Encoding Health Records into Pathway Representations for Deep Learning
| Autor: | Stefanie Speichert, Vanessa Lopez, Natasha Mulligan, Joao H. Bettencourt-Silva, Marco Luca Sbodio |
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| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
Source code Computer science business.industry Deep learning media_common.quotation_subject Health records Machine learning computer.software_genre Convolutional neural network Patient pathway Task (project management) Encoding (memory) Artificial intelligence business computer media_common |
| DOI: | 10.3233/shti210800 |
| Popis: | There is a growing trend in building deep learning patient representations from health records to obtain a comprehensive view of a patient’s data for machine learning tasks. This paper proposes a reproducible approach to generate patient pathways from health records and to transform them into a machine-processable image-like structure useful for deep learning tasks. Based on this approach, we generated over a million pathways from FAIR synthetic health records and used them to train a convolutional neural network. Our initial experiments show the accuracy of the CNN on a prediction task is comparable or better than other autoencoders trained on the same data, while requiring significantly less computational resources for training. We also assess the impact of the size of the training dataset on autoencoders performances. The source code for generating pathways from health records is provided as open source. |
| Databáze: | OpenAIRE |
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