Results 101 to 110 of about 267,421 (217)
Prediction-Constrained Topic Models for Antidepressant Recommendation [PDF]
arXiv, 2017 Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervised latent Dirichlet allocation that balances two goals: faithful generative explanations of high-dimensional data and accurate prediction of associated class labels. Existing
arxiv
Iatrogenic epilepsy due to antidepressant drugs [PDF]
, 1969 V. Dallos, K. W. G. Heathfield
openalex +1 more source
Pharmaceutical Sciences and Research, 2019 Schizophrenic patients who used antidepressant combination drug therapy in their treatment can cause drug interaction. This study aimed to determine the potential drug interactions of antidepressant in schizophrenic patients. The study designed was cross-
Atika Wahyu Puspitasari +1 more
doaj
Studies on the peripheral pharmacology of fenazoxine, a potential antidepressant drug [PDF]
, 1969 J.R. Bassett +3 more
openalex +1 more source
Neuropsychiatric Disease and Treatment, 2018 Zoë Zuilhof, Sandhaya Norris, Claude Blondeau, Pierre Tessier, Pierre Blier Department of Psychiatry, University of Ottawa, The Royal Ottawa Institute of Mental Health Research, Ottawa, ON, Canada Introduction: This study investigated if optimized
Zuilhof Z +4 more
doaj
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning [PDF]
arXiv, 2019 The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images ...
arxiv
The effect of antidepressants and “tranquillizers” on the response of mice to ethanol [PDF]
, 1968 Gerald Milner
openalex +1 more source