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Current Neuropharmacology Apr 2017Athanasios Koukopoulos provided a radical model for understanding depressive and manic conditions. (Review)
Review
BACKGROUND
Athanasios Koukopoulos provided a radical model for understanding depressive and manic conditions.
OBJECTIVE
To review, explain, and analyze Koukopoulos' concept of the primacy of mania, with special attention to the role of antidepressants.
METHOD
A conceptual review of Koukopoulos' writings and lectures on this topic is given.
RESULTS
Koukopoulos held that depressive states are caused by manic states; the former do not occur without the latter. The most common scenario of the inseparability of depressive and manic symptoms occurs in mixed states, which we estimate to represent about one-half of all depressive episodes in all patients (not just bipolar illness). In a review of the empirical evidence for this topic, we conclude that empirical evidence exists to support the primary of mania thesis in almost 80% of depressed patients. Since antidepressants worsen mania, they would be expected to worsen depression as well in this model. We provide evidence that supports this view in most persons with depressive states.
CONCLUSION
Koukopoulos' model of affective illness is one where manic states are the primary pathology, and depressive conditions are a secondary consequence. Hence treatment of depression with antidepressants would be less effective than treatment with mood stabilizers, since treating an effect is less successful than treating its cause. This approach would reverse current assumptions in psychiatry.
Topics: Antidepressive Agents; Bipolar Disorder; Diagnosis, Differential; History, 20th Century; History, 21st Century; Humans; Psychiatric Status Rating Scales; Psychiatry
PubMed: 28503112
DOI: 10.2174/1570159X14666160621113432 -
BMC Psychiatry Mar 2022Many critical illness survivors experience new or worsening mental health impairments. Psychiatry consultation services can provide a critical role in identifying,...
BACKGROUND
Many critical illness survivors experience new or worsening mental health impairments. Psychiatry consultation services can provide a critical role in identifying, addressing, and preventing mental health challenges during and after admission to the acute medical care setting. However, psychiatry involvement in the ICU setting is lower than in other hospital settings and the conventional process in many hospitals requires other care providers to request consultation by psychiatry. Despite these differences, no studies have sought ICU provider perspectives on psychiatry consultation's current and desired role. We aimed to obtain stakeholder feedback on psychiatry's current and desired roles in the ICU, and potential benefits and drawbacks of increasing psychiatry's presence.
METHODS
A web-based survey obtained perspectives from 373 critical care physicians and advance practice providers, bedside nurses, physical and occupational therapists, pharmacists, and consultation-liaison psychiatry physicians and advance practice providers at a tertiary care center using multiple choice and open-ended questions. Descriptive information and content analysis of qualitative data provided information on stakeholder perspectives.
RESULTS
Psychiatry's primary current role was seen as assistance with management of mental health issues (38%) and suicide risk assessments (23%). 46% wished for psychiatry's increased involvement in the ICU. Perceived benefits of increased psychiatry presence in the ICU included early psychological support in parallel with medical care, identification of psychiatric factors impacting treatment, and facilitation of family understanding of the patient's mental state/delirium. An additional perceived benefit included reduction in provider burnout through processing difficult situations and decreasing family psychological distress. However, one concern included potential conflict among providers regarding treatment.
CONCLUSIONS
Those who work closely with the critically ill patients think that increased psychological support in the ICU would be beneficial. By contrast, psychiatry's current involvement is seen to be limited, perhaps driven by varying perceptions of what psychiatry's role is or should be.
Topics: Critical Care; Critical Illness; Humans; Mental Disorders; Mental Health; Psychiatry; Referral and Consultation
PubMed: 35303814
DOI: 10.1186/s12888-022-03855-w -
Psychiatry and Clinical Neurosciences Jul 2016
Topics: Humans; Neuroimaging; Psychiatry
PubMed: 27374184
DOI: 10.1111/pcn.12406 -
Tijdschrift Voor Psychiatrie 2023Artificial intelligence (AI) can be a valuable addition to psychiatry by helping to make diagnoses, personalize treatments, and support patients during their recovery....
BACKGROUND
Artificial intelligence (AI) can be a valuable addition to psychiatry by helping to make diagnoses, personalize treatments, and support patients during their recovery. However, it is important to consider the risks and ethical implications of using this technology.
AIM
In this article, we explore how AI can change the future of psychiatry from a co-creation perspective, meaning that people and machines work together to provide the best possible care. We provide both critical and optimistic perspectives on how AI can influence psychiatry.
METHOD
A co-creation methodology was used to produce this essay, involving interaction between my prompt and the text generated in response by the AI-based chatbot ChatGPT.
RESULTS
We describe how AI can be used to make diagnoses, personalize treatments, and support patients during their recovery. We also discuss the risks and ethical implications of using AI in psychiatry.
CONCLUSION
If we critically examine the risks and ethical implications of using AI in psychiatry and promote co-creation between people and machines, AI can contribute to improved care for patients in the future.
Topics: Humans; Artificial Intelligence; Psychiatry
PubMed: 37323042
DOI: No ID Found -
Journal of Psychosomatic Research Apr 2007The European Association of Consultation-Liaison Psychiatry and Psychosomatics (EACLPP) has organized a workgroup to establish consensus on the contents and organization...
European guidelines for training in consultation-liaison psychiatry and psychosomatics: report of the EACLPP Workgroup on Training in Consultation-Liaison Psychiatry and Psychosomatics.
OBJECTIVE
The European Association of Consultation-Liaison Psychiatry and Psychosomatics (EACLPP) has organized a workgroup to establish consensus on the contents and organization of training in consultation-liaison (C-L) for psychiatric and psychosomatic residents.
METHODS
Initially, a survey among experts has been conducted to assess the status quo of training in C-L in different European countries. In several consensus meetings, the workgroup discussed aims, core contents, and organizational issues of standards of training in C-L. Twenty C-L specialists in 14 European countries participated in a Delphi procedure answering a detailed consensus checklist, which included different topics under discussion.
RESULTS
Consensus on the following issues has been obtained: (1) all residents in psychiatry or psychosomatics should be exposed to C-L work as part of their clinical experience; (2) a minimum of 6 months of full-time (or equivalent part-time) rotation to a C-L department should take place on the second part of residency; (3) advanced training should last for at least 12 months; (4) supervision of trainees should be clearly defined and organized; and (5) trainees should acquire knowledge and skills on the following: (a) assessment and management of psychiatric and psychosomatic disorders or situations (e.g., suicide/self-harm, somatization, chronic pain and psychiatric disorders, and abnormal illness behavior in somatically ill patients); (b) crisis intervention and psychotherapy methods appropriate for medically ill patients; (c) psychopharmacology in physically ill patients; (d) communication with severely ill patients and dying patients, as well as with medical staff; (e) promotion of coordination of care for complex patients across several disciplines; and (f) organization of C-L service in relation to general hospital and/or primary care. In addition, the workgroup elaborated recommendations on the form of training and on assessment of competency.
CONCLUSION
This document is a first step towards establishing recognized training in C-L psychiatry and psychosomatics across the European Union.
Topics: Curriculum; Europe; Humans; Internship and Residency; Psychiatry; Psychosomatic Medicine; Referral and Consultation
PubMed: 17383503
DOI: 10.1016/j.jpsychores.2006.11.003 -
Journal of Neurology, Neurosurgery, and... Jan 2016Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it... (Review)
Review
Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.
Topics: Computational Biology; Humans; Mathematics; Mental Disorders; Psychiatry
PubMed: 26157034
DOI: 10.1136/jnnp-2015-310737 -
European Child & Adolescent Psychiatry Jun 2019
Topics: Adolescent; Adolescent Psychiatry; Child; Child Psychiatry; Cooperative Behavior; Europe; Family; Humans; Mental Disorders; Patient Care Team; Research
PubMed: 31129733
DOI: 10.1007/s00787-019-01354-0 -
East Asian Archives of Psychiatry :... Dec 2015
Topics: Human Rights; Humans; Patient Rights; Physician-Patient Relations; Psychiatry; Social Control, Formal
PubMed: 26764287
DOI: No ID Found -
Dialogues in Clinical Neuroscience Mar 2015The Research Domain Criteria (RDoC) project was initiated by the National Institute of Mental Health (NIMH) in early 2009 as the implementation of Goal 1.4 of its... (Review)
Review
The Research Domain Criteria (RDoC) project was initiated by the National Institute of Mental Health (NIMH) in early 2009 as the implementation of Goal 1.4 of its just-issued strategic plan. In keeping with the NIMH mission, to "transform the understanding and treatment of mental illnesses through basic and clinical research," RDoC was explicitly conceived as a research-related initiative. The statement of the relevant goal in the strategic plan reads: "Develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures." Due to the novel approach that RDoC takes to conceptualizing and studying mental disorders, it has received widespread attention, well beyond the borders of the immediate research community. This review discusses the rationale for the experimental framework that RDoC has adopted, and its implications for the nosology of mental disorders in the future.
Topics: Biomedical Research; Humans; Mental Disorders; Psychiatry
PubMed: 25987867
DOI: 10.31887/DCNS.2015.17.1/bcuthbert -
PloS One 2020The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The... (Review)
Review
BACKGROUND
The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective.
DESIGN/METHODS
A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within 'GloVe' and 'Word2Vec' word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health.
RESULTS
Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication.
CONCLUSION
Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.
Topics: Bias; Data Science; Health Status Disparities; Humans; Intersectoral Collaboration; Linguistics; Mental Health; Natural Language Processing; Psychiatry
PubMed: 33332380
DOI: 10.1371/journal.pone.0240376