An Ensemble Net of Convolutional Auto-Encoder and Graph Auto-Encoder for Auto-Diagnosis
| Autor: | Linlin You, Jie Chen, Guokai Yan, Changping Ji, Jianqiang Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry 020208 electrical & electronic engineering 02 engineering and technology Machine learning computer.software_genre Autoencoder Knowledge-based systems Information coding 020901 industrial engineering & automation Artificial Intelligence Software deployment 0202 electrical engineering electronic engineering information engineering Robot Graph (abstract data type) Artificial intelligence Medical diagnosis business computer Software Decoding methods |
| Zdroj: | IEEE Transactions on Cognitive and Developmental Systems. 13:189-199 |
| ISSN: | 2379-8939 2379-8920 |
| DOI: | 10.1109/tcds.2020.2984335 |
| Popis: | Effective auto-diagnosis assistants can benefit our healthcare system in various aspects, such as, saving labor cost, sharing knowledge among the crowd, and timely supporting the patients. However, the existing auto-diagnosis models are ineffective due to issues caused by information island, poor information coding, and inefficient informative retrieval. To address these issues, this article presents a diagnosis assistant that is designed and implemented to manage abundant historical inquiries between patients and doctors. The core of the auto-diagnosis system is a novel model called ensemble net of convolutional auto-encoder and graph auto-encoder (EN-C+GAE) which can be trained using historical data and generate a list of candidate diagnoses for a doctor to select. The experimental results show that the proposed approach outperforms the counterparts in generating more fluent and relevant diagnoses. The proposed system also shows its potential in real-world deployment in healthcare scenarios. |
| Databáze: | OpenAIRE |
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