Predicting Patient's Choices of Hospital Levels Using Deep Learning and Representation Improvements

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1251-1257
Number of pages7
ISBN (Electronic)9789881476890
StatePublished - 2021
Externally publishedYes
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 14 12 202117 12 2021

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period14/12/2117/12/21

Bibliographical note

Publisher Copyright:
© 2021 APSIPA.

Fingerprint

Dive into the research topics of 'Predicting Patient's Choices of Hospital Levels Using Deep Learning and Representation Improvements'. Together they form a unique fingerprint.

Cite this