Authors:
Jing Qi
1
;
Girvan Burnside
2
;
Paul Charnley
3
and
Frans Coenen
1
Affiliations:
1
Department of Computer Science, The University of Liverpool, Liverpool L69 3BX, U.K.
;
2
Department of Biostatistics, The University of Liverpool, Liverpool L69 3BX, U.K.
;
3
Wirral University Teaching Hospital NHS Foundation Trust, Arrowe Park Hospital, Wirral CH49 5PE, U.K.
Keyword(s):
Data Prioritisation, Time Series Classification, kNN, LSTM-RNN.
Abstract:
A particular challenge for any hospital is the large amount of pathology data that doctors are routinely presented with. Pathology result analysis is routine in hospital environments. Some form of machine learning for pathology result prioritisation is therefore desirable. Patients typically have a history of pathology results, and each pathology result may have several dimensions, hence time series analysis for prioritisation suggests itself. However, because of the resource required, labelled prioritisation training data is typically not readily available. Hence traditional supervised learning and/or ranking is not a realistic solution and some alternative solution is required. The idea presented in this paper is to use the outcome event, what happened to a patient, as a proxy for a ground truth prioritisation data set. This idea is explored using two approaches: kNN time series classification and Long Short-Term Memory deep learning.