Categorizing Patient Disease into ICD-10 with Deep Learning for Semantic Text Classification
How to leverage insights into big electronic health records (EHRs) becomes increasingly important for accomplishing precision medicine to improve the quality of human healthcare. When analyzing big Chinese EHRs, there are a lot of applications that we need to categorize patients’ diseases according...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2020
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/71286 |
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| Sumario: | How to leverage insights into big electronic health records (EHRs) becomes increasingly important for accomplishing precision medicine to improve the quality of human healthcare. When analyzing big Chinese EHRs, there are a lot of applications that we need to categorize patients’ diseases according to the medical coding standard. In this paper, we develop natural language processing (NLP), deep learning, and machine learning algorithms to automatically categorize each patient’s individual diseases into the ICD-10 standard. Experimental results show that the convolutional neural network (CNN) algorithm outperforms the recurrent neural network (RNN)-based long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms, and it generates much better results than the support vector machine (SVM), one of the most popular conventional machine learning algorithms, demonstrating the great impact of deep learning on medical big data analysis. |
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| ISBN: | 978-1-83880-705-4 |
| DOI: | 10.5772/intechopen.91292 |