Cluster analysis of patients suffering from addictions

International audienceWe investigate the contribution of unsupervised learning to identify patient's profiles suffering from addictions. We propose a new clustering approach based on coupling b-coloring of graph and Bregman hard clustering algorithm in order to automatically find the number of categories or groups of patients and the "best" representative patients' profile of each group. The study was carried out in close collaboration with the French co-operative health organization called the "Centre Mutualiste d'Addictologie", an aftercare centre for addictions. The quantitative data arises from a cohort of seven different aftercare centres for addiction located in France. The study concerns 301 patients suffering from dependence (addictions with psychoactive substances and/or behaviour addictions)

Constraint Selection for Semi-supervised Topological Clustering

In this paper, we propose to adapt the batch version of self-organizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones

Process mining for clinical processes: A comparative analysis of four Australian hospitals

Business process analysis and process mining, particularly within the health care domain, remain under-utilised. Applied research that employs such techniques to routinely collected, health care data enables stakeholders to empirically investigate care as it is delivered by different health providers. However, cross-organisational mining and the comparative analysis of processes present a set of unique challenges in terms of ensuring population and activity comparability, visualising the mined models and interpreting the results. Without addressing these issues, health providers will find it difficult to use process mining insights, and the potential benefits of evidence-based process improvement within health will remain unrealised. In this paper, we present a brief introduction on the nature of health care processes; a review of the process mining in health literature; and a case study conducted to explore and learn how health care data, and cross-organisational comparisons with process mining techniques may be approached. The case study applies process mining techniques to administrative and clinical data for patients who present with chest pain symptoms at one of four public hospitals in South Australia. We demonstrate an approach that provides detailed insights into clinical (quality of patient health) and fiscal (hospital budget) pressures in health care practice. We conclude by discussing the key lessons learned from our experience in conducting business process analysis and process mining based on the data from four different hospitals

Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection

International audienceIn this paper, we propose an efficient and robust approach for semi-supervised feature selection, based on the constrainedLaplacian score. Themain drawback of this method is the choice of the scant supervision information, represented by pairwise constraints. In fact, constraints are proven to have some noise which may deteriorate learning performance. In this work, we try to override any negative effects of constraint set by the variation of theirsources. This is achieved by an ensemble technique using both a resampling of data (bagging) and a random subspace strategy. Experiments on high-dimensional datasets are provided for validating the proposed approach and comparing it with other representative feature selection methods

An extensive empirical comparison of ensemble learning methods for binary classification

International audienc