Elias Egho

On mining complex sequential data by means of FCA and pattern structures

Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of “complex” sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of formal concept analysis and its extension based on “pattern structures”. Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e. a data reduction of sequential structures) are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analysing interesting patient patterns from a French healthcare data-set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use-case which is the main motivation for this work.

On measuring similarity for sequences of itemsets

Computing the similarity between sequences is a very important challenge for many different data mining tasks. There is a plethora of similarity measures for sequences in the literature, most of them being designed for sequences of items. In this work, we study the problem of measuring the similarity between sequences of itemsets. We focus on the notion of common subsequences as a way to measure similarity between a pair of sequences composed of a list of itemsets. We present new combinatorial results for efficiently counting distinct and common subsequences. These theoretical results are the cornerstone of an effective dynamic programming approach to deal with this problem. In addition, we propose an approximate method to speed up the computation process for long sequences. We have applied our method to various data sets: healthcare trajectories, online handwritten characters and synthetic data. Our results confirm that our measure of similarity produces competitive scores and indicate that our method is relevant for large scale sequential data analysis.

An Approach for Mining Care Trajectories for Chronic Diseases

With the increasing burden of chronic illnesses, administrative health care databases hold valuable information that could be used to monitor and assess the processes shaping the trajectory of care of chronic patients. In this context, temporal data mining methods are promising tools, though lacking flexibility in addressing the complex nature of medical events. Here, we present a new algorithm able to extract patient trajectory patterns with different levels of granularity by relying on external taxonomies. We show the interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system.

Healthcare trajectory mining by combining multidimensional component and itemsets

Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g.

Mining heterogeneous multidimensional sequential patterns

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A contribution to the discovery of multidimensional patterns in healthcare trajectories

Radiography, Appendectomy

On Projections of Sequential Pattern Structures (with an application on care trajectories)

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A FCA-based analysis of sequential care trajectories

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The representation of sequential patterns and their projections within Formal Concept Analysis

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Application Domains - Biology and Chemistry

Aspirin, Paracetamol