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. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

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. Finally, we use the discovered patterns as features for a prediction model. We demonstrate the framework on the task of treatment prediction in the Intensive Care Unit, in the domains of Hypoglycemia, Hypokalemia and Hypotension.

Medical treatment decision making is a complex process that involves integrating multivariate time-oriented data from multiple sources and is often influenced by factors such as patient load. In this study, we propose the Recurring Target Prediction (RTP) Pipeline to support treatment decision making by predicting the next medical action most likely to be administered, based on the historical data from patients in similar contexts. The method transforms raw time-stamped data into symbolic time intervals, incorporating domain knowledge. Each of the patient’s data are segmented by pre-defined trigger conditions (e.g., hypoglycemia), with each segment containing a feature window (historical data as symbolic time intervals); a prediction window (e.g., treatment dosage); and an optional prediction gap between the feature and prediction windows, enabling a future treatment alert. A frequent pattern-mining method is applied to the feature windows, and features generated from the mined patterns (e.g., count within each record and mean duration) are used as input to a Two-Step prediction model. First, a binary classifier predicts whether treatment is necessary, followed by a regression model to predict dosage. Finally, SHapley Additive exPlanations (SHAP) provide insights into the model’s decision making. We have evaluated the pipeline on an Intensive Care Unit (ICU) dataset, across three domains: hypoglycemia, hypokalemia, and hypotension. Key contributions include leveraging the recurrence of medical conditions and events to enrich the dataset, reducing false positives through a Two-Step prediction model, allowing prediction gaps for advance treatment notice, and incorporating SHAP, and introducing a two-level SHAP-based method for aggregating the relative weights of temporal patterns and components, to enhance the model’s interpretability.