Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment recommendations, deep
neural networks and in particular convolutional neural networks have advanced to a …
neural networks and in particular convolutional neural networks have advanced to a …
A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
Importance Biological psychiatry aims to understand mental disorders in terms of altered
neurobiological pathways. However, for one of the most prevalent and disabling mental …
neurobiological pathways. However, for one of the most prevalent and disabling mental …
Shared and specific patterns of structural brain connectivity across affective and psychotic disorders
Background Altered brain structural connectivity has been implicated in the pathophysiology
of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major …
of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major …
Multiple sclerosis endophenotypes identified by high-dimensional blood signatures are associated with distinct disease trajectories
CC Gross, A Schulte-Mecklenbeck… - Science Translational …, 2024 - science.org
One of the biggest challenges in managing multiple sclerosis is the heterogeneity of clinical
manifestations and progression trajectories. It still remains to be elucidated whether this …
manifestations and progression trajectories. It still remains to be elucidated whether this …
Predicting intelligence from brain gray matter volume
A positive association between brain size and intelligence is firmly established, but whether
region-specific anatomical differences contribute to general intelligence remains an open …
region-specific anatomical differences contribute to general intelligence remains an open …
Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans
C Brossard, J Grèze, JA de Busschère, A Attyé… - Scientific Reports, 2023 - nature.com
The prediction of the therapeutic intensity level (TIL) for severe traumatic brain injury (TBI)
patients at the early phase of intensive care unit (ICU) remains challenging. Computed …
patients at the early phase of intensive care unit (ICU) remains challenging. Computed …
Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning
A Gallardo-Pizarro, O Peyrony… - Expert Review of Anti …, 2024 - Taylor & Francis
Introduction Artificial intelligence (AI) and machine learning (ML) have the potential to
revolutionize the management of febrile neutropenia (FN) and drive progress toward …
revolutionize the management of febrile neutropenia (FN) and drive progress toward …
[HTML][HTML] Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
The fast-paced development of machine learning (ML) and its increasing adoption in
research challenge researchers without extensive training in ML. In neuroscience, ML can …
research challenge researchers without extensive training in ML. In neuroscience, ML can …
A systematic evaluation of machine learning-based biomarkers for major depressive disorder across modalities
Background Biological psychiatry aims to understand mental disorders in terms of altered
neurobiological pathways. However, for one of the most prevalent and disabling mental …
neurobiological pathways. However, for one of the most prevalent and disabling mental …
Deep learning enabled surrogate model of complex food processes for rapid prediction
D Ghosh, A Datta - Chemical Engineering Science, 2023 - Elsevier
Computer-aided mechanistic modeling of food, while robust and capable of producing
detailed solutions, demands massive computational resources and runtime precluding …
detailed solutions, demands massive computational resources and runtime precluding …