The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of
depressive syndromes. The heterogeneity of
depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, "a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed" as the "next-generation treatment for
mental disorders" by Thomas Insel. Understanding the heterogeneity renders promise for
personalized medicine to treat cases of
depressive syndrome, in terms of both defining
symptom clusters and selecting
antidepressants. Machine learning algorithms have emerged as a tool for
personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive
biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining
symptom clusters and selecting
antidepressants. Potentially applicable suggestions to realize machine learning-based
personalized medicine for
depressive syndrome are also provided herein.