Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records

摘要

ObjectiveMental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses—autism, bipolar disorder, and schizophrenia.