A Calculated Risk: Commentary on “Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia”
The advent of machine learning (ML) in psychiatry offers a tantalizing promise: to tame the inherent complexity and heterogeneity of mental illness and its treatment into actionable, patient-specific predictions. The manuscript, “Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia,” addresses one of the most persistent and debilitating iatrogenic conditions in psychiatry—tardive dyskinesia (TD). The very title, with its cautious “may help,” appropriately signals both the potential and the preliminary nature of this endeavor.
The central thesis is both clinically relevant and methodologically sound. TD, a often irreversible movement disorder caused by dopamine receptor blockade, remains a major clinical challenge. While the introduction of second-generation antipsychotics was thought to lower the risk, TD has not been relegated to the history books. The ability to predict which patients are most vulnerable before they receive a specific antipsychotic would be a paradigm shift, moving from reactive management to proactive, personalized prevention. This manuscript positions itself at this critical intersection.
manuscript link:
https://pubmed.ncbi.nlm.nih.gov/36621324/
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