Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
8-1-2019 12:00 AM
End Date
11-1-2019 12:00 AM
Description
The hospital outpatient non-attendance imposes huge financial burden on hospitals every year. The non- attendance issue roots in multiple diverse reasons which makes the problem space particularly complicated and undiscovered. The aim of this research is to build an advanced predictive model for non-attendance considering whole spectrum of factors and their complexities from big hospital data. We proposed a novel non-attendance prediction model based on deep neural networks. The proposed method is based on sparse stacked denoising autoencoders (SSDAEs). Different with exiting deep learning applications in hospital data which have separated data reconstruction and prediction phases, our model integrated both phases aiming to have higher performance than divided- classification model in predicting tasks from EPR. The proposed method is compared with some well-known machine learning classifiers and representative research works for non-attendance prediction. The evaluation results reveal that the proposed deep approach drastically outperforms other methods in practice.
Deep Learning for Predicting Non-attendance in Hospital Outpatient Appointments
Grand Wailea, Hawaii
The hospital outpatient non-attendance imposes huge financial burden on hospitals every year. The non- attendance issue roots in multiple diverse reasons which makes the problem space particularly complicated and undiscovered. The aim of this research is to build an advanced predictive model for non-attendance considering whole spectrum of factors and their complexities from big hospital data. We proposed a novel non-attendance prediction model based on deep neural networks. The proposed method is based on sparse stacked denoising autoencoders (SSDAEs). Different with exiting deep learning applications in hospital data which have separated data reconstruction and prediction phases, our model integrated both phases aiming to have higher performance than divided- classification model in predicting tasks from EPR. The proposed method is compared with some well-known machine learning classifiers and representative research works for non-attendance prediction. The evaluation results reveal that the proposed deep approach drastically outperforms other methods in practice.
https://aisel.aisnet.org/hicss-52/hc/big_data_on_healthcare_app/3