ANALYZING SCHIZOPHRENIC TENDENCIES USING AI AND PRELIMINARY RESEARCH

MindBalance is a mobile application developed to provide real-time support for individuals experiencing symptoms of schizophrenia. The platform integrates cognitive assessment, auditory hallucination validation, positive affirmations, and psychoeducation into a single accessible tool [6]. It features a semantic fluency test that utilizes a Siamese neural network trained on the AnSim dataset to detect signs of disorganized thinking, and an auditory validation program that compares user-described sounds to real-world audio recordings using a large language model [7]. Positive affirmations are generated through the Google Gemini model to reframe negative thought patterns in real time. Experiments were conducted to validate the system's core functions: a Siamese network assessed semantic coherence between animal name pairs, and a large language model evaluated discrepancies between perceived and recorded sounds. Results demonstrated that lightweight models could accurately detect semantic disorganization and auditory inconsistencies, supporting the feasibility of using AI-driven mobile tools for real-time schizophrenia symptom monitoring [8].

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Publication Details

Published
2025-06-21
DOI
https://doi.org/10.5121/csit.2025.151104
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
0.00
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article

ANALYZING SCHIZOPHRENIC TENDENCIES USING AI AND PRELIMINARY RESEARCH

T. J. Wang, Austin Amakye Ansah
Mental Health Research Topics
article

ANALYZING SCHIZOPHRENIC TENDENCIES USING AI AND PRELIMINARY RESEARCH

T. J. Wang, Austin Amakye Ansah
article en

Abstract

MindBalance is a mobile application developed to provide real-time support for individuals experiencing symptoms of schizophrenia. The platform integrates cognitive assessment, auditory hallucination validation, positive affirmations, and psychoeducation into a single accessible tool [6]. It features a semantic fluency test that utilizes a Siamese neural network trained on the AnSim dataset to detect signs of disorganized thinking, and an auditory validation program that compares user-described sounds to real-world audio recordings using a large language model [7]. Positive affirmations are generated through the Google Gemini model to reframe negative thought patterns in real time. Experiments were conducted to validate the system's core functions: a Siamese network assessed semantic coherence between animal name pairs, and a large language model evaluated discrepancies between perceived and recorded sounds. Results demonstrated that lightweight models could accurately detect semantic disorganization and auditory inconsistencies, supporting the feasibility of using AI-driven mobile tools for real-time schizophrenia symptom monitoring [8].

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Mental Health Research Topics
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