Adiposity accumulation in the liver is an early-stage indicator of non-alcoholic fatty liver disease. Analysis of ultrasound (US) backscatter echoes from liver parenchyma with deep learning (DL) may offer an affordable alternative for hepatic steatos...
AIM: The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis.
In numerous classification problems, class distribution is not balanced. For example, positive examples are rare in the fields of disease diagnosis and credit card fraud detection. General machine learning methods are known to be suboptimal for such ...
Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention b...
BMC medical informatics and decision making
Jul 25, 2022
BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep l...
Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ran...
BACKGROUND: A patient's health information is generally fragmented across silos because it follows how care is delivered: multiple providers in multiple settings. Though it is technically feasible to reunite data for analysis in a manner that underpi...
BACKGROUND: Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a mul...
PURPOSE: The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using the trends of four easy-to-collect vital signs.
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for...