Revolutionizing Mental Wellness With a Neurosymbolic Artificial Intelligence (Preprint)
Bibliographic record
Abstract
BACKGROUND The integration of artificial intelligence (AI) in mental health care has traditionally been hampered by the reliance on static models that necessitate extensive training and fine-tuning, thereby limiting their adaptability and personalization capabilities [1][2]. OBJECTIVE This study aims to introduce an innovative framework that utilizes the linguistic capabilities of large language models (LLMs), specifically GPT-3.5, to create a dynamic, user-specific language and conversational corpus that aligns with the individual psychological and linguistic profiles of patients. METHODS Our approach, termed 'spontaneous language symbiosis and combustion,' eliminates the need for conventional training or fine-tuning. It establishes a symbiotic relationship between the LLM and the user's unique conversational data, facilitating real-time adaptation to the user's evolving language and mental state. RESULTS Implementation of this methodology has shown significant advancements in personalizing patient interactions, incorporating temporal abstractions, and enhancing the AI's operational framework to effectively anticipate and address adverse behavioral patterns. Ethical considerations were meticulously evaluated to ensure a balanced deployment that maximizes benefits while minimizing dependency risks [3][4]. CONCLUSIONS The findings from this study suggest that this cutting-edge AI-driven tool offers a cost-effective, adaptable, and empathetic alternative to traditional mental health care methodologies. By democratizing AI in healthcare, our tool emerges as a promising universal solution for mental health support, poised to transform clinical practices and mark a significant milestone in the convergence of artificial intelligence and healthcare. CLINICALTRIAL
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".