Meryem Beyazal, Senem Ozgur, Ilker Ufuk Sayici, Serpil Kaya Celebi
Assessment of Heart Rate Variability in Children with Mitral Valve Prolapse
Pediatric cardiology
despite extensive research prodigious advances neuroscience comprehension nature schizophrenia remains rudimentary failure make progress attributed extreme heterogeneity condition enormous complexity human brain limitations extant research paradigms inadequacy traditional statistical methods integrate interpret increasingly large amounts multidimensional information relevant unravelling brain function fortunately rapidly developing science machine learning appears provide tools capable addressing impediments enthusiasm potential machine learning methods break current impasse reflected steep increase number scientific publication application machine learning study schizophrenia machine learning approaches however poorly understood schizophrenia researchers clinicians alike paper provide simple description nature techniques machine learning application study schizophrenia summarize potential constraints illustrations six studies machine learning schizophrenia address common misconceptions machine learning suggest guidelines researchers readers science editors reviewers burgeoning machine learning literature schizophrenia order realize enormous promise suggest need disciplined application machine learning methods study schizophrenia clear recognition capability challenges accompanied concurrent effort improve machine learning literacy among neuroscientists mental health professionals