Difference between revisions of "Timeseries representation learning for EHR"
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https://proceedings-of-deim.github.io/DEIM2022/papers/H23-4.pdf | https://proceedings-of-deim.github.io/DEIM2022/papers/H23-4.pdf | ||
https://proceedings.neurips.cc/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf | https://proceedings.neurips.cc/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf | ||
| − | + | |Supervisor=Amira Soliman, Stefan Byttner, Kobra Etminani, Omar Hamed, Ali Amirahmadi | |
| − | |Supervisor=Amira Soliman, Stefan Byttner, Kobra Etminani, Omar Hamed | + | |
|Level=Master | |Level=Master | ||
|Status=Open | |Status=Open | ||
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The use of Electronic Health Records (EHR) is increasing in both research and clinical practice to enhance the ability to provide the needed care to patients without posing additional economic burdens. Patient data is considered temporal data that is being measured over time. EHR data is complex and sparse therefore challenging to model. In this master thesis, the objective is to investigate the state-of-the-art techniques for representation learning of temporal data and compare their impact on target machine learning tasks using EHR data. Example of research questions: How time affects representation space? How to handle irregular timescales? | The use of Electronic Health Records (EHR) is increasing in both research and clinical practice to enhance the ability to provide the needed care to patients without posing additional economic burdens. Patient data is considered temporal data that is being measured over time. EHR data is complex and sparse therefore challenging to model. In this master thesis, the objective is to investigate the state-of-the-art techniques for representation learning of temporal data and compare their impact on target machine learning tasks using EHR data. Example of research questions: How time affects representation space? How to handle irregular timescales? | ||
Latest revision as of 08:49, 5 October 2022
| Title | Timeseries representation learning for EHR |
|---|---|
| Summary | Timeseries representation learning for Electronic Health Records |
| Keywords | Timeseries data, Electronic Health Records, Representation Learning |
| TimeFrame | |
| References | Data: https://mimic.mit.edu/docs/about/
Papers: https://arxiv.org/pdf/1907.05321.pdf https://www.ijcai.org/proceedings/2021/0324.pdf https://proceedings-of-deim.github.io/DEIM2022/papers/H23-4.pdf https://proceedings.neurips.cc/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf |
| Prerequisites | |
| Author | |
| Supervisor | Amira Soliman, Stefan Byttner, Kobra Etminani, Omar Hamed, Ali Amirahmadi |
| Level | Master |
| Status | Open |
The use of Electronic Health Records (EHR) is increasing in both research and clinical practice to enhance the ability to provide the needed care to patients without posing additional economic burdens. Patient data is considered temporal data that is being measured over time. EHR data is complex and sparse therefore challenging to model. In this master thesis, the objective is to investigate the state-of-the-art techniques for representation learning of temporal data and compare their impact on target machine learning tasks using EHR data. Example of research questions: How time affects representation space? How to handle irregular timescales?
