The Research on Neural Network Diagnosis and Treatment System of Child Mental Health Disorders

2014 ◽  
Vol 707 ◽  
pp. 188-192
Author(s):  
Bing Mei Chen ◽  
Han Wen Zhou ◽  
Xiao Ping Fan ◽  
Xue Rong Li ◽  
Zhi Ming Zhou
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Child Mental Health ◽  
Epidemiological Data ◽  
Treatment Method ◽  
Mental Health Disorders ◽  
Treatment System ◽  
Diagnosis And Treatment ◽  
Icd 10 ◽  
Health Disorders

The research of diagnosis and treatment system of child mental health disorders is based on artificial neural network and expert system. It combines the diagnosis standard of ICD 10, DSM IV with 40 years clinical experiences and knowledge of senior child psychiatrists. It also combines computer science with child psychiatry, child psychology, psychological estimate, psychological therapy and so on. The learning samples come from the epidemiological data in more than a dozen nationwide hospitals. The correct rate of system diagnosis is 99%. The system can diagnose 61 kinds of child mental health disorders and give a treatment method suggestion.

Integrated Mental Health Training Relates to Pediatric Residents’ Confidence with Child Mental Health Disorders

2020 ◽  
Vol 44 (3) ◽  
pp. 299-304
Author(s):  
Skyler McLaurin-Jiang ◽  
Gail M. Cohen ◽  
Callie L. Brown ◽  
Palmer Edwards ◽  
Laurie W. Albertini
Keyword(s):  
Mental Health ◽  
Child Mental Health ◽  
Mental Health Disorders ◽  
Mental Health Training ◽  
Health Training ◽  
Pediatric Residents ◽  
Health Disorders ◽  
Integrated Mental Health

Prevention Approaches in Child Mental Health Disorders

2010 ◽  
Vol 45 (4) ◽  
pp. 521-539 ◽  
Author(s):  
Kathleen R. Delaney ◽  
Ruth “Topsy” Staten
Keyword(s):  
Mental Health ◽  
Child Mental Health ◽  
Mental Health Disorders ◽  
Health Disorders

A national child mental health literacy initiative is needed to reduce childhood mental health disorders

2019 ◽  
Vol 53 (4) ◽  
pp. 286-290 ◽  
Author(s):  
Lucy A Tully ◽  
David J Hawes ◽  
Frances L Doyle ◽  
Michael G Sawyer ◽  
Mark R Dadds
Keyword(s):  
Mental Health ◽  
Health Literacy ◽  
Child Mental Health ◽  
Mental Health Literacy ◽  
Mental Health Problems ◽  
Parental Knowledge ◽  
Mental Health Disorders ◽  
Childhood Disorders ◽  
Health Disorders ◽  
Low Levels

Half of all lifetime mental health disorders emerge in childhood, so intervening in the childhood years is critical to prevent chronic trajectories of mental health disorders. The prevalence of child mental health disorders is not decreasing despite the increased availability of evidence-based interventions. One key reason for the high prevalence and low treatment uptake may be low levels of child mental health literacy in the general community. Mental health literacy refers to knowledge and beliefs about mental health disorders that aid in their recognition, prevention and management. There is emerging evidence of poor recognition of child mental health problems in the community and low levels of parental knowledge about how to seek help, along with high levels of stigmatising attitudes. Although Australia has been a world leader in research and practice in improving mental health literacy for adolescent and adult mental health disorders, particularly depression and anxiety, mental health literacy for childhood disorders has been largely overlooked. In order to improve knowledge of child mental health disorders, reduce stigma, improve appropriate help-seeking and impact on the prevalence of child mental health disorders, we argue that a national initiative focussing on increasing mental health literacy for childhood disorders is urgently needed.

Behavioral epigenetics and the developmental origins of child mental health disorders

2012 ◽  
Vol 3 (6) ◽  
pp. 395-408 ◽  
Author(s):  
B. M. Lester ◽  
C. J. Marsit ◽  
E. Conradt ◽  
C. Bromer ◽  
J. F. Padbury
Keyword(s):  
Mental Health ◽  
Fetal Programming ◽  
Child Mental Health ◽  
Mental Health Disorders ◽  
Developmental Origins ◽  
Populations At Risk ◽  
Health And Disease ◽  
Health Disorders ◽  
Evo Devo

Advances in understanding the molecular basis of behavior through epigenetic mechanisms could help explain the developmental origins of child mental health disorders. However, the application of epigenetic principles to the study of human behavior is a relatively new endeavor. In this paper we discuss the ‘Developmental Origins of Health and Disease’ including the role of fetal programming. We then review epigenetic principles related to fetal programming and the recent application of epigenetics to behavior. We focus on the neuroendocrine system and develop a simple heuristic stress-related model to illustrate how epigenetic changes in placental genes could predispose the infant to neurobehavioral profiles that interact with postnatal environmental factors potentially leading to mental health disorders. We then discuss from an ‘Evo-Devo’ perspective how some of these behaviors could also be adaptive. We suggest how elucidation of these mechanisms can help to better define risk and protective factors and populations at risk.

scholarly journals Testing an individualized digital decision assist system for the diagnosis and management of mental and behavior disorders in children and adolescents

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Carolyn E. Clausen ◽  
Bennett L. Leventhal ◽  
Øystein Nytrø ◽  
Roman Koposov ◽  
Odd Sverre Westbye ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Adolescent Mental Health ◽  
Child And Adolescent ◽  
Mental Health Disorders ◽  
Diagnosis And Treatment ◽  
Health Disorders

Abstract Background Nearly half of all mental health disorders develop prior to the age of 15. Early assessments, diagnosis, and treatment are critical to shortening single episodes of care, reducing possible comorbidity and long-term disability. In Norway, approximately 20% of all children and adolescents are experiencing mental health problems. To address this, health officials in Norway have called for the integration of innovative approaches. A clinical decision support system (CDSS) is an innovative, computer-based program that provides health professionals with clinical decision support as they care for patients. CDSS use standardized clinical guidelines and big data to provide guidance and recommendations to clinicians in real-time. IDDEAS (Individualised Digital DEcision Assist System) is a CDSS for diagnosis and treatment of child and adolescent mental health disorders. The aim of IDDEAS is to enhance quality, competency, and efficiency in child and adolescent mental health services (CAMHS). Methods/design IDDEAS is a mixed-methods innovation and research project, which consists of four stages: 1) Assessment of Needs and Preparation of IDDEAS; 2) The Development of IDDEAS CDSS Model; 3) The Evaluation of the IDDEAS CDSS; and, 4) Implementation & Dissemination. Both qualitative and quantitative methods will be used for the evaluation of IDDEAS CDSS model. Child and adolescent psychologists and psychiatrists (n = 30) will evaluate the IDDEAS` usability, acceptability and relevance for diagnosis and treatment of attention-deficit/hyperactivity disorder. Discussion The IDDEAS CDSS model is the first guidelines and data-driven CDSS to improve efficiency of diagnosis and treatment of child and adolescent mental health disorders in Norway. Ultimately, IDDEAS will help to improve patient health outcomes and prevent long-term adverse outcomes by providing each patient with evidence-based, customized clinical care. Trial registration ISRCTN, ISRCTN12094788. Ongoing study, registered prospectively 8 April 2020 10.1186/ISRCTN12094788

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