AIMC Topic: Algorithms

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Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V-A Practical Approach to Regression Problems.

Acta neurochirurgica. Supplement
This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outcome. We supply fully structured code for the readers to download and execute in parallel to this sect...

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV-A Practical Approach to Binary Classification Problems.

Acta neurochirurgica. Supplement
We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for example, the occurrence of a complication, in the statistical programming language R. To illustrate...

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...

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...

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis.

Journal of X-ray science and technology
BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even mo...

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 ...

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints.

Methods in molecular biology (Clifton, N.J.)
The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug d...

Classification by a stacking model using CNN features for COVID-19 infection diagnosis.

Journal of X-ray science and technology
Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumon...

Artificial intelligence: opportunities in lung cancer.

Current opinion in oncology
PURPOSE OF REVIEW: In this article, we focus on the role of artificial intelligence in the management of lung cancer. We summarized commonly used algorithms, current applications and challenges of artificial intelligence in lung cancer.

Advanced Deep Learning Algorithms for Infectious Disease Modeling Using Clinical Data: A Case Study on COVID-19.

Current medical imaging
BACKGROUND: Dealing with the COVID-19 pandemic has been one of the most important objectives of many countries.Intently observing the growth dynamics of the cases is one way to accomplish the solution for the pandemic.