Abstract: Technology for predicting health conditions of patients is disclosed. In an example, a first data set comprising features of health data is obtained. A first epoch of training is performed using the first data set. A second data set is generated by applying a bias value to values of a first feature of the first data set. A second epoch of training is performed using the second data set to train the machine learning model. A first set of data comprising static data and a second set of data comprising dynamic data is received, from which a time series data set is derived. A value is determined as absent in the time series data set. The value is assigned using a given data. The time series data set is provided as input to the trained machine learning model to predict health conditions of a patient.