AIMC Topic: Machine Learning

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Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.

International journal of environmental research and public health
While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interaction...

Expanding the drug discovery space with predicted metabolite-target interactions.

Communications biology
Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite-host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potenti...

Individualized prediction of COVID-19 adverse outcomes with MLHO.

Scientific reports
The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, includi...

seqQscorer: automated quality control of next-generation sequencing data using machine learning.

Genome biology
Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based ...

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

IEEE journal of biomedical and health informatics
A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism...

StoCast: Stochastic Disease Forecasting With Progression Uncertainty.

IEEE journal of biomedical and health informatics
Forecasting patients' disease progressions with rich longitudinal clinical data has drawn much attention in recent years due to its impactful application in healthcare and the medical field. Researchers have tackled this problem by leveraging traditi...

Disease Prediction via Graph Neural Networks.

IEEE journal of biomedical and health informatics
With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e.g., symptoms, patient demogr...

LSTM-based approach for predicting periodic motions of an impacting system via transient dynamics.

Neural networks : the official journal of the International Neural Network Society
Dynamically impacting systems are characterised with inherent instability and complex non-linear phenomena which makes it practically difficult to predict the steady state response of the system at transient periods. This study investigates the abili...

Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images.

International journal of legal medicine
Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learning techniques have been applied for age estimation recently but lacking performance comparison betwee...

Machine Learning-Based Prediction of Small Intracranial Aneurysm Rupture Status Using CTA-Derived Hemodynamics: A Multicenter Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Small intracranial aneurysms are being increasingly detected while the rupture risk is not well-understood. We aimed to develop rupture-risk models of small aneurysms by combining clinical, morphologic, and hemodynamic informa...