AIMC Topic: Machine Learning

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Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep.

Biometrics
Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety. To date, the FDA approves locked algorithms prior to marketing and requires future updates to undergo separate premarket ...

Predicting the first smoking lapse during a quit attempt: A machine learning approach.

Drug and alcohol dependence
BACKGROUND: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate d...

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting.

Epilepsia
OBJECTIVE: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessm...

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools.

The Science of the total environment
The overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. ...

Application of deep learning in genomics.

Science China. Life sciences
In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification, autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in ...

Personalized treatment for coronary artery disease patients: a machine learning approach.

Health care management science
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electron...

Machine learning and individual variability in electric field characteristics predict tDCS treatment response.

Brain stimulation
BACKGROUND: Transcranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delive...

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.

Journal of computer-aided molecular design
Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in...

A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide, where high rates of no response to treatment or relapse and delays in receiving a proper diagnosis ...