TY - JOUR AU - Wang, Yuan AU - Wei, Yake AU - Yang, Hao AU - Li, Jingwei AU - Zhou, Yubo AU - Wu, Qin PY - 2020 DA - 2020/09/21 TI - Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model JO - BMC Medical Informatics and Decision Making SP - 238 VL - 20 IS - 1 AB - Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients’ outcomes and data mining is such a powerful prediction tool, many AKI prediction models based on machine learning methods have been proposed. Our motivation is inspired by the fact that the incidence of AKI is a changing temporal sequence affected by the joint action of patients’ daily drug combinations and their physiological indexes. However, most existing models have not considered such a temporal correlation. Besides, due to great challenges caused by sparse, high-dimensional and highly imbalanced clinical data, it is hard to achieve ideal performance. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-020-01245-4 DO - 10.1186/s12911-020-01245-4 ID - Wang2020 ER -