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Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation

Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation

However, UMAP is designed to preserve the global structure of the data, making it more stable and less sensitive to parameter changes and initial conditions compared with other dimensionality reduction algorithms such as t-distributed Stochastic Neighbor Embedding. To illustrate this stability, we present UMAP projections using the specified parameters with different random seeds (Figure 4) and with the same random seed but varying local neighborhood and minimum distance parameters (Figure 5).

Ryan Allen Shewcraft, John Schwarz, Mariann Micsinai Balan

JMIR AI 2025;4:e67369

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