{"id":4064,"date":"2026-02-18T13:23:55","date_gmt":"2026-02-18T13:23:55","guid":{"rendered":"https:\/\/ifx0.com\/?p=4064"},"modified":"2026-02-18T13:23:57","modified_gmt":"2026-02-18T13:23:57","slug":"antipsychotic-based-machine-learning-models-may-help-prediction-of-tardive-dyskinesia-in-patients-with-schizophrenia-introductionary-comment-on-manuscript","status":"publish","type":"post","link":"https:\/\/ifx0.com\/index.php\/2026\/02\/18\/antipsychotic-based-machine-learning-models-may-help-prediction-of-tardive-dyskinesia-in-patients-with-schizophrenia-introductionary-comment-on-manuscript\/","title":{"rendered":"Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia  (introductionary comment on manuscript)"},"content":{"rendered":"\n<h3>A Calculated Risk: Commentary on &#8220;Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia&#8221;<\/h3>\n<p class=\"ds-markdown-paragraph\">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, &#8220;Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia,&#8221; addresses one of the most persistent and debilitating iatrogenic conditions in psychiatry\u2014tardive dyskinesia (TD). The very title, with its cautious &#8220;may help,&#8221; appropriately signals both the potential and the preliminary nature of this endeavor.<\/p>\n<p class=\"ds-markdown-paragraph\">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.<\/p>\n<p>manuscript link:<\/p>\n<p><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/36621324\/\">https:\/\/pubmed.ncbi.nlm.nih.gov\/36621324\/<\/a><\/p>\n<p>\u00a0<\/p>\n<p>cite:<\/p>\n<div class=\"gs_citr\" tabindex=\"0\">Uludag, K., Wang, D. M., Mohamoud, Y., Wu, H. E., &amp; Zhang, X. (2023). Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia. <i>Schizophrenia research<\/i>, <i>252<\/i>, 33-35.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>A Calculated Risk: Commentary on &#8220;Antipsychotic-based machine learning models may help prediction of tardive dyskinesia in patients with schizophrenia&#8221; The advent of machine learning (ML) in psychiatry offers a tantalizing promise: to tame the inherent complexity and heterogeneity of mental <a href=\"https:\/\/ifx0.com\/index.php\/2026\/02\/18\/antipsychotic-based-machine-learning-models-may-help-prediction-of-tardive-dyskinesia-in-patients-with-schizophrenia-introductionary-comment-on-manuscript\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4064","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/posts\/4064","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/comments?post=4064"}],"version-history":[{"count":1,"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/posts\/4064\/revisions"}],"predecessor-version":[{"id":4065,"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/posts\/4064\/revisions\/4065"}],"wp:attachment":[{"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/media?parent=4064"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/categories?post=4064"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ifx0.com\/index.php\/wp-json\/wp\/v2\/tags?post=4064"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}