AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Relieving the Incompatibility of Network Representation and Classification for Long-Tailed Data Distribution.

Computational intelligence and neuroscience
In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes wil...

A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in ...

Machine learning in systematic reviews: Comparing automated text clustering with Lingo3G and human researcher categorization in a rapid review.

Research synthesis methods
Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automat...

Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses.

BMC medical research methodology
BACKGROUND: Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial ...

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: This study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implemen...

Comparison of the Meta-Active Machine Learning Model Applied to Biological Data-Driven Experiments with Other Models.

Journal of healthcare engineering
Currently, many methods that could estimate the effects of conditions on a given biological target require either strong modelling assumptions or separate screens. Traditionally, many conditions and targets, without doing all possible experiments, co...

Mixed Script Identification Using Automated DNN Hyperparameter Optimization.

Computational intelligence and neuroscience
Mixed script identification is a hindrance for automated natural language processing systems. Mixing cursive scripts of different languages is a challenge because NLP methods like POS tagging and word sense disambiguation suffer from noisy text. This...

Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.

Molecular cancer research : MCR
Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tool...

Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review.

BMJ open
OBJECTIVE: To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for...

A high-resolution trajectory data driven method for real-time evaluation of traffic safety.

Accident; analysis and prevention
Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are com...