AIMC Topic: Machine Learning

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Extracting adverse drug events from clinical Notes: A systematic review of approaches used.

Journal of biomedical informatics
BACKGROUND: An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and p...

In Silico prediction of inhibitors for multiple transporters via machine learning methods.

Molecular informatics
Transporters play an indispensable role in facilitating the transport of nutrients, signaling molecules and the elimination of metabolites and toxins in human cells. Contemporary computational methods have been employed in the prediction of transport...

Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow.

Journal of chemical information and modeling
Machine learning (ML) methods can train a model to predict material properties by exploiting patterns in materials databases that arise from structure-property relationships. However, the importance of ML-based feature analysis and selection is often...

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis.

Medicina (Kaunas, Lithuania)
: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of se...

Classification of human walking context using a single-point accelerometer.

Scientific reports
Real-world walking data offers rich insights into a person's mobility. Yet, daily life variations can alter these patterns, making the data challenging to interpret. As such, it is essential to integrate context for the extraction of meaningful infor...

Image annotation and curation in radiology: an overview for machine learning practitioners.

European radiology experimental
"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and d...

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Reviews in the neurosciences
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides ...

Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO emissions.

Environmental research
Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In th...

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients ...