Predicting non-attendance in hospital outpatient appointments using Deep Learning Approach

Dashtban, M. and Li, W. ORCID: https://orcid.org/0000-0003-2878-3185 (2022) Predicting non-attendance in hospital outpatient appointments using Deep Learning Approach. Health Systems, 11 (3). pp. 189-210. ISSN 2047-6965 doi: 10.1080/20476965.2021.1924085

Abstract/Summary

The hospital outpatient non-attendance imposes a substantial financial burden on hospitals and roots in multiple diverse reasons. This research aims to build an advanced predictive model for predicting non-attendance regarding the whole spectrum of probable contributing factors to non-attendance that could be collated from heterogeneous sources including electronic patients records and external non-hospital data. We proposed a new non-attendance prediction model based on deep neural networks and machine learning models. The proposed approach works upon sparse stacked denoising autoencoders (SDAEs) to learn the underlying manifold of data and thereby compacting information and providing a better representation that can be utilised afterwards by other learning models as well. The proposed approach is evaluated over real hospital data and compared with several well-known and scalable machine learning models. The evaluation results reveal the proposed approach with softmax layer and logistic regression outperforms other methods in practice.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/97845
Item Type	Article
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Health Innovation Partnership (HIP) Henley Business School > Digitalisation, Marketing and Entrepreneurship
Publisher	Taylor and Francis
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