AIMC Topic: Machine Learning

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Predicting dental caries outcomes in young adults using machine learning approach.

BMC oral health
OBJECTIVES: To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques.

Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis.

JMIR public health and surveillance
BACKGROUND: Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has b...

A causal perspective on brainwave modeling for brain-computer interfaces.

Journal of neural engineering
. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a contro...

A systematic analysis of regression models for protein engineering.

PLoS computational biology
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

PLoS computational biology
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Ye...

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.

PloS one
BACKGROUND: The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and res...

Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients.

Expert opinion on drug safety
OBJECTIVE: Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Environmental science & technology
Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of toxicity data. We the...

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

Physical and engineering sciences in medicine
To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT s...

Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization--XGBoost machine learning model can be interpreted based on SHAP.

Intensive & critical care nursing
BACKGROUND: The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infections and non-healing wounds.