Learning Electronic Health Records through Hyperbolic Embedding of Medical Ontologies

Lu, Qiuhao; Silva, Nisansa de; Kafle, Sabin; Cao, Jiazhen; Dou, Dejing; Nguyen, Thien Huu; Sen, Prithviraj; Hailpern, Brent; Reinwald, Berthold; Li, Yunyao

doi:10.1145/3307339.3342148

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…This enables us to train the model within a reasonable amount of time, despite using thousands of features -i.e. our best performing model trained in 40 minutes × 14 epochs on a single GPU 3 .…”

Section: Prognostic Multitask Modelmentioning

confidence: 99%

“…For hour buckets with few features we zero pad. Each embedding e t from bucket at hour t 3 GeForce GTX 1080 Ti with 11GB memory, 10 Core CPU and 32GB RAM.…”

Section: Prognostic Multitask Modelmentioning

confidence: 99%

“…For many machine learning tasks we can observe that more complex models with more data tend to outperform the previous state-of-art model. Particularly deep learning approaches can implicitly learn to extract and apply task relevant features [1][2][3][4][5][6][7][8] . However, complex models require more compute resources and large amounts of data to learn well -usually the more data the better.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a highly limited feature set combined with relatively small dataset does not provide enough input information to fully utilize deep learning methods. This problem is compounded by an increasing trend to adopt the latest large, complex models with many learnable parameters from domains like computer vision or natural language processing [1][2][3][4][5][6][7][8] , where data types are much more homogeneous and large scale data can be readily exploited. On the other hand, Tomašev et al 10 provide a recent example of the performance benefits gained from using all instead of an expert-selected feature set.…”

Section: Introductionmentioning

confidence: 99%

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EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings

Rethmeier

Serbetci

Möller

et al. 2020

Preprint

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Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinical prediction did not improve expectedly. Instead of following this trend of using complex neural models in combination with small, pre-selected feature sets, we propose EffiCare, which focuses on minimizing hospital resource requirements for assistive clinical prediction models. First, by embedding medical events,we eliminate manual domain feature-engineering and increase the amount of learning data. Second, we use small,but data-efficient models, that compute faster and are easier to interpret. We evaluate our approach on four clinical prediction tasks and achieve substantial performance improvements over highly resource-demanding state-of-the-art methods. Finally, to evaluate our model beyond score improvements, we apply explainability and interpretability methods to analyze the decisions of our model and whether it uses data sources and parameters efficiently.

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Section: Prognostic Multitask Modelmentioning

confidence: 99%

“…For hour buckets with few features we zero pad. Each embedding e t from bucket at hour t 3 GeForce GTX 1080 Ti with 11GB memory, 10 Core CPU and 32GB RAM.…”

Section: Prognostic Multitask Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings

Rethmeier

Serbetci

Möller

et al. 2020

Preprint

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“…In this experiment, we follow the data preprocessing procedure of previous work (Harutyunyan et al, 2017;Lin et al, 2018;Lu et al, 2019), and generate a dataset of 48, 410 ICU stay records out of the freely available MIMIC-III database (Johnson et al, 2016). The task is to predict whether or not a patient in an ICU stay will be readmitted within 30 days after discharge.…”

Section: Dataset and Second View Constructionmentioning

confidence: 99%

Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization

Silva

Dou

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods.

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