Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each of a plurality of systems, including maintenance data and operational data. The instructions are further executable to display one or more algorithm templates and one or more data features calculated from the operational data, in a graphical user interface. The instructions are further executable to receive a selection of an algorithm template, a data feature, and a value of a parameter associated with the algorithm template, and to train and evaluate the selected algorithm template on the selected data feature according to the received value. The instructions are further executable to display a result of a metric of the evaluation, and generate a predictive maintenance model using the selected algorithm template.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including maintenance data and operational data. The instructions are further executable to evaluate correlation between each of a plurality of operational data features and maintenance events of the maintenance data, using a supervised machine learning classification model. The instructions are further executable to display a quantitative result of the evaluation for each operational data feature in a graphical user interface, receive a selection of one or more operational data features of the plurality of operational data features, and generate a predictive maintenance model using the selected one or more operational data features according to a machine learning method.

Abstract: The present invention relates, in part, to methods for the stratification, prognosis, diagnosis and stratification of a cancer, such as an ovarian cancer or a breast cancer.

Abstract: A method for identifying a mutational signature may include receiving one or more digital images into electronic storage for at least one patient, identifying one or more neoplasms in each received digital image, extracting one or more visual features from each identified neoplasm, and applying a trained machine learning system to identify a mutational signature ratio vector for the one or more extracted visual features.

Abstract: A method, apparatus, system, and computer program product for managing pumps in an aircraft. Flight information about an operation of the pumps in a pump package in the aircraft is received by a computer system. The flight information is received from the aircraft. A number of times that an abnormal switching occurred for the pumps within a window of consecutive flights is determined by the computer system when the abnormal switching is identified from the flight information. A set of actions is performed by the computer system when the abnormal switching occurred a number of times for the pumps within the window of consecutive flights that exceeds a set of thresholds for the abnormal switching that is considered healthy for the pumps.

Abstract: The present invention relates, in part, to methods for the stratification, prognosis, diagnosis and stratification of a cancer, such as an ovarian cancer or a breast cancer.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each of a plurality of systems, including maintenance data and operational data. The instructions are further executable to display one or more algorithm templates and one or more data features calculated from the operational data, in a graphical user interface. The instructions are further executable to receive a selection of an algorithm template, a data feature, and a value of a parameter associated with the algorithm template, and to train and evaluate the selected algorithm template on the selected data feature according to the received value. The instructions are further executable to display a result of a metric of the evaluation, and generate a predictive maintenance model using the selected algorithm template.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory including one or more digital storage devices, and a plurality of instructions stored in the memory. The instructions are executable by the one or more processors to receive a historical dataset relating to each system of a plurality of systems, and including maintenance data and operational data. The instructions are further executable to receive a first selection of a first operational data feature and a first system, and display operational data associated with the first operational data feature and the first system, and maintenance data associated with the first system, on a timeline in a graphical user interface. The instructions are further executable to receive a second selection of a second operational data feature and generate a predictive maintenance model using the second operational data feature according to a machine learning method.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including maintenance data and operational data. The instructions are further executable to evaluate correlation between each of a plurality of operational data features and maintenance events of the maintenance data, using a supervised machine learning classification model. The instructions are further executable to display a quantitative result of the evaluation for each operational data feature in a graphical user interface, receive a selection of one or more operational data features of the plurality of operational data features, and generate a predictive maintenance model using the selected one or more operational data features according to a machine learning method.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory including one or more digital storage devices, and a plurality of instructions stored in the memory. The instructions are executable by the one or more processors to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including maintenance data and operational data. The instructions are further executable to receive a rule set for processing a first attribute of the operation data and calculate a custom data feature from the historical dataset according to the received rule set. The instructions are further executable to generate a predictive maintenance model, using the custom data feature according to a machine learning method.

Abstract: A method, apparatus, system, and computer program product for managing pumps in an aircraft. Flight information about an operation of the pumps in a pump package in the aircraft is received by a computer system. The flight information is received from the aircraft. A number of times that an abnormal switching occurred for the pumps within a window of consecutive flights is determined by the computer system when the abnormal switching is identified from the flight information. A set of actions is performed by the compute system when the abnormal switching occurred a number of times for the pumps within the window of consecutive flights that exceeds a set of thresholds for the abnormal switching that is considered healthy for the pumps.

Abstract: The present invention relates, in part, to methods for the stratification, prognosis, diagnosis and stratification of a cancer, such as an ovarian cancer or a breast cancer.