Sentence examples for mixed features from inspiring English sources

The replacement of the DSM-IV-TR diagnostic criteria for a mixed episode with the new 'With Mixed Features' specifier in DSM-5 demonstrates a growing recognition of the clinical relevance of subthreshold depression during mania.

The DSM-5 'With Mixed Features' specifier, and accompanying patient-rated Mini-International Neuropsychiatric Interview (M.I.N.I).

Archaeologists have been debating for years whether the fossil record shows evidence of individuals with mixed features.

By checking the variable importance ranked by the model, machine could feed us back with clinical features of narcolepsy on machine scale (i.e., machine tells human how it ranks the mixed features of narcolepsy in EU-NN database).

Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics.

Based on the proposed models, we give the definition of significance of mixed features and construct a greedy attribute reduction algorithm.

"The fact that experts differ over whether to classify these specimens as Australopithecine or human indicates the mixed features that they display, and the fossils provide valuable clues to the evolutionary changes that led to the first members of the human genus".

The aim of this study was to describe the phenomenology of mania and depression in bipolar patients experiencing a manic episode with mixed features as defined in the new Diagnostic and Statistical Manual of Mental Disorders (DSM-5).

We propose an algorithm that can convert a feature model with mixed features (both positive and negative) to a feature model containing either only positive or only negative features.

In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities.

High-order bidirectional associative memory (BAM) neural networks with mixed features such as time delays and impulses play an increasingly important role in the design and implementation of neural network systems.