The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical ed...
In this paper, we present a novel methodology based on machine learning for identifying the most appropriate from a set of available state-of-the-art object detectors for a given application. Our particular interest is to develop a road map for ident...
BACKGROUND: Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods...
BACKGROUND: The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine le...
The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyz...
INTRODUCTION: The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machin...
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...
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified releva...
In this study, we developed a system that predicts the binding sites of proteins for five mononucleotides (AMP, ADP, ATP, GDP, and GTP). The system comprises two machine learning (ML)-based predictors using a convolutional neural network and a gradie...
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