Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification DOI Creative Commons
Miguel Suárez, Ana M. Torres,

P Blasco-Segura

et al.

Life, Journal Year: 2025, Volume and Issue: 15(3), P. 394 - 394

Published: March 3, 2025

Bipolar disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression, posing significant challenges for accurate timely diagnosis. This study explores the use Random Forest (RF) algorithm as machine learning approach to classify patients with BD healthy controls based on electroencephalogram (EEG) data. A total 330 participants, including euthymic controls, were analyzed. EEG recordings processed extract key features, power in frequency bands complexity metrics such Hurst Exponent, which measures persistence or randomness time series, Higuchi’s Fractal Dimension, used quantify irregularity brain signals. The RF model demonstrated robust performance, achieving an average accuracy 93.41%, recall specificity exceeding 93%. These results highlight algorithm’s capacity handle complex, noisy datasets while identifying features relevant classification. Importantly, provided interpretable insights into physiological markers associated BD, reinforcing clinical value diagnostic tool. findings suggest that reliable accessible method supporting diagnosis complementing traditional practices. Its ability reduce delays, improve classification accuracy, optimize resource allocation make it promising tool integrating artificial intelligence care. represents step toward precision psychiatry, leveraging technology understanding management mental health disorders.

Language: Английский

Brain Markers of Resilience to Psychosis in High-Risk Individuals: A Systematic Review and Label-Based Meta-Analysis of Multimodal MRI Studies DOI Creative Commons
Guusje Collin, Joshua E. Goldenberg, Xiao Chang

et al.

Brain Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 314 - 314

Published: March 17, 2025

Background/Objectives: Most individuals who have a familial or clinical risk of developing psychosis remain free from psychopathology. Identifying neural markers resilience in these at-risk may help clarify underlying mechanisms and yield novel targets for early intervention. However, contrast to studies on biomarkers, are scarce. The current study aimed identify potential brain psychosis. Methods: A systematic review the literature yielded total 43 MRI that reported resilience-associated changes with an elevated Label-based meta-analysis was used synthesize findings across modalities. Results: Resilience-associated were significantly overreported default mode language network, among highly connected central regions. Conclusions: These suggest DMN language-associated areas hubs be hotspots changes. systems thus key interest as inquiry and, possibly, intervention populations.

Language: Английский

Citations

0
Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures DOI Open Access
Brian A. Zaboski, Lora Bednarek

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2442 - 2442

Published: April 3, 2025

Obsessive-compulsive disorder (OCD) is a complex psychiatric condition characterized by significant heterogeneity in symptomatology and treatment response. Advances neuroimaging, EEG, other multimodal datasets have created opportunities to identify biomarkers predict outcomes, yet traditional statistical methods often fall short analyzing such high-dimensional data. Deep learning (DL) offers powerful tools for addressing these challenges leveraging architectures capable of classification, prediction, data generation. This brief review provides an overview five key DL architectures-feedforward neural networks, convolutional recurrent generative adversarial transformers-and their applications OCD research clinical practice. We highlight how models been used the predictors response, diagnose classify OCD, advance precision psychiatry. conclude discussing implementation DL, summarizing its advances promises underscoring field.

Language: Английский

Citations

0
The Role of Artificial Intelligence in Managing Bipolar Disorder: A New Frontier in Patient Care DOI Open Access
Jelena Milić,

Iva Zrnic,

Edita Grego

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2515 - 2515

Published: April 7, 2025

Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients healthcare providers. Traditional treatment methods, including medication therapy, remain vital, but there increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has potential improve mood episode prediction, personalize plans, provide real-time support, offering new opportunities managing more effectively. Our primary objective was explore role transforming management BD, specifically tracking, personalized regimens. Methods: To management, we conducted review recent literature using key search terms. We included studies discussed applications personalization. The were selected based on their relevance AI's with attention PICO criteria: Population-individuals diagnosed BD; Intervention-AI tools personalization, support; Comparison-traditional methods (when available); Outcome-measures effectiveness, improvements patient care. Results: findings from research reveal promising developments use Studies suggest AI-powered can enable proactive care, improving outcomes reducing burden professionals. ability analyze data wearable devices, smartphones, even social media platforms provides valuable insights early detection dynamic adjustments. Conclusions: While still its stages, it presents transformative However, further development are crucial fully realize supporting optimizing efficacy.

Language: Английский

Citations

0
Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification DOI Creative Commons
Miguel Suárez, Ana M. Torres,

P Blasco-Segura

et al.

Life, Journal Year: 2025, Volume and Issue: 15(3), P. 394 - 394

Published: March 3, 2025

Bipolar disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression, posing significant challenges for accurate timely diagnosis. This study explores the use Random Forest (RF) algorithm as machine learning approach to classify patients with BD healthy controls based on electroencephalogram (EEG) data. A total 330 participants, including euthymic controls, were analyzed. EEG recordings processed extract key features, power in frequency bands complexity metrics such Hurst Exponent, which measures persistence or randomness time series, Higuchi’s Fractal Dimension, used quantify irregularity brain signals. The RF model demonstrated robust performance, achieving an average accuracy 93.41%, recall specificity exceeding 93%. These results highlight algorithm’s capacity handle complex, noisy datasets while identifying features relevant classification. Importantly, provided interpretable insights into physiological markers associated BD, reinforcing clinical value diagnostic tool. findings suggest that reliable accessible method supporting diagnosis complementing traditional practices. Its ability reduce delays, improve classification accuracy, optimize resource allocation make it promising tool integrating artificial intelligence care. represents step toward precision psychiatry, leveraging technology understanding management mental health disorders.

Language: Английский

Citations

0