Abstract: The invention relates to a device for storing and supplying fluid, comprising a cryogenic tank, a withdrawal circuit comprising a first withdrawal line comprising a first heat exchanger located outside the tank, the withdrawal circuit comprising a set of one or more valves permitting withdrawal, in the process entering the first heat exchanger, an electronic control member for making at least some of the set of valves open/close, when the set of valves is configured to not allow withdrawal, the electronic control member being configured to switch the set of valves to a first state, in which at least a portion of the internal volume of the first heating heat exchanger is fluidically isolated from at least a portion of the rest of the withdrawal circuit, or a second state, in which at least a portion of the volume of the heating heat exchanger fluidically communicates with at least a portion of the rest of the withdrawal circuit.

Abstract: The invention relates to a device for storing and supplying fluid, comprising a cryogenic tank, a withdrawal circuit comprising a first withdrawal line comprising a first heat exchanger located outside the tank, the withdrawal circuit comprising a set of one or more valves permitting withdrawal, in the process entering the first heat exchanger, an electronic control member for making at least some of the set of valves open/close, when the set of valves is configured to not allow withdrawal, the electronic control member being configured to switch the set of valves to a first state, in which at least a portion of the internal volume of the first heating heat exchanger is fluidically isolated from at least a portion of the rest of the withdrawal circuit, or a second state, in which at least a portion of the volume of the heating heat exchanger fluidically communicates with at least a portion of the rest of the withdrawal circuit.

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.