Abstract: Methods, systems, and apparatus for a method that predicts an individual survival survival time of a patient. The method includes obtaining clinical data associated with health factors of the patient. The method includes obtaining liquid biopsy data associated with one or more attributes of diseased cells within the patient. The method includes predicting or determining a survival time of the patient using a deep learning model based on the clinical data and the liquid biopsy data. The method includes providing or outputting the survival time.

Abstract: A method for creating an interpretable model for healthcare predictions includes training, by a deep learning processor, a neural network to predict health information by providing training data, including multiple combinations of measured or observed health metrics and corresponding medical results, to the neural network. The method also includes determining, by the deep learning processor and using the neural network, prediction data including predicted results for the measured or observed health metrics for each of the multiple combinations of the measured or observed health metrics based on the training data. The method also includes training, by the deep learning processor or a learning processor, an interpretable machine learning model to make similar predictions as the neural network by providing mimic data, including combinations of the measured or observed health metrics and corresponding predicted results of the prediction data, to the interpretable machine learning model.

Abstract: Methods, systems, and apparatus for a method that predicts an individual survival survival time of a patient. The method includes obtaining clinical data associated with health factors of the patient. The method includes obtaining liquid biopsy data associated with one or more attributes of diseased cells within the patient. The method includes predicting or determining a survival time of the patient using a deep learning model based on the clinical data and the liquid biopsy data. The method includes providing or outputting the survival time.

Abstract: A method for creating an interpretable model for healthcare predictions includes training, by a deep learning processor, a neural network to predict health information by providing training data, including multiple combinations of measured or observed health metrics and corresponding medical results, to the neural network. The method also includes determining, by the deep learning processor and using the neural network, prediction data including predicted results for the measured or observed health metrics for each of the multiple combinations of the measured or observed health metrics based on the training data. The method also includes training, by the deep learning processor or a learning processor, an interpretable machine learning model to make similar predictions as the neural network by providing mimic data, including combinations of the measured or observed health metrics and corresponding predicted results of the prediction data, to the interpretable machine learning model.

Abstract: An optimization-driven sparse learning framework is disclosed to identify discriminative system components among system input features that are essential for system output prediction. In biomarker discovery, to handle the combinatorial interactions among gene or protein expression measurements for identifying interaction complexes and disease biomarkers, the system uses both single input features and high-order input feature interactions.