Artificial Neural Network to Assist Psychiatric Diagnosis

Zou, Yajie; Shen, Yi; Shu, Lingjie; Wang, Y; Feng, Fuli; Xu, Kailai; Ou, Yu‐Yen; Song, Yujae; Zhong, Yongwang; Wang, M (Wang Meng); Liu, W

doi:10.1192/bjp.169.1.64

Cited by 31 publications

(18 citation statements)

References 3 publications

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“…Experimental results show that the proposed method achieves an accuracy of 98.75% for identifying the psychiatric problems. This work [24] uses two neural networks, i.e., Backpropagation (BP) and Kohonen networks. This artificial intelligent neural system is fit for psychiatric diagnosis and programmed (using sixty cases) to classify neurosis, schizophrenia and normal people.…”

Section: Neural Network Methodsmentioning

confidence: 99%

An Overview of Various Computing Methods in Psychiatry and Neuropsychiatry

Yadav¹

2017

IJRASET

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Section: Neural Network Methodsmentioning

confidence: 99%

An Overview of Various Computing Methods in Psychiatry and Neuropsychiatry

Yadav¹

2017

IJRASET

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“…An associative memory is a mechanism where the network returns a given stored pattern when it is shown another input pattern sufficiently similar to the stored one [11,7]. Although neural networks have been proposed to help diagnose neuroses and schizophrenia [16], we are not aware of any previous effort to model neuroses with a distributed processing, neural network approach.…”

Section: Functional Model For the Neurosesmentioning

confidence: 99%

A Description of Neuroses and Psychoanalytic Working-Through Using Adaptive Neural Networks

Wedemann¹,

Donangelo²,

Carvalho³

2016

Anais Do 6. Congresso Brasileiro De Redes Neurais

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“…An accelerating diffusion into diverse disciplines is indicated by the fact that articles on the use of ANNs are appearing in highly regarded nontechnical journals. For example, an introduction to ANNs (Baxt, 1995) and an ANN application that used a large database collected on intensive-care-unit patients (Dybowski et al, 1996) (Lucek & Ott, 1997), psychiatry (Zou et al, 1996), and statistics (Warner & Misra, 1996). In these articles, the results were often compared with results obtained through commonly used statistical methods (Dybowski et al, 1996;loannidis et al, 1998).…”

Section: Applications To Psychiatric and Psychological Researchmentioning

confidence: 99%

“…Measures from the Diagnostic Interview for Children and Adolescents (Herjanic & Reich, 1982) were input to an ANN to predict adolescent hopelessness (Kashani, Nair, Rao, Nair, & Reid, 1996). The Composite International Diagnostic Interview (Robins et al, 1988) (Zou et al, 1996).…”

Section: Applications To Psychiatric and Psychological Researchmentioning

confidence: 99%

Applying artificial neural network models to clinical decision making.

Price

Spitznagel²,

Downey³

et al. 2000

Psychological Assessment

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Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed.In the area of psychological assessment, a computer-based algorithm approach has relied heavily on the expert knowledge of clinicians. In a more empirically based approach-used when insufficient knowledge is available, existing knowledge is debated, or a special population is investigated-linear models such as factor analysis or regression models are frequently applied to empirical data to derive meaningful constructs and appropriate measures. In this approach, statistical inferences are needed to assess the generality of constructs and rules because the gold standard of expert knowledge may not be applied. mortality. The details of ANN methodology in these contexts include ANN parameter specification, software, cross-validation, and the receiver operating characteristic (ROC) evaluation. The performance of the ANN is compared with the performance of the linear statistical analyses frequently used in the area of psychological assessment: linear and quadratic discriminant analyses and logistic regression analyses. We examine the performance of ANN models with varying numbers of hidden neurons to show when complex network architecture is needed to achieve a better level of prediction than that achieved in linear models. We then discuss issues for research practitioners to consider in order to make optimal use of ANNs. Future directions of ANN applications and current salient issues in psychological assessment are discussed. Artificial Neural Networks for Psychological AssessmentBackground

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Artificial Neural Network to Assist Psychiatric Diagnosis

Abstract: ANN might be used to improve psychiatric diagnosis.

Cited by 31 publications

References 3 publications

An Overview of Various Computing Methods in Psychiatry and Neuropsychiatry

An Overview of Various Computing Methods in Psychiatry and Neuropsychiatry

A Description of Neuroses and Psychoanalytic Working-Through Using Adaptive Neural Networks

Applying artificial neural network models to clinical decision making.

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