A Health Social Network Recommender System

Song, Insu; Dillon, Denise; Goh, Tze Jui; Min, Sung Hee

doi:10.1007/978-3-642-25044-6_29

Cited by 23 publications

(15 citation statements)

References 14 publications

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“…Three themes were identified among studies examining treatment and support: (i) ML with mobile and sensor data to detect changes in behaviour indicative of mental health conditions (Salafi and Kah, 2015; Chalmers et al ., 2016); (ii) ML to provide personalised and timely treatment or interventions (Auer and Griffiths, 2018; Bae et al ., 2018 a ; Chen et al ., 2017 b ; Yang et al ., 2017); and, (iii) analysis of online support groups for mental health communities (Song et al ., 2011; Nguyen et al ., 2014 a , 2014 b ; Deetjen and Powell, 2016; Kavuluru et al ., 2016; Thin et al ., 2017). The studies identified in this category demonstrate several benefits of ML for treatment and support.…”

Section: Resultsmentioning

confidence: 99%

Machine learning in mental health: a scoping review of methods and applications

2019

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BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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Section: Resultsmentioning

confidence: 99%

Machine learning in mental health: a scoping review of methods and applications

2019

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“…Three themes were identified among studies examining treatment and support: (i) ML with mobile and sensor data to detect changes in behaviour indicative of mental health conditions [213,214]; (ii) ML to provide personalised and timely treatment or interventions [215][216][217][218]; and, (iii) analysis of online support groups for mental health communities [219][220][221][222][223][224]. The studies identified in this category demonstrate several benefits of ML for treatment and support.…”

Section: Detection and Diagnosismentioning

confidence: 99%

“…Further, ML techniques were used with mobile sensor and survey data to provide personalised and timely intervention for depression [216], gambling addiction [217] and alcohol dependency [218] with positive results. Additional benefits have been demonstrated when using ML with data from online communities, such as matching patients to suitable support communities [219] and automatic moderation of helpful comments in suicide and autism support groups [223,224].…”

Section: Detection and Diagnosismentioning

confidence: 99%

Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

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ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

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“…The speedy expansion of social networks has given researchers opportunities to study social processes, interactions, and relationships [12][13][14][15][16]. Furthermore mobile phone peneration rate in the world has given opporunities to provide cost effective finance, education, and health services [17][18][19][20][21].…”

Section: B Performance Issue In Visualization For Big Datamentioning

confidence: 99%

Centrality clustering-based sampling for big data visualization

Nguyen

Song

2016

2016 International Joint Conference on Neural Networks (IJCNN)

Self Cite

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Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in recent years. One of the major difficulties of information visualization is performance, and this is even more critical when visualizing big data. One potential solution to this challenge is data sampling while maintaining fidelity of visual representation. In this paper, we propose two new centrality clustering-based sampling approaches that apply centrality measures on clusters of data points in order to make more informed sampling than random sampling approaches. We evaluate the new methods on graph data sets. The results show that the new methods significantly outperform existing data sampling methods in term of perceived differences and their ability to preserve essential visual information. Moreover computational complexity is comparable or even better than simple random sampling methods.

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A Health Social Network Recommender System

Cited by 23 publications

References 14 publications

Machine learning in mental health: a scoping review of methods and applications

Machine learning in mental health: a scoping review of methods and applications

Machine learning in mental health: A systematic scoping review of methods and applications

Centrality clustering-based sampling for big data visualization

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