Islab at 07:49, 5 October 2022

2022-10-05T07:49:46Z

Islab: Created page with "{{StudentProjectTemplate |Summary=Timeseries representation learning for Electronic Health Records |Keywords=Timeseries data, Electronic Health Records, Representation Learnin..."

2022-09-30T14:46:10Z

Created page with "{{StudentProjectTemplate |Summary=Timeseries representation learning for Electronic Health Records |Keywords=Timeseries data, Electronic Health Records, Representation Learnin..."

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{{StudentProjectTemplate
|Summary=Timeseries representation learning for Electronic Health Records
|Keywords=Timeseries data, Electronic Health Records, Representation Learning
|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

|Supervisor=Amira Soliman, Stefan Byttner, Kobra Etminani, Omar Hamed
|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?

@@ Line 8: / Line 8: @@
 https://proceedings-of-deim.github.io/DEIM2022/papers/H23-4.pdf
 https://proceedings.neurips.cc/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf
+|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?

Timeseries representation learning for EHR - Revision history

Islab at 07:49, 5 October 2022

Islab: Created page with "{{StudentProjectTemplate |Summary=Timeseries representation learning for Electronic Health Records |Keywords=Timeseries data, Electronic Health Records, Representation Learnin..."