This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.

The analysis of medical data is a challenging task for health care systems since a huge amount of interesting knowledge can be automatically mined to effectively support both physicians and health care organizations. This paper proposes a data analysis framework based on a multiple-level clustering technique to identify the examination pathways commonly followed by patients with a given disease. This knowledge can support health care organizations in evaluating the medical treatments usually adopted, and thus the incurred costs. The proposed multiple-level strategy allows clustering patient examination datasets with a variable distribution. To measure the relevance of specific examinations for a given disease complication, patient examination data has been represented in the Vector Space Model using the TF-IDF method. As a case study, the proposed approach has been applied to the diabetic care scenario. The experimental validation, performed on a real collection of diabetic patients, demonstrates the effectiveness of the approach in identifying groups of patients with a similar examination history and increasing severity in diabetes complications.

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Arterial hypertension and cancer are two of the most important causes of mortality in the world; correlations between these two clinical entities are complex and various. Cancer therapy using old (e.g., mitotic spindle poisons) as well as new (e.g., monoclonal antibody) drugs may cause arterial hypertension through different mechanisms; sometimes the increase of blood pressure levels may be responsible for chemotherapy withdrawal. Among newer cancer therapies, drugs interacting with the VEGF (vascular endothelial growth factors) pathways are the most frequently involved in hypertension development. However, many retrospective studies have suggested a relationship between antihypertensive treatment and risk of cancer, raising vast public concern. The purposes of this brief review have then been to analyse the role of chemotherapy in the pathogenesis of hypertension, to summarize the general rules of arterial hypertension management in this field and finally to evaluate the effects of antihypertensive therapy on cancer disease.

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