a b s t r a c tIn clinical medicine, multidimensional time series data can be used to find the rules of disease progress by data mining technology, such as classification and prediction. However, in multidimensional time series data mining problems, the excessive data dimension causes the inaccuracy of probability density distribution to increase the computational complexity. Besides, information redundancy and irrelevant features may lead to high computational complexity and over-fitting problems. The combination of these two factors can reduce the classification performance. To reduce computational complexity and to eliminate information redundancies and irrelevant features, we improved upon a multidimensional time series feature selection method to achieve dimension reduction. The improved method selects features through the combination of the Kozachenko-Leonenko (K-L) information entropy estimation method for feature extraction based on mutual information and the feature selection algorithm based on class separability. We performed experiments on the Electroencephalogram (EEG) dataset for verification and the non-small cell lung cancer (NSCLC) clinical dataset for application. The results show that with the comparison of CLeVer, Corona and AGV, respectively, the improved method can effectively reduce the dimensions of multidimensional time series for clinical data.
In real world, CTEPH is a relatively common and serious complication in PE patients diagnosed for the first time. Early diagnosis and treatment of PE will decrease the incidence of CTEPH in these unspecified patients.
Initial combination therapy in treatment-naive PAH subjects with WHO functional class III or IV can significantly improve 6MWD, hemodynamics, and quality of life compared with monotherapy. Further studies with large samples and placebo controls are required to assess the tolerability and efficacy of initial combination therapy in patients with PAH. (ClinicalTrials.gov registration NCT01712997).
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