Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
Classification of mental disordersThe classification of mental disorders is also known as psychiatric nosology or psychiatric taxonomy. It represents a key aspect of psychiatry and other mental health professions and is an important issue for people who may be diagnosed. There are currently two widely established systems for classifying mental disorders: Chapter V of the tenth International Classification of Diseases (ICD-10) produced by the World Health Organization (WHO); The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) produced by the American Psychiatric Association (APA).
Mental disorderA mental disorder, also referred to as a mental illness or psychiatric disorder, is a behavioral or mental pattern that causes significant distress or impairment of personal functioning. A mental disorder is also characterized by a clinically significant disturbance in an individual's cognition, emotional regulation, or behavior. It is usually associated with distress or impairment in important areas of functioning. There are many different types of mental disorders. Mental disorders may also be referred to as mental health conditions.
LearningLearning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.
Bipolar disorderBipolar disorder, previously known as manic depression, is a mental disorder characterized by periods of depression and periods of abnormally elevated mood that each last from days to weeks. If the elevated mood is severe or associated with psychosis, it is called mania; if it is less severe, it is called hypomania. During mania, an individual behaves or feels abnormally energetic, happy or irritable, and they often make impulsive decisions with little regard for the consequences.
Diagnostic and Statistical Manual of Mental DisordersThe Diagnostic and Statistical Manual of Mental Disorders (DSM; latest edition: DSM-5-TR, published in March 2022) is a publication by the American Psychiatric Association (APA) for the classification of mental disorders using a common language and standard criteria. It is the main book for the diagnosis and treatment of mental disorders in the United States and is considered one of the principal guides of psychiatry, along with the ICD, CCMD, and the Psychodynamic Diagnostic Manual.
SchizophreniaSchizophrenia is a mental disorder characterized by continuous or relapsing episodes of psychosis. Major symptoms include hallucinations (typically hearing voices), delusions, and disorganized thinking. Other symptoms include social withdrawal, and flat affect. Symptoms typically develop gradually, begin during young adulthood, and in many cases never become resolved. There is no objective diagnostic test; diagnosis is based on observed behavior, a psychiatric history that includes the person's reported experiences, and reports of others familiar with the person.
Automated machine learningAutomated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning.
PsychiatryPsychiatry is the medical specialty devoted to the diagnosis, prevention, and treatment of deleterious mental conditions. These include various matters related to mood, behaviour, cognition, and perceptions. Initial psychiatric assessment of a person begins with a case history and mental status examination. Physical examinations, psychological tests, and laboratory tests may be conducted. On occasion, neuroimaging or other neurophysiological studies are performed.
Personality disorderPersonality disorders (PD) are a class of mental disorders characterized by enduring maladaptive patterns of behavior, cognition, and inner experience, exhibited across many contexts and deviating from those accepted by the individual's culture. These patterns develop early, are inflexible, and are associated with significant distress or disability. The definitions vary by source and remain a matter of controversy.
Transfer learningTransfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for , knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing/transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency.
Thought disorderA thought disorder (TD) is a disturbance in cognition which affects language, thought and communication. Psychiatric and psychological glossaries in 2015 and 2017 identified thought disorders as encompassing poverty of ideas, neologisms, paralogia, word salad, and delusions - all disturbances of thought content and form. Two specific terms have been suggested — content thought disorder (CTD) and formal thought disorder (FTD).
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Outline of bipolar disorderThe following outline is provided as an overview of and topical guide to bipolar disorder: Bipolar disorder – mental disorder with cyclical periods of depression and periods of elevated mood. The elevated mood is significant and is known as mania, a severe elevation that can be accompanied by psychosis in some cases, or hypomania, a milder form of mania. During mania, an individual behaves or feels abnormally energetic, elated, or irritable. Individuals often make poorly thought out decisions with little regard to the consequences.
Schizoaffective disorderSchizoaffective disorder (SZA, SZD) is a mental disorder characterized by abnormal thought processes and an unstable mood. This diagnosis requires symptoms of both schizophrenia (usually psychosis) and a mood disorder: either bipolar disorder or depression. The main criterion is the presence of psychotic symptoms for at least two weeks without any mood symptoms. Schizoaffective disorder can often be misdiagnosed when the correct diagnosis may be psychotic depression, bipolar I disorder, schizophreniform disorder, or schizophrenia.
Spectrum disorderA spectrum disorder is a mental disorder that includes a range of linked conditions, sometimes also extending to include singular symptoms and traits. The different elements of a spectrum either have a similar appearance or are thought to be caused by the same underlying mechanism. In either case, a spectrum approach is taken because there appears to be "not a unitary disorder but rather a syndrome composed of subgroups". The spectrum may represent a range of severity, comprising relatively "severe" mental disorders through to relatively "mild and nonclinical deficits".
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Bipolar II disorderBipolar II disorder (BP-II) is a mood disorder on the bipolar spectrum, characterized by at least one episode of hypomania and at least one episode of major depression. Diagnosis for BP-II requires that the individual must never have experienced a full manic episode. Otherwise, one manic episode meets the criteria for bipolar I disorder (BP-I). Hypomania is a sustained state of elevated or irritable mood that is less severe than mania yet may still significantly affect the quality of life and result in permanent consequences including reckless spending, damaged relationships and poor judgment.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
