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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260921T211556Z
UID:Seminar-DMML-1113@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Danushka Bollegala:MAILTO:Danushka.Bollegala@liverpool.ac.uk
DTSTART:20210407T113000
DTEND:20210407T123000
SUMMARY:Data Mining and Machine Learning Series
DESCRIPTION:Jing Qi: Data Prioritisation in the Absence of a Ground Truth\n\nPathology 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, to address the problem of data prioritisation in the absence of ground truth data, we proposed two solutions with the first one considering prioritisation by Anomaly Detection (using DBSAN) and the second using Ground Truth Proxy (KNN and RNN). Experimental results show that both mechanisms were able to identify priority records.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1113
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