An anti-fraud framework for medical insurance based on deep learning

Guoming Zhang, Shucun Fu, Xiaolong Xu, Lianyong Qi, Xuyun Zhang, Wanchun Dou*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Given rising medical costs, medical expense control has become an important task in the healthcare domain. To solve the shortage of medical reimbursement mechanisms based on medical service items, single-disease payment models have been extensively studied. However, the approach of payment via a single-disease model is also flawed, and fraud may occur. Herein, we present an anti-fraud framework for medical insurance based on deep learning to automatically identify suspicious medical records, ensure the effective implementation of single-disease charges, and reduce the workload of medical insurance auditors. The framework first predicts the probabilities of diseases according to patients’ chief complaints and then evaluates whether the disease codes written in medical records are reasonable via the predicted probabilities; finally, medical records with unreasonable disease codes are selected as abnormal cases for manual auditing. We conduct experiments on a real-world dataset from a large hospital and demonstrate that our model can play an effective role in anti-fraud for medical insurance.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication15th International Conference, ADMA 2019, Proceedings
EditorsJianxin Li, Sen Wang, Shaowen Qin, Xue Li, Shuliang Wang
Place of PublicationSwitzerland
PublisherSpringer
Pages871-878
Number of pages8
ISBN (Electronic)9783030352318
ISBN (Print)9783030352301
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event15th International Conference on Advanced Data Mining and Applications, ADMA 2019 - Dalian, China
Duration: 21 Nov 201923 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11888 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Advanced Data Mining and Applications, ADMA 2019
CountryChina
CityDalian
Period21/11/1923/11/19

Keywords

  • Anti-fraud
  • Deep learning
  • Electronic Medical Record (EMR)
  • International Classification of Diseases (ICD)

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