A Fraud Resilient Medical Insurance Claim System
Authors: Yuliang Shi, Chenfei Sun, Qingzhong Li, Lizhen Cui, Han Yu, Chunyan Miao
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Shandong University, Jinan, China 2Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore |
| Pseudocode | No | The paper describes the system modules textually and with a diagram, but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No statement regarding the release of open-source code or a link to a code repository for the described methodology was found. |
| Open Datasets | No | The paper states the system has been 'tested in an online medical insurance claim system in China' and uses data from 'The Dareway Medical Insurance Claim System, which is being used by Zibo City in China'. This indicates a specific, likely private, dataset. No concrete access information, such as links, DOIs, repository names, or citations to a publicly available dataset, is provided. |
| Dataset Splits | No | The paper mentions processing historical claim data and building baselines but does not specify exact training, validation, or test split percentages, absolute sample counts, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments or the system were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | No | The paper describes the system's components and their conceptual operations but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |