Corpus ID: 88518125

Embedding Complexity In the Data Representation Instead of In the Model: A Case Study Using Heterogeneous Medical Data

@inproceedings{Bajor2018EmbeddingCI,
  title={Embedding Complexity In the Data Representation Instead of In the Model: A Case Study Using Heterogeneous Medical Data},
  author={Jacek M. Bajor and Diego Alberto Mesa and Travis Osterman and Thomas A. Lasko},
  year={2018}
}
  • Jacek M. Bajor, Diego Alberto Mesa, +1 author Thomas A. Lasko
  • Published 2018
  • Mathematics
  • Electronic Health Records have become popular sources of data for secondary research, but their use is hampered by the amount of effort it takes to overcome the sparsity, irregularity, and noise that they contain. Modern learning architectures can remove the need for expert-driven feature engineering, but not the need for expert-driven preprocessing to abstract away the inherent messiness of clinical data. This preprocessing effort is often the dominant component of a typical clinical… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Figures and Tables from this paper.

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 42 REFERENCES

    XGBoost: A Scalable Tree Boosting System

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Opportunities and obstacles for deep learning in biology and medicine

    VIEW 2 EXCERPTS

    Deep learning for healthcare: review, opportunities and challenges. Brie€ngs in bioinformatics (2017)

    • Riccardo MioŠo, Fei Wang, Shuang Wang, Xiaoqian Jiang, Joel T Dudley
    • 2017
    VIEW 2 EXCERPTS