Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using a recurrent neural network. In particular, at each time step, a network input for the time step is processed using a recurrent neural network to update a hidden state of the recurrent neural network. Specifically, the hidden state of the recurrent neural network is partitioned into a plurality of partitions and the plurality of partitions comprises a respective partition for each of a plurality of possible observational features.

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: The inventive subject matter relates to an apparatus, systems, methods, and computer program products relating to tracking and managing vehicle assets. More particularly, the invention relates, preferably, to using wireless systems to track and manage vehicle assets, vehicle inventory, parts inventory, parts and/or equipment installed on said asset, a vehicle operator, a vehicle owner, or a combination thereof.

Abstract: A mounting pad for transporting and anchoring different sizes of electrical distribution transformers has a pad top surface with a plurality of sidewalls extending downwardly therefrom and a cellular subframework positioned below the pad top surface and within the plurality of sidewalls. The pad top surface has an access opening formed therein. A base flange extends outwardly from a bottom of the plurality of sidewalls. Forklifting slots having abutment surfaces are formed through a sidewall and into the subframework. The subframework has a plurality of ribs extending therein. Solid blocks are positioned in the subframework so as to allow the transformer to be secured thereto with helical screws.