Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification

Authors

  • Aishat Salau Nile University of Nigeria, Nigeria
  • Nnanna Agwu Nwojo Nile University of Nigeria, Nigeria
  • Moussa Mahamat Boukar Nile University of Nigeria, Nigeria
  • Osasumwen Usen Independent Researcher, Nigeria
Volume: 13 | Issue: 6 | Pages: 12205-12210 | December 2023 | https://doi.org/10.48084/etasr.6332

Abstract

This study presents a novel approach to medical text classification using a deep active incremental learning model, aiming to improve the automation of the preauthorization process in medical health insurance. By automating decision-making for request approval or denial through text classification techniques, the primary focus is on real-time prediction, utilization of limited labeled data, and continuous model improvement. The proposed approach combines a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with active learning, using uncertainty sampling to facilitate expert-based sample selection and online learning for continuous updates. The proposed model demonstrates improved predictive accuracy over a baseline Long Short-Term Memory (LSTM) model. Through active learning iterations, the proposed model achieved a 4% improvement in balanced accuracy over 100 iterations, underscoring its efficiency in continuous refinement using limited labeled data.

Keywords:

medical text classification, active learning, deep learning, incremental learning, medical preauthorization

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How to Cite

[1]
Salau, A., Agwu Nwojo, N., Mahamat Boukar, M. and Usen, O. 2023. Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification. Engineering, Technology & Applied Science Research. 13, 6 (Dec. 2023), 12205–12210. DOI:https://doi.org/10.48084/etasr.6332.

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