AIMC Topic: Machine Learning

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Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.

Physical and engineering sciences in medicine
Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diagnosis accessible and more reliable in high burden TB countries, digital chest radiography has become...

Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider.

Clinical therapeutics
Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in pharmacovigilance (PV). A better understanding of the scope of artificial intelligence in pharmacovigilance (AIPV) may be advantageous to more sharply defi...

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity ...

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning.

BMC bioinformatics
BACKGROUND: Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing th...

A unified machine learning approach to time series forecasting applied to demand at emergency departments.

BMC emergency medicine
BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provid...

A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

Breast cancer (Tokyo, Japan)
OBJECTIVE: The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomog...

Application of artificial intelligence and machine learning for prediction of oral cancer risk.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND: Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on ...

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Journal of the American Heart Association
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decis...

Skin tear classification using machine learning from digital RGB image.

Journal of tissue viability
AIM: Skin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to...