Ranking Pathology Data in the Absence of a Ground Truth

Pathology results play a critical role in medical decision making. A particular challenge is the large number of pathology results that doctors are presented with on a daily basis. Some form of pathology result prioritisation is therefore a necessity. However, there is no readily available training data that would support a traditional supervised learning approach. Thus some alternative solutions are needed. There are two approaches presented in this paper, anomaly-based unsupervised pathology prioritisation and proxy ground truth-based supervised pathology prioritisation. Two variations of each were considered. With respect to the first, point and time series based unsupervised anomaly prioritisation; and with respect to the second kNN and RNN proxy ground truth-based supervised prioritisation. To act as a focus, Urea and Electrolytes pathology testing was used. The reported evaluation indicated that the RNN proxy ground truth-based supervised pathology prioritisation method produced the best results.

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