AIMC Topic: Machine Learning

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Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II-Generalization and Overfitting.

Acta neurochirurgica. Supplement
We review the concept of overfitting, which is a well-known concern within the machine learning community, but less established in the clinical community. Overfitted models may lead to inadequate conclusions that may wrongly or even harmfully shape c...

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I-Introduction and General Principles.

Acta neurochirurgica. Supplement
We provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modeling, which is the focus of this series. In particular, we define the terms machine learn...

Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus.

Acta neurochirurgica. Supplement
The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the "big data" era, increasing computing power even on mobile devices, and online training resources have both led to...

The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications.

Current cardiology reviews
Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We ...

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.

Critical care clinics
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care un...

Vaccine Design by Reverse Vaccinology and Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction m...

Machine Learning for In Silico ADMET Prediction.

Methods in molecular biology (Clifton, N.J.)
ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safet...

Machine Learning from Omics Data.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the use of ML approaches in larger omics studies. This work provides a guide through...

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.

Methods in molecular biology (Clifton, N.J.)
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their intera...