Evolving classification of intensive care patients from event data

Objective: This work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm—evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes. / Materials and methods: An oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom. / Results: Retrospective study of 3452 episodes of adult patients (≥ 16 years of age) admitted to the ICUs of Guy’s and St. Thomas’ hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n = 2287 and validation set n = 1165. Episode-related time steps: Day 0—time of ICU admission, Day x—end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC = 0.652), Day 1: IIN (AUC = 0.660), Day 2: J48 decision-tree algorithm (AUC = 0.678), Days 3–7: regenerative IN (AUC = 0.717–0.772). Logistic regression AUC: 0.582 (Day 0)—0.827 (Day 7). / Conclusions: Our experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy

Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads

Mining Weighted Frequent Closed Episodes over Multiple Sequences

Frequent episode discovery is introduced to mine useful and interesting temporal patterns from sequential data. The existing episode mining methods mainly focused on mining from a single long sequence consisting of events with time constraints. However, there can be multiple sequences of different importance as the persons or entities associated with each sequence can be of different importance. Aiming to mine episodes in multiple sequences of different importance, we first define a new kind of episodes, i.e., the weighted frequent closed episodes, to take sequence importance, episode distribution and occurrence frequency into account together. Secondly, to facilitate the mining of such new episodes, we present a new concept called maximal duration serial episodes to cut a whole sequence into multiple maximum episodes using duration constraints, and discuss its properties for episode shrinking processing. Finally, based on the theoretical properties, we propose a two-phase approach to efficiently mine these new episodes. In Phase I, we adopt a level-wise episode shrinking framework to discover the candidate frequent closed episodes with the same prefixes, and in Phase II, we match the candidates with different prefixes to find the frequent close episodes. Experiments on simulated and real datasets demonstrate that the proposed episode mining strategy has good mining effectiveness and efficiency

Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur