Activity Prediction Method for Nursing Care Records with Missing Entries

Abstract

With the proliferation of Information and Communication Technology (ICT), recording systems for care activities using smartphones and tablets are becoming widespread in the field of nursing care. As a result, it is becoming possible to predict future caregiving activities using the care record history recorded in the systems. The prediction of future caregiving activity enables us to develop various caregiving applications, such as support for the preparation of future caregiving activity, detection of missing entries in nursing care records, and prior information for real-time caregiving activity recognition using sensors. However, while the recording has become easier with the introduction of the recording systems, there are still many missing nursing care entries. When data with many missing entries is used as training data for activity prediction methods, the performance of the prediction methods will deteriorate. In this paper, we propose a caregiving activity prediction method that is robust against missing entries. The proposed model has a module for correcting missing entries, and its intermediate output is used to estimate whether or not a certain caregiving activity will occur from the current time to one hour later using the caregiving activity records of the past T hours. We evaluated the effectiveness of the proposed method using data obtained from actual nursing care facilities.