To address the misclassifications observed in the text-based model within the multi-modal system, several strategies can be implemented:
Data Augmentation: Increasing the amount of text data by augmenting the existing transcripts or collecting additional data can help improve the model's performance. This can provide a more comprehensive understanding of the language patterns associated with schizophrenia symptoms.
Semantic Coherence Analysis: Since semantic coherence plays a crucial role in differentiating schizophrenia subjects from healthy controls, incorporating advanced natural language processing techniques to analyze the coherence of text responses can enhance the model's ability to detect subtle linguistic cues indicative of schizophrenia.
Contextual Information: Integrating contextual information from the interview setting, such as non-verbal cues or interviewer prompts, can provide a richer context for the text analysis. This contextual information can help the model better interpret the short utterances and improve the overall classification accuracy.
Ensemble Learning: Employing ensemble learning techniques by combining the predictions of multiple models, including different text processing approaches or pre-trained language models, can help mitigate the weaknesses of individual models and enhance the overall classification performance.
Fine-tuning Attention Mechanisms: Fine-tuning the attention mechanisms within the multi-modal system specifically for the text modality can help prioritize relevant textual features and improve the model's ability to capture the nuances of language associated with schizophrenia symptoms.