AI Medical Compendium Topic

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Data Interpretation, Statistical

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Application of Ontology Technology in Health Statistic Data Analysis.

Studies in health technology and informatics
UNLABELLED: Research Purpose: establish health management ontology for analysis of health statistic data. Proposed Methods: this paper established health management ontology based on the analysis of the concepts in China Health Statistics Yearbook, a...

Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches.

Statistical applications in genetics and molecular biology
Modern biological experiments often involve high-dimensional data with thousands or more variables. A challenging problem is to identify the key variables that are related to a specific disease. Confounding this task is the vast number of statistical...

Statistical Inference for Data Adaptive Target Parameters.

The international journal of biostatistics
Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples...

Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences.

The international journal of biostatistics
Comparing the relative fit of competing models can be used to address many different scientific questions. In classical statistics one can, if appropriate, use likelihood ratio tests and information based criterion, whereas clinical medicine has tend...

Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.

The international journal of biostatistics
This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treat...

SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions.

Combinatorial chemistry & high throughput screening
BACKGROUND: Docking allows to predict ligand binding to proteins, since the 3D-structure for the target is available. Several docking studies have been carried out to identify potential ligands for drug targets. Many of these studies resulted in the ...

Bioimage Informatics for Big Data.

Advances in anatomy, embryology, and cell biology
Bioimage informatics is a field wherein high-throughput image informatics methods are used to solve challenging scientific problems related to biology and medicine. When the image datasets become larger and more complicated, many conventional image a...

Machine learning: Trends, perspectives, and prospects.

Science (New York, N.Y.)
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core...

Should You Trust Your Money to a Robot?

Big data
Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. I contrast human and machine strengths and weaknesses in making investment decisions. The analysis rev...

Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.

Research report (Health Effects Institute)
INTRODUCTION: The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. However, the National Academies of S...