Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41