TY - JOUR AU - Wolff, J. AU - Gary, A. AU - Jung, D. AU - Normann, C. AU - Kaier, K. AU - Binder, H. AU - Domschke, K. AU - Klimke, A. AU - Franz, M. PY - 2020 DA - 2020/02/06 TI - Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach JO - BMC Medical Informatics and Decision Making SP - 21 VL - 20 IS - 1 AB - A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-020-1042-2 DO - 10.1186/s12911-020-1042-2 ID - Wolff2020 ER -