ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm.
Liu, Juan (Medallia) | Bier, Eric (Palo Alto Research Center) | Wilson, Aaron (Palo Alto Research Center) | Guerra-Gomez, John Alexis (Yahoo Labs) | Honda, Tomonori (Inflection.com) | Sricharan, Kumar (Palo Alto Research Center) | Gilpin, Leilani (Massachusetts Institute for Technology) | Davies, Daniel (Palo Alto Research Center)
Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure.
Inappropriate prescribing of antimicrobials is a major clinical concern that affects as many as 50 percent of prescriptions. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. As a remedy, we are developing a machine learning algorithm that combines instance-based learning and rule induction techniques to discover new rules for detecting inappropriate prescriptions from previous false alerts.
Weiss, Jeremy C. (University of Wisconsin-Madison) | Natarajan, Sriraam (Wake Forest University) | Peissig, Peggy L. (Marshfield Clinic Research Foundation) | McCarty, Catherine A. (Essentia Institute of Rural Health) | Page, David (University of Wisconsin-Madison)
Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices.
Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The third aims to build a hypoglycemia predictor that could alert patients to dangerously low blood glucose levels in time to take preventive action.
Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this article we focus on detecting hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are obtained using standard labor monitoring devices. Then, we use the parameters of these models as attributes in a binary classification problem.
In this article we provide insight into the BodyMedia FIT armband system -- a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system's success.
Barrett, Christopher (Network Dynamics and Sim Science Lab) | Bisset, Keith (Network Dynamics and Sim Science Lab) | Leidig, Jonathan (Network Dynamics and Sim Science Lab) | Marathe, Achla (Network Dynamics and Sim Science Lab) | Marathe, Madhav V. (Network Dynamics and Sim Science Lab)
We discuss an interaction-based approach to study the coevolution between socio-technical networks, individual behaviors, and contagion processes on these networks. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics – a general purpose modeling environment to support pandemic planning and response. New advances in network science, machine learning, high performance computing, data mining and behavioral modeling were necessary to develop Simdemics.
Farm managers have to deal with many conflicting objectives when planning which crop to cultivate. Soil characteristics are extremely important when determining yield potential. According to the objectives to be considered the crop selection problem may be difficult to solve using traditional tools. Therefore, this work proposes an approach based on Multiobjective Evolutionary Algorithms to help in the selection of an appropriate cultivation plan considering five crop alternatives and five objectives simultaneously.
Mattiussi, Claudio (Swiss Federal Institute of Technology in Lausanne (EPFL)) | Marbach, Daniel (Swiss Federal Institute of Technology in Lausanne (EPFL)) | Dürr, Peter (Swiss Federal Institute of Technology in Lausanne (EPFL)) | Floreano, Dario (Swiss Federal Institute of Technology in Lausanne (EPFL))
Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and the reverse engineering of biological networks.