Sunday, 18 December 2016

Caring deeper: motif detection from medical records using convolutional nets

In the previous post, I have introduced DeepCare, a LSTM-based model for (potentially very long) medical records with irregular timing and treatments.

Here I introduce another deep net called Deepr, a CNN-based model for relatively short medical records. The main purpose is learning to discover medical motifs that lead to some future events (e.g., death).

Unlike DeepCare which assumes a clear temporal dynamics in the medical records, Deepr requires only repeated short patterns (motifs) over the data sequence. Time gaps are discretized into symbols which are treated in the same way as diagnoses, procedures and medications. All symbols are then sequenced. Those co-occurring will be randomly ordered.

Once Deepr has been learnt, motif segments in a record that respond well to an outcome can be detected.

Note that Deepr can be used in other situations where irregular time gaps and discrete data are present.

Update references

  • Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, 2017.
  • DeepCare: A Deep Dynamic Memory Model for Predictive Medicine, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, PAKDD'16, Auckland, NZ, April 2016.

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