Estimation of Record Contents for Automatic Generation of Care Records

Reviewed
Haru Kaneko, Tahera Hossain, Sozo Inoue,
Activity and Behavior Computing, Springer Nature
(Not Available)
(Not Available)
289-306
2021-01-01
https://link.springer.com/chapter/10.1007/978-981-15-8944-7_18
Elderly person are increasing all over the world. Smart nursing facilities
are required to support them for their good health.Especially in Japan, it is essential to
have automated nursing facilities center to support the relentlessly increasing elderly
person all over the country.As long as elderly people are increasing, the demand for
nursing care services are also increasing day by day.There is a shortage of nurse
to support this vast elderly people.In order to resolve the shortage of nurse, it is
important to reduce the work load for nurse.There are some research to simplify the
care recording process.The main approach is activity recognition or the development
of new apps. However, we aimed for automatic generation of the care records of near
future. So in this paper, we evaluate the accuracy of the automatic generation of
care records using care record data from real nursing facility to simplify the task of
making care records.We made two machine learning models to estimate the "target
patient" and the "recorded value" of care records.And we evaluated the classification
result from the data collected by the care record app.We used two months of data we
collected from Japanese nursing facility for the evaluation.We can achieve, average
F1-score of ’target patient’ estimation is 74% and the average F1-score of ’detail of
record’ estimation is 58%. We believe that using these results in care records app
will simplify the task of making care records.

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