AIMC Topic: Biostatistics
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Large Language Models can Help with Biostatistics and Coding Needed in Radiology Research.
Academic radiology
Oct 15, 2024
INTRODUCTION: Original research in radiology often involves handling large datasets, data manipulation, statistical tests, and coding. Recent studies show that large language models (LLMs) can solve bioinformatics tasks, suggesting their potential in...
Universal probabilistic programming offers a powerful approach to statistical phylogenetics.
Communications biology
Feb 24, 2021
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabi...
At the intersection of machine learning, biology, and health: an interview with Lorin Crawford.
Communications biology
Jan 4, 2021
Lorin Crawford began his independent career at Brown University School of Public Health with his own lab in the summer of 2017. He is currently a Senior Researcher at Microsoft Research, New England while also keeping his faculty position at Brown Un...
A non-parametric effect-size measure capturing changes in central tendency and data distribution shape.
PloS one
Sep 24, 2020
MOTIVATION: Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treat...
Combining deep learning with token selection for patient phenotyping from electronic health records.
Scientific reports
Jan 29, 2020
Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information a...
What should medical students know about artificial intelligence in medicine?
Journal of educational evaluation for health professions
Jul 3, 2019
Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical prof...
Evaluating classification accuracy for modern learning approaches.
Statistics in medicine
Jan 30, 2019
Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily av...
A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.
Biometrical journal. Biometrische Zeitschrift
May 16, 2018
Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two st...
Artificial Intelligence in Medical Practice: The Question to the Answer?
The American journal of medicine
Nov 7, 2017
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice rem...
Collaborative targeted learning using regression shrinkage.
Statistics in medicine
Nov 2, 2017
Causal inference practitioners are routinely presented with the challenge of model selection and, in particular, reducing the size of the covariate set with the goal of improving estimation efficiency. Collaborative targeted minimum loss-based estima...
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