...

SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer.

Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlighting the role of pharmacogenomics in developing bet...