Peer recognition of prodromal signs of psychosis : a signal detection analysis : a thesis presented in partial fulfilment of the requirements for the degree in Master of Arts in Psychology at Massey University, Palmerston North, New Zealand

Using signal detection analysis, this study investigated young peoples' sensitivity to prodromal signs or psychotic symptoms compared to more everyday signs of distress in their friends. In a questionnaire format, 117 high school students (aged 13 to 16 years) were asked to report the level of concern they would have if one of their friends exhibited certain characteristics. Half of the latter were neutral, everyday phenomena (no signal), and the remainder were either DSM-IV symptoms of psychosis or empirically-derived prodromal signs of early onset psychosis (signal). Each possible sign was modified (made more serious) by descriptors used in psychological models to define pathology behaviorally: rare in youth, high in frequency, recent change, and lack of obvious (rational) environmental cause. High frequency was the modifier leading to the greatest degree of concern. Accurate and sensitive detection, based on d' values, was adequate for psychotic symptoms, especially by females rather than by males, although depressed mood (a prodromal sign in this context) was most readily detected as a worrisome feature. The study has implications for analyzing how youth judge indices of distress in their friends and for their general ability to recognize that certain characteristics are more troublesome than others. Telling a responsible adult of their concerns was the most frequently suggested response, followed by attempting to help and talking to the peer about their concerns. If rapid detection of early onset psychosis is to be a goal of preventative mental health services, youth who are sensitive to classic symptoms of psychosis may still need educating in recognizing the difference between behavioral characteristics that are part of everyday distress and those that are indicative of more serious adjustment difficulties that might be emerging

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Design And Construction Of An Expert System For Early Detection of Mental Illness in COVID-19 Patients And Handling Using Certainty Factor Method

Covid-19 is a new type of virus that can be transmitted to humans. This virus spreads very quickly and has spread to almost all countries. In the midst of Covid-19 outbreak, social phenomenon emerged that has the potential to exacerbate the situation, it is negative social stigma against a person or group of people who experience symptoms or have COVID-19 disease. They are labeled, stereotyped, discriminated against, and treated differently because they are associated with Covid-19 disease. This pandemic puts pressure on the emergence of mental illnesses such as fatigue, stress, fear, sadness, loneliness, schizophrenia, anxiety, depression, or post-traumatic stress disorder (PTSD) and its probability of dying from Covid 19 is almost 3 times higher than those who do not have mental illness. Therefore, an expert system is needed to detect mental disorders in Covid-19 patients early as prevention. This system works as psychologist experts detect Covid-19 patients through the people closest to the patient. By observing the conditions and symptoms arose in the patient's psychological condition, the user will fill the data in the system to find out the level of disturbance experienced by the patient in real time. Then, the system will provide solutions, diagnosis results and appropriate treatment methods for patients so that symptoms of mental disorders can be detected and prevented early on without direct contact between patients and expert

Synthetic cannabinoid use in a case series of patients with psychosis presenting to acute psychiatric settings : Clinical presentation and management issues

Background: Novel Psychoactive Substances (NPS) are a heterogeneous class of synthetic molecules including synthetic cannabinoid receptor agonists (SCRAs). Psychosis is associated with SCRAs use. There is limited knowledge regarding the structured assessment and psychometric evaluation of clinical presentations, analytical toxicology and clinical management plans of patients presenting with psychosis and SCRAs misuse. Methods: We gathered information regarding the clinical presentations, toxicology and care plans of patients with psychosis and SCRAs misuse admitted to inpatients services. Clinical presentations were assessed using the PANSS scale. Vital signs data were collected using the National Early Warning Signs tool. Analytic chemistry data were collected using urine drug screening tests for traditional psychoactive substances and NPS. Results: We described the clinical presentation and management plan of four patients with psychosis and misuse of SCRAs. Conclusion: The formulation of an informed clinical management plan requires a structured assessment, identification of the index NPS, pharmacological interventions, increases in nursing observations, changes to leave status and monitoring of the vital signs. The objective from using these interventions is to maintain stable physical health whilst rapidly improving the altered mental state.Peer reviewedFinal Published versio

Seeing Voices: Potential Neuroscience Contributions to a Reconstruction of Legal Insanity

Part I of this Article explains the insanity defense in the United States. Next, Part II discusses some of the brain-based research about mental illness, focusing on schizophrenia research. Then, Part III looks at traumatic brain injury and the relationship among injury, cognition, and behavior. Finally, Part IV explains how a new neuroscience-informed standard might better inform our moral decision making about legal insanity

Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject

Decision support system for the diagnosis of schizophrenia disorders

Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de PsiquiatriaUniversidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Informática MédicaUNIFESP, EPM, Depto. de PsiquiatriaUNIFESP, EPM, Depto. de Informática MédicaSciEL

Recommendations to the Social Security Administration on the Design of the Mental Health Treatment Study

Many beneficiaries with mental illness who have a strong desire to work nevertheless continue to seek the protection and security of disability benefits, not only because of the income such benefits provide but also for the health care coverage that comes with it. Further complicating matters is that few jobs available to people with mental illnesses have mental health care coverage, forcing individuals to choose between employment and access to care. These barriers, coupled with the limited treatment options and negative employer attitudes and even discrimination when it comes to employing people with serious metal illness, help "explain" the very rates of low labor force participation among people with psychiatric disabilities