#computational-psychiatry News & Analysis

3 articles tagged with #computational-psychiatry. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · Mar 67/10

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BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry

Researchers introduce BioLLMAgent, a hybrid framework combining reinforcement learning models with large language models to simulate human decision-making in computational psychiatry. The framework demonstrates strong interpretability while accurately reproducing human behavioral patterns and successfully simulating cognitive behavioral therapy principles.

AINeutralarXiv – CS AI · May 286/10

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You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention

Researchers propose that human behavioral variability stems from dynamic latent states—weighted neural-psychological conditions that determine how individuals process decisions moment-to-moment. Drawing on 24 months of data from 200,000+ users, the framework suggests human outcomes are causally controllable through state-targeted interventions, with implications for AI personalization, digital health, and behavioral prediction systems.

AINeutralarXiv – CS AI · May 16/10

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AI Models for Depressive Disorder Detection and Diagnosis: A Review

A comprehensive review of 55 studies examines AI methods for detecting and diagnosing Major Depressive Disorder, revealing trends toward graph neural networks for brain connectivity analysis, large language models for linguistic data, and multimodal fusion approaches. The survey highlights how AI can address the subjectivity in clinical depression diagnosis while advancing computational psychiatry through improved explainability and fairness.