Abstract
A mechanism to support the prioritisation of multi-variate pathology data, in the absence of a ground truth prioritisation, is presented. The motivation is the ever increasing quantity of pathology data that clinicians are expected to consider. The fundamental idea, given a previously unseen pathology result and the associated pathology history, is to use a deep learning model to predict future pathology results and then use the prediction to classify the new pathology result according to a pre-defined set of prioritisation levels. A further challenge is that patient pathology history, expressed as a multi-variate time series, tends to be irregularly time stamped and of variable length. The proposed approach used a Recurrent Neural Network to make predictions and a bounding box technique for the classification. The approach was evaluated using Urea and Electrolytes pathology data. The operation of the proposed approach was also compared with previously reported approaches, and was found to outperform these previous approaches.
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Qi, J., Burnside, G., Coenen, F. (2022). Pathology Data Prioritisation: A Study Using Multi-variate Time Series. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2022. Lecture Notes in Computer Science, vol 13428. Springer, Cham. https://doi.org/10.1007/978-3-031-12670-3_13
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DOI: https://doi.org/10.1007/978-3-031-12670-3_13
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