[HTML][HTML] Machine learning in healthcare
H Habehh, S Gohel - Current genomics, 2021 - ncbi.nlm.nih.gov
Abstract Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML)
technology have brought on substantial strides in predicting and identifying health …
technology have brought on substantial strides in predicting and identifying health …
Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain mapping approaches to multivariate predictive models …
using traditional univariate brain mapping approaches to multivariate predictive models …
[HTML][HTML] Brain–phenotype models fail for individuals who defy sample stereotypes
Individual differences in brain functional organization track a range of traits, symptoms and
behaviours,,,,,,,,,,–. So far, work modelling linear brain–phenotype relationships has …
behaviours,,,,,,,,,,–. So far, work modelling linear brain–phenotype relationships has …
[HTML][HTML] Evaluation of a decided sample size in machine learning applications
D Rajput, WJ Wang, CC Chen - BMC bioinformatics, 2023 - Springer
Background An appropriate sample size is essential for obtaining a precise and reliable
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …
[HTML][HTML] Ten simple rules for predictive modeling of individual differences in neuroimaging
Establishing brain-behavior associations that map brain organization to phenotypic
measures and generalize to novel individuals remains a challenge in neuroimaging …
measures and generalize to novel individuals remains a challenge in neuroimaging …
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
Sample-size determination methodologies for machine learning in medical imaging research: a systematic review
I Balki, A Amirabadi, J Levman… - Canadian …, 2019 - journals.sagepub.com
Purpose The required training sample size for a particular machine learning (ML) model
applied to medical imaging data is often unknown. The purpose of this study was to provide …
applied to medical imaging data is often unknown. The purpose of this study was to provide …
[HTML][HTML] Individual variation in functional topography of association networks in youth
The spatial distribution of large-scale functional networks on the cerebral cortex differs
between individuals and is particularly variable in association networks that are responsible …
between individuals and is particularly variable in association networks that are responsible …
Influence of sample size and analytic approach on stability and interpretation of brain‐behavior correlations in task‐related fMRI data
Limited statistical power due to small sample sizes is a problem in fMRI research. Most of the
work to date has examined the impact of sample size on task‐related activation, with less …
work to date has examined the impact of sample size on task‐related activation, with less …
[HTML][HTML] Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Cognitive performance can be predicted from an individual's functional brain connectivity
with modest accuracy using machine learning approaches. As yet, however, predictive …
with modest accuracy using machine learning approaches. As yet, however, predictive …