A domain-agnostic approach for characterization of lifelong learning systems.

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system.

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs.

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model.

Artificial intelligence for pediatric ophthalmology.

Purpose Of Review: Despite the impressive results of recent artificial intelligence applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric patients and how artificial intelligence techniques can address these challenges, surveys recent applications to pediatric ophthalmology, and discusses future directions.

Recent Findings: The most significant advances involve the automated detection of retinopathy of prematurity, yielding results that rival experts.

MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine.

Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients.

Increased severity of alcoholic liver injury in female verses male rats: a microarray analysis.

Alcoholic liver disease (ALD) is an increasingly recognized condition that may progress to end-stage liver disease. In addition to alcohol consumption, genetic factors, dietary fatty acids, gender and viral infection potentiate the severity of alcoholic liver injury. In humans, significant gender differences in susceptibility to ALD are observed.

Transcriptional networks in a rat model for nonalcoholic fatty liver disease: a microarray analysis.

Background: Nonalcoholic fatty liver disease (NAFLD) is a common hepatic condition that may progress to end-stage liver disease. High-fat diets in animals reproduce many of the features found in nonalcoholic steatohepatitis.

Objective: To understand how various dietary or genetic factors influence the development of fatty liver and consequently NAFLD, we performed microarray-based expression profiling of genes, induced by fish oil and dextrose diet, a putative mediator of alcohol-like effects on the liver of the female rat.