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A novel deep learning based method for myocardial strain quantification.

Biomedical physics & engineering express
This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination.We measure the global and regional myocardial stra...

A hybridization of XGBoost machine learning model by Optuna hyperparameter tuning suite for cardiovascular disease classification with significant effect of outliers and heterogeneous training datasets.

International journal of cardiology
BACKGROUND: Over the last few decades: heart disease (HD) has emerged as one of the deadliest diseases in the world. Approximately more than 31 % of the population dies from HD each year. The Diagnosis of HD in an earlier stage is a cognitively chall...

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Computers in biology and medicine
Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build...

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Journal of chemical information and modeling
Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and researc...

American College of Surgeons survival calculator for biliary tract cancers: using machine learning to individualize predictions.

Surgery
BACKGROUND: Although cancer prognosis is most commonly estimated by tumor stage, survival is multifactorial. Our objective was to develop an American College of Surgeons "Biliary Tract Cancer Survival Calculator" prototype using machine learning to g...

Interaction effect between data discretization and data resampling for class-imbalanced medical datasets.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundData discretization is an important preprocessing step in data mining for the transfer of continuous feature values to discrete ones, which allows some specific data mining algorithms to construct more effective models and facilitates the d...

Detection of retinal diseases using an accelerated reused convolutional network.

Computers in biology and medicine
Convolutional neural networks are continually evolving; with some efforts aimed at improving accuracy, others at increasing speed, and some at enhancing accessibility. Improving accessibility broadens the application of neural networks across a wider...

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

IEEE transactions on visualization and computer graphics
Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial ob...

Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model Prompts.

IEEE transactions on visualization and computer graphics
Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially a...