Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Chang, Yue-Shan; Fan, Chih-Tien; Lo, Win-Tsung; Hung, Wan-Chun; Yuan, Shyan-Ming

doi:10.1016/j.future.2014.05.004

Cited by 47 publications

(29 citation statements)

References 34 publications

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“…Следует отметить, что рассматриваются именно пропозициональные формулы, а не их истинностные значения. Более того, в дальнейших выкладках рассматриваются классы эквивалент-ности пропозициональных формул; в частности, конъюнкции над одними и теми же формулами, но с разным порядком следования последних, не различаются [2,3,8,12]. , 22].…”

Section: определения обозначения и базовые результатыunclassified

“…Байесовские сети, в том числе алгебраические байесовские сети (АБС) [2,3] и связанные с ними конструкции, являются одним из видов вероятностных графических моделей. Байесовские сети все шире используются в системах распознавания образов [4,5], системах моделирования поведения [6], системах моделирования рисков и обнаружения отказов [7][8][9], оценивания повреждений и мониторинга состояния здоровья [10][11][12][13], а также при анализе климата и окружающей среды [14,15].…”

Section: Introductionunclassified

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Matrix-vector algorithms of local posteriori inference in algebraic bayesian networks on quanta propositions

Алексеевич¹,

Львович²,

Владимирович³

2015

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Section: определения обозначения и базовые результатыunclassified

Section: Introductionunclassified

Matrix-vector algorithms of local posteriori inference in algebraic bayesian networks on quanta propositions

Алексеевич¹,

Львович²,

Владимирович³

2015

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…It has been used in diverse applications such as assessment of probabilities of falling down from height at work (Alizadeh et al 2014), supplier selection (Dogan and Aydin 2011), environmental modelling (Uusitalo et al 2005;Celio et al 2014), health applications (Chang et al 2015), etc. In this study, we use BBNs to construct a model depicting the causal relationships between various attributes of a replenishment system in the Army and, then, conducting a sensitivity analysis to arrive at the most critical of these attributes.…”

Section: Introductionmentioning

confidence: 99%

Bayesian belief network for assessing impact of factors on army’s lean–agile replenishment system

Sharma

Kulkarni

2016

Journal of Military Studies

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A dynamic time-separated lean-agile spare part replenishment system can prove beneficial to the army by being efficient (cost saving) during peace and effective (assured availability) during war. The logistics echelons must have certain attributes in order to implement such a dynamic replenishment system. The purpose of this article is to identify the factors/attributes that are necessary in a spare part replenishment system of vehicles and weapon platforms in order to implement a time-separated leanagile strategy through a systematic literature review. Furthermore, the article will investigate the impact of these factors/attributes, individually and collectively, on overall system performance. This will enable logistics managers to focus only on the factors that have greater impact on the system. A model explaining the effects of various contributory factors/attributes on the overall logistics system has been developed through a comprehensive literature review, experts' judgments and inputs from practising logisticians in the military field. The article then models the system using a Bayesian belief network (BBN) on Netica software. After the development of the model using Netica, a sensitivity analysis based on the mutual information criterion is conducted to identify the critical factors that most significantly affect a dynamic lean-agile spare part replenishment system. The study addresses the identified need of applying BBN to model an uncertain and complex military logistics domain.

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“…In addition, with the advances of information technology, there is increasing research [1,2,3] aiming at assisting depression diagnosis and treatment have been proposed. However, it is difficult to continuously observe a person's physical and psychological conditions, even though it has been improved the techniques of diagnosing and evaluating the depression in the past decade.…”

Section: Introductionmentioning

confidence: 99%

A mental disorder early warning approach by observing depression symptom in social diary

Fang

Tai

Chang

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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With the advances of information technology, there are increasing researches aiming at assisting depression diagnosis and treatment. In most of them the user is necessarily actively joining the diagnosis and treatment program while he has perceived mental disorder himself. In order to early prevent the mental disorder, in this paper we propose an early warning mechanism that observes and mines user diary published on social network platform, and generates a score of getting mental disordered or depressed. If the score is large than a threshold, the system can notify the user and his friends on the social network to take care about the friend. We have conducted experiments to evaluate the proposed approach, and the results show that the proposed approach is effective.

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Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Cited by 47 publications

References 34 publications

Matrix-vector algorithms of local posteriori inference in algebraic bayesian networks on quanta propositions

Matrix-vector algorithms of local posteriori inference in algebraic bayesian networks on quanta propositions

Bayesian belief network for assessing impact of factors on army’s lean–agile replenishment system

A mental disorder early warning approach by observing depression symptom in social diary

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