Modeling patient trajectories using different representation learning techniques
| Title | Modeling patient trajectories using different representation learning techniques |
|---|---|
| Summary | Modeling Electronic Health Record (EHR) data and predict future events for specific patients |
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| References | Attention is all you need: https://arxiv.org/abs/1706.03762
Bert: Pre-training of deep bidirectional transformers for language understanding: https://arxiv.org/abs/1810.04805 BEHRT: transformer for electronic health records: https://www.nature.com/articles/s41598-020-62922-y MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning: https://arxiv.org/abs/2107.09288 Heterogeneous Similarity Graph Neural Network on Electronic Health Records: https://arxiv.org/abs/2101.06800 Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer: https://arxiv.org/abs/1906.04716 Variationally Regularized Graph-based Representation Learning for Electronic Health Records: https://arxiv.org/pdf/1912.03761.pdf |
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| Author | |
| Supervisor | Stefan Byttner, Kobra Etminani, Amira Soliman |
| Level | Master |
| Status | Open |
The importance of effective modeling 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. A patient trajectory describes the evolution of a quantity, behavior, biomarkers from lab tests, or some other repeated measure of interest over time. Modeling patient trajectories is a hot topic and there are many state-the-art deep learning techniques used for such purpose.
In this master thesis, the objective is to compare state-of-the-art representation learning techniques for modeling EHR data and predicting patient trajectories. For this comparative analysis we plan to consider transformers, recurrent neural architectures (RNNs), and graph neural networks (GNNs). Patients with congestive heart failure can be used as a use-case, specifically predicting the readmission of those patients is one of the challenging tasks for such group.
