2017
DOI: 10.3233/thc-161289
|View full text |Cite
|
Sign up to set email alerts
|

A model for continuous monitoring of patients with major depression in short and long term periods

Abstract: The final publication is available at IOS Press through http://dx.doi.org/10.3233/THC-161289BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
3
1
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 34 publications
0
3
0
Order By: Relevance
“…Agnisarman et al [22] have designed a remote video medical diagnosis system, which used the Internet of Things technology to facilitate the doctor's medical diagnosis process, with multifunctional modes such as online consultation and video dialogue. Mugica et al [23] designed an ECG monitor matched with the Android system's intelligent terminal to realize remote monitoring. Tamilselvi et al [24] proposed a health monitoring system that can assess patients' primary symptoms like their oxygen level, body temperature, and eye movement using the IoT platform.…”
Section: Introductionmentioning
confidence: 99%
“…Agnisarman et al [22] have designed a remote video medical diagnosis system, which used the Internet of Things technology to facilitate the doctor's medical diagnosis process, with multifunctional modes such as online consultation and video dialogue. Mugica et al [23] designed an ECG monitor matched with the Android system's intelligent terminal to realize remote monitoring. Tamilselvi et al [24] proposed a health monitoring system that can assess patients' primary symptoms like their oxygen level, body temperature, and eye movement using the IoT platform.…”
Section: Introductionmentioning
confidence: 99%
“…Due to these limitations, efforts are also being made to identify barriers to the use of mobile apps for depression treatment. 68 Recently, as technology has advanced, attempts have been made to detect depression based on electroencephalograms (EEGs), deep learning, and machine learning in various applications in the medical field. 69 For example, there have been attempts to predict depression using machine learning random forest, k-nearest neighbor algorithm, or SVM.…”
Section: Related Researchmentioning
confidence: 99%
“…Moreover, state-of-the-art treatment involves steady monitoring of depressive individuals, which could be realised with continuous measurements of severity-related language patterns. Such automatized language detection related to depression severity could be used as an early warning system to prevent relapse [36]. Relapse is a major driver of health related costs and personal burden in relation to depression.…”
Section: Prediction Of Depressive Symptoms Through Word Usementioning
confidence: 99%