The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help
pages 795–802. IEEE, 2013. It is well known that supervised learning problems with ℓ1 (Lasso) and ℓ2 (Tikhonov or Ridge) regularizers will result in very different solutions. For example, the ℓ1 solution vector will be sparser and can potentially be used both for prediction and feature selection. However, given a data set it is often hard to determine
Speaker: Bo Han (RedMarker/Kaplan Professional) Seminar Date: Tuesday November 28 12:00pm Brief abstract: Social media has become a gold mine of insights into the people, opinions and events. However, this massive amount of data also challenges the efficiency and effectiveness of existing machine learning algorithms. One way to approach this problem is “divide-and-conquer”. You can partition the data into
Speaker: Tom Osborn Seminar Date: Tuesday November 21 12:00pm Brief abstract: Active learning is a branch of semi-supervised machine learning for domains where data is expensive and where precise understanding is critical. Traditionally, active learning builds a model of a domain with a trade-off between exploration and exploitation probing of a domain to generate labelled data usefully. Earlier applications
Evidence from UK interest rate futures market reveals that traders have been “drowning” the market with oversized orders, increasing their allocation under a pure pro-rata matching algorithm. The 2007 introduction of a time element to the order matching mechanism has modified the behaviour of traders. No longer is the order book drowned with orders which
Speaker: Dr Felicity Flack, PHRN Seminar Date: Tuesday October 17 12:00pm Brief abstract: The Population Health Research Network (PHRN) has been funded via the National Collaborative Research Infrastructure Strategy (NCRIS), in collaboration with universities, as well as state and territory governments, to develop national data linkage infrastructure for Australian researchers. In 2013-14 the PHRN Program Office consulted with the
Speaker: Michael Sheng Seminar Date: Tuesday October 10 12:00pm Short Bio: Michael Sheng is a professor at Macquarie University, Sydney, since 1 January 2017. Dr. Michael Sheng was a full Professor and Deputy Head of the School of Computer Science at the University of Adelaide. Michael holds a PhD degree in computer science from the University of New South
Speaker: Georgie Drury & Anita Grindlay (from startup Sprinday) Seminar Date: Tuesday September 19 12:00pm Brief abstract: To put it simply, Springday powers wellbeing. We use technology to engage with individuals by creating an ecosystem of wellbeing resources, gamified challenges and activities, software and hardware integrations, face-to-face services and benefits. Forrester Consulting, Thought Leadership Paper, The Dawn of Data-Driven
Speaker: Roger Kermode (UTS) Seminar Date: Tuesday September 5 12:00pm Brief abstract: Recent research by the AIIA shows that many organisations are flying on auto-pilot when it comes to effectively using data and analytics for strategic purposes. Most of the effort locally in AI and Machine Learning focuses on operational and tactical issues which presents both risks (for incumbent
Speaker: Nejla Ghaboosi (IR) Seminar Date: Tuesday August 29 12:00pm Brief abstract: A substantial fraction of unsupervised learning research is driven by generative modelling. Generative Adversarial Networks (GANs) are a recently introduced class of generative models. This presentation tries to address: (1) why GANs are worth of studying, (2) how GANs compare to other generative models, (3) the details