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Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

Proactive screening for depression through metaphorical and automatic text analysis.

Yair Neuman¹, Yohai Cohen, Dan Assaf

¹Department of Education, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel. yneuman@bgu.ac.il

Artificial Intelligence in Medicine

|July 10, 2012

Summary

This summary is machine-generated.

This study introduces Pedesis, a system for automatic depression screening in texts. It improves public health by identifying depression signs with high accuracy, aiding early intervention.

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Area of Science:

Computational linguistics
Public health informatics
Mental health technology

Background:

Automated depression screening is crucial for public health.
Existing methods face challenges in proactive and accurate detection.
The Pedesis system aims to address these limitations.

Purpose of the Study:

To describe a novel system, Pedesis, for proactive and automatic depression screening.
To leverage web-harvested metaphorical relations for depression lexicon construction.
To evaluate the system's efficacy in identifying depression in textual data.

Main Methods:

The Pedesis system extracts metaphorical relations and conceptual domains related to depression from the web.
A "depression lexicon" is created by human experts using extracted information.
The lexicon is employed to automatically assess depression levels and topic relevance in texts.

Main Results:

The system demonstrated a 9% improvement in predicting depression-related queries on a mental health site.
On a blog corpus, Pedesis achieved an 84.2% classification rate for detecting depression signs (p<.001).
Human expert comparison yielded an average precision of 78% and recall of 76%.

Conclusions:

Automatic text-based depression screening is feasible and beneficial for the mental health system.
The Pedesis system offers a promising tool for enhancing public health surveillance.
Further integration of such technologies can improve mental health support and early detection.