Learning from medical data streams: an introduction

Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference

Bio-inspired Approaches for Engineering Adaptive Systems

AbstractAdaptive systems are composed of different heterogeneous parts or entities that interact and perform actions favouring the emer- gence of global desired behavior. In this type of systems entities might join or leave without disturbing the collective, and the system should self-organize and continue performing their goals. Furthermore, entities must self-evolve and self-improve by learn- ing from their interactions with the environment. The main challenge for engineering these systems is to design and develop distributed and adaptive algorithms that allow system entities to select the best suitable strategy/action and drive the system to the best suitable behavior according to the current state of the system and environment changes. This paper describes existing work related to the development of adaptive systems and approaches and shed light on how features from natural and biological systems could be exploited for engineering adaptive approaches

Analytical Procedures Phase of PCAOB Audits: A Note of Caution in Selecting the Forecasting Model

The best-practices execution of PCAOB audits requires the use of Analytical Procedures at the Planning and the Substantive Phases. This often finds the auditor using the standard OLS two-parameter linear regression forecasting model [OLSR] to project account-values from the Planning Phase to balances expected at Year-End so as to effect a variance analysis at the Substantive Phase. This is the point of departure of our study. We examine the practical effect of using the OLSR model in a time-series context of the audit. Specifically, this research report provides information on the use of the OLSR model as the model of choice in the audit context compared to the ARIMA(0,2,2)/Holt model which is usually the standard choice for an exponential smoothing model in the presence of autocorrelation of data in the time-stream; autocorrelation is the usual case for longitudinal series taken in the audit. Results: We find that there are reasons to condition the selection of the forecasting model in the Analytical Procedures context based upon autocorrelation in the data-stream. When the time-stream of data exhibits autocorrelation the OLSR model fails in a statistically significant manner to capture the next or one-period ahead client value at the same rate as does the ARIMA/Holt model. This then has implications for the False Negative Investigation Error

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p