AIMC Topic: Machine Learning

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Using Sensor Data to Detect Lameness and Mastitis Treatment Events in Dairy Cows: A Comparison of Classification Models.

Sensors (Basel, Switzerland)
The aim of this study was to develop classification models for mastitis and lameness treatments in Holstein dairy cows as the target variables based on continuous data from herd management software with modern machine learning methods. Data was colle...

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.

International journal of environmental research and public health
The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained undere...

Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort.

BMC medicine
BACKGROUND: The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among prete...

Identification of Patients with Heart Failure in Large Datasets.

Heart failure clinics
Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of case...

Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Nature reviews. Gastroenterology & hepatology
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome anal...

Comparing the capabilities of transfer learning models to detect skin lesion in humans.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This arti...

Bayer's in silico ADMET platform: a journey of machine learning over the past two decades.

Drug discovery today
Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early dru...

Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Psychopharmacology
RATIONALE: Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior th...

Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

Annals of neurology
OBJECTIVE: There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries.

Time-resolved neurotransmitter detection in mouse brain tissue using an artificial intelligence-nanogap.

Scientific reports
The analysis of neurotransmitters in the brain helps to understand brain functions and diagnose Parkinson's disease. Pharmacological inhibition experiments, electrophysiological measurement of action potentials, and mass analysers have been applied f...