Unsupervised learning for anomaly detection in Australian medical payment data.

James Kemp James Kemp Unsupervised learning for anomaly detection in Australian medical payment data. 2023 University of New South Wales, Sydney, Australia http://hdl.handle.net/1959.4/101495 https://unsworks.unsw.edu.au/bitstreams/46420ed4-b564-4177-9c34-b4ca47ad8d0a/download https://doi.org/10.26190/unsworks/25202 https://www.base-search.net/Record/1bc508c82065ff81ca033028163c7cb9b607a9eeeb41b2662e33705986b3beef base-search.net (ftunswworks:oai:unsworks.library.unsw.edu.au:1959.4/101495) James Kemp Christopher Barker Norm Good Michael Bain 0001 Developing an anomaly detection framework for Medicare claims. 234-237 2023 ACSW https://doi.org/10.1145/3579375.3579410 conf/acsw/2023 db/conf/acsw/acsw2023.html#KempBGB23 James Kemp Christopher Barker Norm Good Michael Bain 0001 Context Discovery and Cost Prediction for Detection of Anomalous Medical Claims, with Ontology Structure Providing Domain Knowledge. 29-40 2023 HEALTHINF https://doi.org/10.5220/0011611000003414 conf/biostec/2023hi db/conf/biostec/healthinf2023.html#KempBGB23 James Kemp Christopher Barker Norm Good Michael Bain 0001 Graphical Association Analysis for Identifying Variation in Provider Claims for Joint Replacement Surgery. 805-809 2023 MedInfo https://doi.org/10.3233/SHTI231076 conf/medinfo/2023 db/conf/medinfo/medinfo2023.html#KempBG023 James Kemp Christopher Barker Norm Good Michael Bain 0001 Sequential pattern detection for identifying courses of treatment and anomalous claim behaviour in medical insurance. 3039-3046 2022 BIBM https://doi.org/10.1109/BIBM55620.2022.9995541 conf/bibm/2022 db/conf/bibm/bibm2022.html#KempBGB22 Michael Bain 0001 Christopher Barker Norm Good