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Related Concept Videos

Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

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Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
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Depression: Overview01:18

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about...
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Related Experiment Video

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A New Method for Inducing a Depression-Like Behavior in Rats
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Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

Chiaming Tung1, Wenhsiang Lu1

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC.

Artificial Intelligence in Medicine
|December 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced event extraction (E3) method and an event-driven depression tendency warning (EDDTW) model to predict depression from web posts. The EDDTW model shows promise for early detection of major depressive disorder.

Keywords:
Depression tendencyNegative emotionNegative eventPart of speech pattern

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Natural language processing

Background:

  • The internet serves as a platform for individuals to express daily moods and feelings.
  • Analyzing online text for mental health indicators is an emerging area of research.

Purpose of the Study:

  • To investigate text-mining technology for analyzing and predicting depression tendency in web posts.
  • To develop a model for early detection of major depressive disorder through online content analysis.

Main Methods:

  • Defined depression factors: negative events, emotions, symptoms, and thoughts from web posts.
  • Proposed an enhanced event extraction (E3) method to identify negative event terms.
  • Developed an event-driven depression tendency warning (EDDTW) model to predict depression risk.

Main Results:

  • The EDDTW model achieved a recall rate of 0.668 and an F-measure of 0.624.
  • The enhanced event extraction method improved recall by expanding the negative event lexicon.
  • The EDDTW model can track depression tendency trends for individual authors, aiding clinical assessment.

Conclusions:

  • An E3 method was presented for extracting negative event terms from web posts.
  • A novel EDDTW model was proposed to predict depression tendency in web posts.
  • The EDDTW model may facilitate early detection of major depressive disorder in online authors.