Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

Diagnostics, Год журнала: 2023, Номер 13(3), С. 573 - 573

Опубликована: Фев. 3, 2023

This paper discusses the promising areas of research into machine learning applications for prevention and correction neurodegenerative depressive disorders. These two groups disorders are among leading causes decline in quality life world when estimated using disability-adjusted years. Despite decades research, development new approaches assessment (especially pre-clinical) diseases remains priority neurophysiology, psychology, genetics, interdisciplinary medicine. Contemporary technologies medical data infrastructure create opportunities. However, reaching a consensus on application methods their integration with existing standards care is still challenge to overcome before innovations could be widely introduced clinics. The clinical predictions classification algorithms contributes towards creating unified approach use growing data. should integrate requirements professionals, researchers, governmental regulators. In current paper, state presented.

Язык: Английский

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Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study

JMIR Mental Health, Год журнала: 2023, Номер 11, С. e54369 - e54369

Опубликована: Дек. 25, 2023

Mentalization, which is integral to human cognitive processes, pertains the interpretation of one's own and others' mental states, including emotions, beliefs, intentions. With advent artificial intelligence (AI) prominence large language models in health applications, questions persist about their aptitude emotional comprehension. The prior iteration model from OpenAI, ChatGPT-3.5, demonstrated an advanced capacity interpret emotions textual data, surpassing benchmarks. Given introduction ChatGPT-4, with its enhanced visual processing capabilities, considering Google Bard's existing functionalities, a rigorous assessment proficiency mentalizing warranted.

Язык: Английский

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The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Март 19, 2024

This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing development artificial intelligence (AI)-powered tools for depression and anxiety detection from level intricate algorithms to practical applications. Delivering essential mental health care services is now significant public priority. In recent years, AI has become game-changer in early identification intervention these pervasive disorders. can potentially empower behavioral healthcare by helping psychiatrists collect objective data on patients' progress tasks. study emphasizes current understanding AI, different types its use multiple disorders, advantages, disadvantages, future potentials. As technology develops digitalization modern era increases, there will be rise application psychiatry; therefore, needed. We searched PubMed, Google Scholar, Science Direct using keywords this. studies electronic records (EHR) with machine learning techniques diagnosing all clinical conditions, roughly 99 publications have been found. Out these, 35 were identified disorders age groups, among them, six utilized EHR sources. By critically analyzing prominent scholarly works, we aim illuminate state this technology, exploring successes, limitations, directions. doing so, hope contribute nuanced AI's potential revolutionize diagnostics pave way further research important domain.

Язык: Английский

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Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Март 12, 2024

Abstract Artificial intelligence provides an opportunity to try redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals focal epilepsy originating the temporal lobe (TLE) 91 healthy controls, we show phenotypic heterogeneity in pathophysiological progression of TLE. This study was registered Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning left or right hippocampus; third cortex-predominant phenotype, hippocampus after neocortex; fourth phenotype without but amygdala enlargement. These four are replicated independent validation cohort (109 individuals). differences neuroanatomical signature, characteristics. Five-year follow-up observations these reveal differential seizure outcomes among subtypes, indicating that specific may benefit surgery pharmacological treatment. findings suggest diverse pathobiological basis underlying potentially yields stratification prognostication – necessary step for precise medicine.

Язык: Английский

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Old dog, new tricks? Exploring the potential functionalities of ChatGPT in supporting educational methods in social psychiatry

International Journal of Social Psychiatry, Год журнала: 2023, Номер 69(8), С. 1882 - 1889

Опубликована: Июнь 30, 2023

Artificial Intelligence is ever-expanding and large-language models are increasingly shaping teaching learning experiences. ChatGPT a prominent recent example of this technology has generated much debate around the benefits disadvantages chatbots in educational domains.This study seeks to demonstrate possible use-cases supporting methods specific social psychiatry.Through interactions with 3.5, we asked list six ways which it could aid psychiatry teaching. Subsequently, requested that perform one tasks identified its responses.ChatGPT highlighted several roles fulfil settings, including as an information provider, tool for debates discussions, facilitator self-directed content-creator course materials. For latter scenario, based on another prompt, hypothetical case vignette topic relevant psychiatry.Based our experiences, can be effective tool, offering opportunities active case-based students instructors psychiatry. However, their current form, have limitations must considered, misinformation inherent biases, although these may only temporary nature technologies continue advance. Accordingly, argue support education appropriate caution encourage educators become attuned potential through further detailed research area.

Язык: Английский

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Innovative deep learning techniques for monitoring aggressive behavior in social media posts

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Янв. 16, 2024

Abstract The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in context detecting cyber-trolling behaviors. With rising prevalence online harassment, developing effective automated tools for aggression detection digital communications has become imperative. This research assesses efficacy Random Forest, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Support Vector (SVM), Naive Bayes identifying cyber troll posts within a publicly available dataset. Each ML classifier was trained tested on dataset curated trolls. gauged using confusion matrices, which provide detailed counts true positives, negatives, false negatives. These metrics were then utilized calculate accuracy, precision, recall, F1 scores better understand each model’s predictive capabilities. Forest outperformed other models, exhibiting highest accuracy balanced precision-recall trade-off, as indicated by positive negative rates, alongside lowest rates. LightGBM, while effective, showed tendency towards higher predictions. SVM, displayed identical matrix results, an anomaly suggesting potential data handling or model application issues that warrant further investigation. findings underscore effectiveness ensemble methods, with leading task. highlights importance selecting appropriate algorithms text classification tasks social media contexts emphasizes need scrutiny into observed among results. Future work will focus exploring reasons behind this occurrence deep techniques enhancing performance.

Язык: Английский

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An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders

Frontiers in Psychiatry, Год журнала: 2024, Номер 15

Опубликована: Авг. 6, 2024

Eating Disorders (EDs) affect individuals globally and are associated with significant physical mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, resource constraints.

Язык: Английский

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From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Biomedicines, Год журнала: 2025, Номер 13(1), С. 167 - 167

Опубликована: Янв. 12, 2025

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence mental health disorders like depression schizophrenia, which necessitate precise, innovative approaches. Emerging technologies artificial intelligence, induced pluripotent stem cells, multi-omics potential transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies animal models single-variable analyses continue be used, frequently failing capture complexities human conditions. Summary: This review critically evaluates transition serendipity precision-based in research. It focuses key innovations dynamic systems modeling network-based approaches that use genetic, molecular, environmental data identify new therapeutic targets. Furthermore, it emphasizes importance interdisciplinary collaboration human-specific overcoming limitations Conclusions: We highlight precision psychiatry’s transformative revolutionizing care. paradigm shift, combines cutting-edge systematic frameworks, promises increased diagnostic accuracy, reproducibility, efficiency, paving way tailored better patient outcomes

Язык: Английский

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Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms

npj Digital Medicine, Год журнала: 2025, Номер 8(1)

Опубликована: Янв. 28, 2025

Язык: Английский

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Framework for detecting, assessing and mitigating mental health issue in the context of online social networks: a viewpoint paper

International Journal of Health Governance, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

Purpose The development and presentation of a framework that integrates modern methods for detecting, assessing mitigating mental health issues in the context dynamic adverse changes social networks. Design/methodology/approach This viewpoint is based on literature review current advancements field. use causal discovery inference forms foundation applying all techniques included (machine learning, deep explainable AI as well large language models generative AI). Additionally, an analysis network effects their influence users’ emotional states conducted. Findings synergy used framework, combined with analysis, opens new horizons predicting diagnosing disorders. proposed demonstrates its applicability providing additional analytics studied subjects (individual traits factors worsen health). It also proves ability to identify hidden processes. Originality/value offers novel perspective addressing rapidly evolving digital platforms. Its flexibility allows adaptation tools various scenarios user groups. application can contribute more accurate algorithms account impact negative (including hidden) external affecting users. Furthermore, it assist diagnostic process.

Язык: Английский

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