AIMC Topic: Machine Learning

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Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.

International journal of medical informatics
BACKGROUND: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an im...

Natural language processing and machine learning to assist radiation oncology incident learning.

Journal of applied clinical medical physics
PURPOSE: To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi-automating incident classification. Our go...

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival.

International journal of molecular sciences
Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are bei...

Risk factor assessments of temporomandibular disorders via machine learning.

Scientific reports
This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieve...

Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.

Nature communications
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellu...

Bayesian supervised machine learning classification of neural networks with pathological perturbations.

Biomedical physics & engineering express
Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the...

An Accurate Deep Learning Model for Clinical Entity Recognition From Clinical Notes.

IEEE journal of biomedical and health informatics
The growing use of electronic health records in the medical domain results in generating a large amount of medical data that is stored in the form of clinical notes. These clinical notes are enriched with clinical entities like disease, treatment, te...

Distant Domain Transfer Learning for Medical Imaging.

IEEE journal of biomedical and health informatics
Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer...

PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches.

Interdisciplinary sciences, computational life sciences
Anti-parasitic peptides (APPs) have been regarded as promising therapeutic candidate drugs against parasitic diseases. Due to the fact that the experimental techniques for identifying APPs are expensive and time-consuming, there is an urgent need to ...

The Evolving Role of Artificial Intelligence in Cardiac Image Analysis.

The Canadian journal of cardiology
Research in artificial intelligence (AI) has progressed over the past decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection a...