Approximate Data Mining Techniques on Clinical Data

The past two decades have witnessed an explosion in the number of medical and healthcare datasets available to researchers and healthcare professionals. Data collection efforts are highly required, and this prompts the development of appropriate data mining techniques and tools that can automatically extract relevant information from data. Consequently, they provide insights into various clinical behaviors or processes captured by the data. Since these tools should support decision-making activities of medical experts, all the extracted information must be represented in a human-friendly way, that is, in a concise and easy-to-understand form. To this purpose, here we propose a new framework that collects different new mining techniques and tools proposed. These techniques mainly focus on two aspects: the temporal one and the predictive one. All of these techniques were then applied to clinical data and, in particular, ICU data from MIMIC III database. It showed the flexibility of the framework, which is able to retrieve different outcomes from the overall dataset. The first two techniques rely on the concept of Approximate Temporal Functional Dependencies (ATFDs). ATFDs have been proposed, with their suitable treatment of temporal information, as a methodological tool for mining clinical data. An example of the knowledge derivable through dependencies may be "within 15 days, patients with the same diagnosis and the same therapy usually receive the same daily amount of drug". However, current ATFD models are not analyzing the temporal evolution of the data, such as "For most patients with the same diagnosis, the same drug is prescribed after the same symptom". To this extent, we propose a new kind of ATFD called Approximate Pure Temporally Evolving Functional Dependencies (APEFDs). Another limitation of such kind of dependencies is that they cannot deal with quantitative data when some tolerance can be allowed for numerical values. In particular, this limitation arises in clinical data warehouses, where analysis and mining have to consider one or more measures related to quantitative data (such as lab test results and vital signs), concerning multiple dimensional (alphanumeric) attributes (such as patient, hospital, physician, diagnosis) and some time dimensions (such as the day since hospitalization and the calendar date). According to this scenario, we introduce a new kind of ATFD, named Multi-Approximate Temporal Functional Dependency (MATFD), which considers dependencies between dimensions and quantitative measures from temporal clinical data. These new dependencies may provide new knowledge as "within 15 days, patients with the same diagnosis and the same therapy receive a daily amount of drug within a fixed range". The other techniques are based on pattern mining, which has also been proposed as a methodological tool for mining clinical data. However, many methods proposed so far focus on mining of temporal rules which describe relationships between data sequences or instantaneous events, without considering the presence of more complex temporal patterns into the dataset. These patterns, such as trends of a particular vital sign, are often very relevant for clinicians. Moreover, it is really interesting to discover if some sort of event, such as a drug administration, is capable of changing these trends and how. To this extent, we propose a new kind of temporal patterns, called Trend-Event Patterns (TEPs), that focuses on events and their influence on trends that can be retrieved from some measures, such as vital signs. With TEPs we can express concepts such as "The administration of paracetamol on a patient with an increasing temperature leads to a decreasing trend in temperature after such administration occurs". We also decided to analyze another interesting pattern mining technique that includes prediction. This technique discovers a compact set of patterns that aim to describe the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important to improve the overall class prediction performance. We show that our classification approach achieves a significant reduction in the number of extracted patterns, compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model. For each technique described above, we developed a tool to retrieve its kind of rule. All the results are obtained by pre-processing and mining clinical data and, as mentioned before, in particular ICU data from MIMIC III database

Discovery of Spatiotemporal Event Sequences

Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [02/2019/TN

Pattern Discovery from Event Data

Events are ubiquitous in real-life. With the rapid rise of the popularity of social media channels, massive amounts of event data, such as information about festivals, concerts, or meetings, are increasingly created and shared by users on the Internet. Deriving insights or knowledge from such social media data provides a semantically rich basis for many applications, for instance, social media marketing, service recommendation, sales promotion, or enrichment of existing data sources. In spite of substantial research on discovering valuable knowledge from various types of social media data such as microblog data, check-in data, or GPS trajectories, interestingly there has been only little work on mining event data for useful patterns. In this thesis, we focus on the discovery of interesting, useful patterns from datasets of events, where information about these events is shared by and spread across social media platforms. To deal with the existence of heterogeneous event data sources, we propose a comprehensive framework to model events for pattern mining purposes, where each event is described by three components: context, time, and location. This framework allows one to easily define how events are related in terms of conceptual, temporal, and spatial (geographic) relationships. Moreover, we also take into account hierarchies for contexts, time, and locations of events, which naturally exist as useful background knowledge to derive patterns at different levels of abstraction and granularity. Based on this framework, we focus on the following problems: (i) mining interval-based event sequence patterns, (ii) mining periodic event patterns, and (iii) extracting semantic annotations for locations of events. Generally, the first two problems consider correlations of events whereas the last one takes correlations of event components into account. In particular, the first problem is a generalization of mining sequential patterns from traditional data, where patterns representing complex temporal relationships among events can be discovered at different levels of abstraction and granularity. The second problem is to find periodic event patterns, where a notion of relaxed periodicity is formulated for events as well as for groups of events that co-occur. The third~problem is to extract semantic annotations for locations on the basis of exploiting correlations of contexts, time, and locations of events. For the three problems above, we respectively propose novel and efficient approaches. Our experiments clearly indicate that extracted patterns and knowledge can be well utilized in various useful tasks, such as event prediction, semantic search for locations, or topic-based clustering of locations

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com