This paper aims to assess the predictive value of physiological data daily collected in a telemonitoring study in the early detection of heart failure (HF) decompensation events. The main hypothesis is that physiological time series with similar progression (trends) may have prognostic value in future clinical states (decompensation or normal condition). The strategy is composed of two main steps: a trend similarity analysis and a predictive procedure. The similarity scheme combines the Haar wavelet decomposition, in which signals are represented as linear combinations of a set of orthogonal bases, with the Karhunen-Loève transform, that allows the selection of the reduced set of bases that capture the fundamental behavior of the time series. The prediction process assumes that future evolution of current condition can be inferred from the progression of past physiological time series. Therefore, founded on the trend similarity measure, a set of time series presenting a progression similar to the current condition is identified in the historical dataset, which is then employed, through a nearest neighbor approach, in the current prediction. The strategy is evaluated using physiological data resulting from the myHeart telemonitoring study, namely blood pressure, respiration rate, heart rate, and body weight collected from 41 patients (15 decompensation events and 26 normal conditions). The obtained results suggest, in general, that the physiological data have predictive value, and in particular, that the proposed scheme is particularly appropriate to address the early detection of HF decompensation.

Cardiovascular disease has a huge impact on health care services, originating unsustainable costs at clinical, social, and economic levels. In this context, patients' risk stratification tools are central to support clinical decisions contributing to the implementation of effective preventive health care. Although useful, these tools present some limitations, in particular, some lack of performance as well as the impossibility to consider new risk factors potentially important in the prognosis of severe cardiac events. Moreover, the actual use of these tools in the daily practice requires the physicians' trust. The main goal of this work addresses these two issues: (i) evaluate the importance of inflammation biomarkers when combined with a risk assessment tool; (ii) incorporation of personalization and interpretability as key elements of that assessment. Firstly, machine learning based models were created to assess the potential of the inflammation biomarkers applied in secondary prevention, namely in the prediction of the six month risk of death/myocardial infarction. Then, an approach based on three main phases was created: (i) set of interpretable rules supported by clinical evidence; (ii) selection based on a machine learning classifier able to identify for a given patient the most suitable subset of rules; (iii) an ensemble scheme combining the previous subset of rules in the estimation of the patient cardiovascular risk. All the results were statistically validated (t-test, Wilcoxon-signed rank test) according to a previous verification of data normality (Shapiro-Wilk). The proposed methodology was applied to a real acute coronary syndrome patients dataset (N = 1544) from the Cardiology Unit of Coimbra Hospital and Universitary centre. The first assessment was based on the GRACE tool and a Random Forest classifier, the incorporation of inflammation biomarkers achieved SE=0.83; SP=0.84 whereas the original GRACE risk factors reached SE=0.75; SP=0.85. In the second phase, the proposed approach with inflammation biomarkers achieved SE=0.763 and SP=0.778. This approach confirms the potential of combining inflammation markers with the GRACE score, increasing SE and SP, when compared with the original GRACE. Additionally, it assures interpretability and personalization, which are critical issues to allow its application in the daily clinical practice.