A processor implemented method, comprising: obtaining, via one or more hardware processors, a unique time series data corresponding to a plurality of parameters of one or more entities, characterized in that: automatically extracting, using an unsupervised encoder integrated within a Deep Recurrent Neural Network, RNN, executed by the one or more hardware processors, one or more features from the unique time series to obtain a unique features set for each of the plurality of parameters independently, wherein the unique features set comprises a fixed-dimensional feature vector, wherein the unique time series data includes clinical time series and the plurality of the parameters correspond to physiological parameters including a glucose level and a heart rate obtained from one or more sensors including a temperature sensor, a motion sensor, a health monitoring sensor; concatenating the one or more extracted features from the unique features set pertaining to each of the plurality of parameters to obtain a concatenated features set comprising a fixed-dimensional concatenated feature vector; learning a non-temporal linear classification model based on the concatenated features set, wherein the fixed-dimensional concatenated feature vector is used as an input for a classification task and the concatenated features set is mapped to a label, and wherein during the learning of the non-temporal linear classification model a weight is obtained using a Least Absolute Shrinkage and Selection Operator (LASSO)-regularized loss function as arg min w 1 N ∑ i = 1 N y i − w. z T i 2 + α ‖ w ‖ 1 where y ( i) ∈ {0,1], ‖ w ‖ 1 = ∑ j = 1 n ∑ k = 1 c w jk is a L 1 -norm, where w jk represents the weight assigned to k - th RNN feature for j - th raw input parameter, and α controls the extent of sparsity - with higher α implying more sparsity, that is, fewer RNN features are selected for the classification task; and generating a relevance score for each of the plurality of parameters such as the glucose level and the heart rate, as sum of absolute values of the weights assigned to the concatenated features set that provide insights of relevant features for the classification task, wherein the classification task includes patient phenotyping of Diabetes Mellitus with high relevance score for the glucose level parameter when learning the non-temporal linear classification model and in-hospital mortality prediction, wherein for the in-hospital mortality, the prediction is to whether the patient survives or not given the unique time series observations up to 48 hours. automatically extracting, using an unsupervised encoder integrated within a Deep Recurrent Neural Network, RNN, executed by the one or more hardware processors, one or more features from the unique time series to obtain a unique features set for each of the plurality of parameters independently, wherein the unique features set comprises a fixed-dimensional feature vector, wherein the unique time series data includes clinical time series and the plurality of the parameters correspond to physiological parameters including a glucose level and a heart rate obtained from one or more sensors including a temperature sensor, a motion sensor, a health monitoring sensor; concatenating the one or more extracted features from the unique features set pertaining to each of the plurality of parameters to obtain a concatenated features set comprising a fixed-dimensional concatenated feature vector; learning a non-temporal linear classification model based on the concatenated features set, wherein the fixed-dimensional concatenated feature vector is used as an input for a classification task and the concatenated features set is mapped to a label, and wherein during the learning of the non-temporal linear classification model a weight is obtained using a Least Absolute Shrinkage and Selection Operator (LASSO)-regularized loss function as arg min w 1 N ∑ i = 1 N y i − w. z T i 2 + α ‖ w ‖ 1 where y ( i) ∈ {0,1], ‖ w ‖ 1 = ∑ j = 1 n ∑ k = 1 c w jk is a L 1 -norm, where w jk represents the weight assigned to k - th RNN feature for j - th raw input parameter, and α controls the extent of sparsity - with higher α implying more sparsity, that is, fewer RNN features are selected for the classification task; and generating a relevance score for each of the plurality of parameters such as the glucose level and the heart rate, as sum of absolute values of the weights assigned to the concatenated features set that provide insights of relevant features for the classification task, wherein the classification task includes patient phenotyping of Diabetes Mellitus with high relevance score for the glucose level parameter when learning the non-temporal linear classification model and in-hospital mortality prediction, wherein for the in-hospital mortality, the prediction is to whether the patient survives or not given the unique time series observations up to 48 hours.