Abstract
In countries that enable patients to choose their own healthcare providers, a common problem is that the patients do not go to unsuitable hospital levels. This might cause problems such as overwhelming tertiary facilities with mild condition patients, and resulting in limited the treatment for acute and critical patients. Our aim is to predict patients' choices of hospital levels to support the evaluation during policy making. We proposed a deep neural network (DNN) framework, which involves an improvement of the representation for insurance data, a DNN design to make accurate predictions, and a model interpretation to further understand the decision of the model. This study used the 5-year nationwide insurance data of Taiwan. With the combination of autoencoder (AE) and DNN, the prediction results achieved an accuracy of 0.94, area under the receiver operating characteristics curve (AUC) of 0.88, sensitivity of 0.93, and specificity of 0.97 with highly imbalanced data. The result shows that changing data representation had a positive effect on the prediction model. The model interpretation results show that past medical experiences and recommendations of acquaintances are most important. Deep learning technology acts as a feasible tool that provides additional evaluation besides using traditional statistical methods.
Original language | English |
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Title of host publication | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1251-1257 |
Number of pages | 7 |
ISBN (Electronic) | 9789881476890 |
State | Published - 2021 |
Externally published | Yes |
Event | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan Duration: 14 12 2021 → 17 12 2021 |
Publication series
Name | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings |
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Conference
Conference | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 |
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Country/Territory | Japan |
City | Tokyo |
Period | 14/12/21 → 17/12/21 |
Bibliographical note
Publisher Copyright:© 2021 APSIPA.