AIMC Topic: Machine Learning

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A novel stacking technique for prediction of diabetes.

Computers in biology and medicine
BACKGROUND: Machine Learning (ML) represents a rapidly growing technology that supplies the most effective solutions for solving complex problems. The application of ML techniques in healthcare is gaining more attention because of ML-associated autom...

GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning.

Journal of chemical information and modeling
Machine learning classifiers trained on class imbalanced data are prone to overpredict the majority class. This leads to a larger misclassification rate for the minority class, which in many real-world applications is the class of interest. For binar...

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

BMJ open
PURPOSE: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challeng...

MLatom 2: An Integrative Platform for Atomistic Machine Learning.

Topics in current chemistry (Cham)
Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input ...

COVID-19 detection using federated machine learning.

PloS one
The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates usin...

Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.

Orthodontics & craniofacial research
OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers.

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Brain and language
Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language diso...

Liver disease classification from ultrasound using multi-scale CNN.

International journal of computer assisted radiology and surgery
PURPOSE: Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subj...

In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods.

Chemical biology & drug design
Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration...

An Ensemble Learning Method for Robot Electronic Nose with Active Perception.

Sensors (Basel, Switzerland)
The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is diffi...