Machine Learning Treats Brain Disorders Where They Most Often Occur

These neuropsychiatric disorders are prevalent in low- to middle-income countries due to various factors, e.g. Around 80 percent of the world's epilepsy occurs in low- to middle-income countries, but only 20 percent of people get treatment. The physician-to-patient ratio can be as low as one for every 20,000 people in those countries, with even fewer psychiatrists and neurologists, causing a so-called treatment gap.1 However, timely diagnosis and treatment of epilepsy is possible and can make a difference.2 Last fall, partnering with the Nanyang Technological University (NTU) of Singapore, we took the first steps in tackling this challenge in our Science for Social Good program. Our team included a Social Good Fellow from Columbia University, several machine learning and cloud computing researchers from IBM Research, and collaborators from NTU. Together, we came up with a cloud-based automated machine learning approach to provide decision support for non-specialist physicians in electroencephalography (EEG) analysis and interpretation.