Time Series Cleaning Methods for Hospital Emergency Admissions

Abstract

Due to the nature of hospital emergency services, density cannot be easily estimated. It is one of the important issues that should be planned for emergency service managers to have sufficient resources continuously in services that develop suddenly, and emergency interventions are made for human life. Effective and efficient management and planning of limited resources are important not only for hospital administrators but also for people who will receive service from emergency services. In this situation, estimating the number of people who will request service in the emergency service with the least error is of great importance in terms of resource management and the operations carried out in the emergency services. The density of patients coming to the emergency department may vary according to the season, special dates, and even time zones during the day. The aim of the study is to show that more successful results will be obtained because of processing the time series by considering the country and area-specific features instead of the traditional approach. In this paper, the patient admission dataset of the public hospital emergency service in Turkey was used. Data cleaning and arranging operations were carried out by considering the official and religious special days of Turkey and the time periods during the day. The data set is first handled holistically, and its performances are measured by making predictions with the LSTM (Long Short Term Memory) model. Then, to examine the effect of time zones, performance values were calculated separately by dividing each day into 3 equal time zones. Finally, to investigate the effect of triage areas on the total density, the model performance was measured by dividing the data forming each time zone into 3 different triage areas in 3 equal time periods. Three stages were applied both on the raw data set and on the data created by extracting the official, religious holidays, and weekend data specific to Turkey. According to the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) results, more successful results are obtained thanks to the cleaning and editing processes. Thanks to the study, it is thought that the data sets used for demand forecasting studies in the health sector will produce results closer to reality by determining and standardizing the purification criteria in this way.

Keywords

• LSTM
• Data Cleaning
• Emergency services
• Time Series

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