AIMC Topic: Machine Learning

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Embracing machine learning and digital health technology for precision dermatology.

The Journal of dermatological treatment
Involvement from the dermatology community in the current revolution in sensors, personal health devices, computing, and machine learning algorithms can enable us to reach the promise of precision medicine in dermatology to deliver the 'right treatme...

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system.

Chemosphere
Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have sy...

A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy.

NeuroImage
In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume...

SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.

Journal of immunology (Baltimore, Md. : 1950)
Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive...

Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty.

The Journal of arthroplasty
BACKGROUND: Postoperative recovery after total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study is to develop machine learning algorithms...

Artificial Intelligence for Pharma: Time for Internal Investment.

Trends in pharmacological sciences
Artificial intelligence (AI) has achieved human-level capabilities and continues to rapidly improve. For the pharmaceutical industry, AI can improve decision-making and transform the quest for better medicines. The keys are investing in data manageme...

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

The journal of physical chemistry. B
Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifyin...

Understanding health management and safety decisions using signal processing and machine learning.

BMC medical research methodology
BACKGROUND: Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time-...