Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study.

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets.

Provision of Decision Support Through Continuous Prediction of Recurring Clinical Actions.

We propose a framework for provision of decision support through the continuous prediction of recurring targets, in particular clinical actions, which can potentially occur more than once in the patient's longitudinal clinical record. We first perform an abstraction of the patient's raw time-stamped data into intervals. Then, we partition the patient's timeline into time windows, and perform frequent temporal patterns mining in the features' window.