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Although effective treatments exist, obsessive-compulsive disorder (OCD) is, according to the views of patients and experts, still associated with chronicity, a term with no clear and consistent definition. To improve patient care and to foster research, a clear distinction between the various concepts of chronicity cited in the literature is crucial. The aim was thus to explicate central concepts related to courses and trajectories in OCD based on an explorative, scoping search of the existing literature. Our review revealed a considerable lack in content validity, as the concepts were operationalized inconsistently. Concepts related to symptom improvement were (complete) recovery, partial/full remission and partial/full response. Terms used in relation with symptom stability or worsening were chronic/continuous, intermittent and episodic course, waxing and waning, relapse, recurrence, deterioration and treatment-refractoriness. All concepts are explained and visualized as a result of the review. Further, based on authors' remarks, we present recommendations on how to enhance care for chronic OCD patients, namely training psychotherapists to apply CBT as intended, managing patient beliefs about disease and treatment, and adapting psychotherapy to OCD subtypes. Finally, we then propose a literature-based definition of treatment-refractory OCD.
Background:
Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models.
Methods:
For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases.
Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning
can then be deployed to classify specific behaviour and mental health patterns.
Results:
We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through
rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project.
Conclusions:
This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns
of unknown effects.