JAMA Medical News Machine Learning for Earlier Diagnosis of Schizophrenia
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Mar 7, 2025 Søren Dinesen Østergaard, a professor at Aarhus University and expert in affective disorders, joins Roy H. Perlis to delve into the groundbreaking use of machine learning for predicting the onset of schizophrenia and bipolar disorder. They discuss how these models can identify at-risk patients in the critical prodromal phase, emphasizing the importance of timely diagnosis. The conversation also touches on the challenges of cross-site validation and the need for careful interpretation of predictive outcomes in diverse clinical settings.
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Challenges in Early Diagnosis
- Early detection of schizophrenia and bipolar disorder is challenging because symptoms can be subtle and mimic other conditions.
- Delayed diagnosis leads to worse patient outcomes, highlighting the need for earlier intervention.
Target Population
- The study focuses on individuals already receiving psychiatric treatment for less severe disorders like depression or anxiety.
- The goal is to identify the prodromal phase of schizophrenia or bipolar disorder within this group.
Practical Application of the Model
- Use the machine learning model as a paraclinical test to guide diagnostic attention.
- A positive prediction should prompt clinicians to ask more questions about schizophrenia or bipolar disorder.