A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Model predicts cannabis use disorder risk for adolescents and young adults.
Study identifies key health behaviors linked to adolescent suicide attempts.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
This paper aims to analyse adolescents' road accidents in Tuscany. The analysis is based on the Database Edit of Osservatorio di Epidemiologia della Toscana. Complexity and heterogeneity of Edit's data represet an interesting scope to apply Machine Learning methods. In particular, in this paper is proposed an analysis …
In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different…
A methodology for binary classification of EEG records which correspond to different mental states is proposed. This model-free methodology is based on our theory of the -complexity of continuous functions which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle mult…
Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we dev…
The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity (FC) measures of three sets of data, derived from resti…
The use of electronic cigarette (e-cigarette) is increasing among adolescents. This is problematic since consuming nicotine at an early age can cause harmful effects in developing teenager's brain and health. Additionally, the use of e-cigarette has a possibility of leading to the use of cigarettes, which is more sever…
Study identifies risk factors for subsequent suicide attempts in youth.
Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis th…
Bayesian method infers contextual bandit policies robustly.
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new var…
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal,…
New robust method for high-dimensional data analysis in imaging studies.
Machine learning improves ASD diagnosis accuracy.
Integrates neural encoders into GLMMs for multimodal data analysis.
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connecti…
ST-GCN improves rs-fMRI prediction accuracy by modeling spatio-temporal graph connectivity.
Deep-SITAR uses autoencoders to predict growth patterns.
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the …
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of brain anatomy segmentation, hundreds or thousands of weakly-labeled volumes are often used as training data. In this paper, we first observe…
Bayesian Deep Noise Neural Network (B-DeepNoise) estimates predictive densities and uncertainty.
Selective inference improves multi-task neuroimaging analysis.
Proposes a novel method to identify complex effects in multi-view datasets.