Adaptive Prediction Timing for Electronic Health Records
Published in ICLR 2020, 2020
Recommended citation: Deasy J, Ercole A, and Lio P (2020). Adaptive Prediction Timing for Electronic Health Records. arXiv preprint arXiv:2003.02554. http://jacobdeasy.github.io/files/publications/2020-03-05-adaptive.pdf
This paper presents an adaptive prediction timing method for sequences, based on equi-precision rather than equi-time intervals, applied to electronic health records. This work was carried out during my PhD with Dr Ari Ercole and Prof. Pietro Lio at the University of Cambridge and accepted at the Machine learning in real life workshop at ICLR 2020.
Recommended citation: Deasy J, Ercole A, and Lio P (2020). Adaptive Prediction Timing for Electronic Health Records. arXiv preprint arXiv:2003.02554.