A Masked language model for multi-source EHR trajectories contextual representation learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916121104351232 |
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| author | Amirahmadi, Ali Ohlsson, Mattias Etminani, Kobra Melander, Olle Björk, Jonas |
| author_facet | Amirahmadi, Ali Ohlsson, Mattias Etminani, Kobra Melander, Olle Björk, Jonas |
| contents | Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Masked language model for multi-source EHR trajectories contextual representation learning Amirahmadi, Ali Ohlsson, Mattias Etminani, Kobra Melander, Olle Björk, Jonas Machine Learning Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes). |
| title | A Masked language model for multi-source EHR trajectories contextual representation learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.06675 |