Machine learning and artificial intelligence in haematology
Digitalization of the medical record and integration of genomic methods into clinical practice
have resulted in an unprecedented wealth of data. Machine learning is a subdomain of …
have resulted in an unprecedented wealth of data. Machine learning is a subdomain of …
A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma
M Khan, J Lin, B Wang, C Chen, Z Huang… - Frontiers in …, 2022 - frontiersin.org
Background Gastric cancer (GC) represents a major global clinical problem with very limited
therapeutic options and poor prognosis. Necroptosis, a recently discovered inflammatory …
therapeutic options and poor prognosis. Necroptosis, a recently discovered inflammatory …
CDKN2A homozygous deletion is a strong adverse prognosis factor in diffuse malignant IDH-mutant gliomas
Abstract Background The 2016 World Health Organization (WHO) classification of central
nervous system tumors stratifies isocitrate dehydrogenase (IDH)–mutant gliomas into 2 …
nervous system tumors stratifies isocitrate dehydrogenase (IDH)–mutant gliomas into 2 …
Nonparametric machine learning and efficient computation with Bayesian additive regression trees: The BART R package
In this article, we introduce the BART R package which is an acronym for Bayesian additive
regression trees. BART is a Bayesian nonparametric, machine learning, ensemble …
regression trees. BART is a Bayesian nonparametric, machine learning, ensemble …
Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival
H Ishwaran, M Lu - Statistics in medicine, 2019 - Wiley Online Library
Random forests are a popular nonparametric tree ensemble procedure with broad
applications to data analysis. While its widespread popularity stems from its prediction …
applications to data analysis. While its widespread popularity stems from its prediction …
Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score
OBJECTIVE To develop and validate a novel, machine learning–derived model to predict
the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM) …
the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM) …
Random survival forests
We introduce random survival forests, a random forests method for the analysis of right-
censored survival data. New survival splitting rules for growing survival trees are introduced …
censored survival data. New survival splitting rules for growing survival trees are introduced …
[HTML][HTML] Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models
Translating the vast data generated by genomic platforms into accurate predictions of
clinical outcomes is a fundamental challenge in genomic medicine. Many prediction …
clinical outcomes is a fundamental challenge in genomic medicine. Many prediction …
[图书][B] Statistical learning from a regression perspective
RA Berk - 2008 - Springer
This chapter launches a more detailed examination of statistical learning within a regression
framework. Once again, the focus is on conditional distributions. How does the conditional …
framework. Once again, the focus is on conditional distributions. How does the conditional …
Analysing the impact of multiple stressors in aquatic biomonitoring data: A 'cookbook'with applications in R
Multiple stressors threaten biodiversity and ecosystem integrity, imposing new challenges to
ecosystem management and restoration. Ecosystem managers are required to address and …
ecosystem management and restoration. Ecosystem managers are required to address and …