Research Using Machine Learning in Psychiatry Expands Rapidly

Interest in the application of machine learning to psychiatry is growing exponentially, as reflected in the increasing number of publications on the subject. Here is a small sample of seminal papers on machine learning:

• “Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.” Chekroud and colleagues used machine learning to analyze the predictive value of 20 depressive symptoms on choice of antidepressant medication using data on more than 7,000 patients from nine antidepressant clinical trials. They found three “symptom clusters”—sleep/insomnia symptoms, core emotional symptoms (such as sad mood and feelings of worthlessness), and atypical symptoms (such as psychomotor agitation or suicidal ideation)—that were associated with response to different medications. For instance, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms. Moreover, the predictive power of the symptom clusters was greater than the difference between any one medication and placebo.

• “Multisite Prediction of 4-Week and 52-Week Treatment Outcomes in Patients With First-Episode Psychosis: A Machine Learning Approach.” Koutsouleris and colleagues applied machine learning to data from 334 patients in the European First-Episode Schizophrenia Trial to predict poor versus good treatment outcomes after four weeks and 52 weeks of treatment. Unemployment, poor education, functional deficits, and unmet psychosocial needs predicted both endpoints at four weeks and 52 weeks, whereas previous depressive episodes, male sex, and suicidality additionally predicted poor one-year outcomes. Predictions at 52 weeks identified patients at risk for symptom persistence, nonadherence to treatment, readmission to hospital, and poor quality of life. Among these patients, amisulpride and olanzapine showed superior efficacy over haloperidol, quetiapine, and ziprasidone.

• “Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.” Koutsouleris and colleagues applied machine learning to MRI data on 92 patients with schizophrenia enrolled in the multisite RESIS trial to predict response to transcranial magnetic stimulation.

• “Treatment Response Prediction and Individualized Identification of First-Episode Drug-Naïve Schizophrenia Using Brain Functional Connectivity.” Bo Cao, M.D., of the University of Alberta and colleagues applied machine learning to MRI data. By measuring the connections of the superior temporal cortex to other regions of the brain, the algorithm successfully identified patients with schizophrenia with 78 percent accuracy. It also predicted with 82 percent accuracy whether a patient would respond to risperidone. ■