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 method for generating a chart suggestion list using data present in a data table includes providing the data table, one or more data types of a plurality of cells of the data table and one or more statistics pertaining to the plurality of cells as input to a machine learning model to obtain one or more outputs indicating one or more chart types for the data table, and creating one or more suggested charts based on the indicated chart types. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.

Abstract: A method for generating a chart suggestion list using data present in a data table includes providing the data table, one or more data types of a plurality of cells of the data table and one or more statistics pertaining to the plurality of cells as input to a machine learning model to obtain one or more outputs indicating one or more chart types for the data table, and creating one or more suggested charts based on the indicated chart types. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.

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 method for generating a chart suggestion list using data present in a data table includes providing the data table, one or more data types of a plurality of cells of the data table and one or more statistics pertaining to the plurality of cells as input to a machine learning model to obtain one or more outputs indicating one or more chart types for the data table, and creating one or more suggested charts based on the indicated chart types. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.

Abstract: A method for generating a chart suggestion list using data present in a data table includes parsing cells of the data table to identify one or more columns and one or more data types of the cells, performing a statistical analysis of the one or more columns to obtain one or more statistics about the one or more columns, and creating one or more suggested charts based on the one or more statistics about the one or more columns and the one or more data types of the cells. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.

Abstract: Processes for converting existing refinery units and equipment to enable processing of renewable triglyceride feedstock to provide hydrocarbon fuels. Originally, the existing refinery units may have bene configured as hydrotreating, hydrocracking, fixed bed reforming, or isomerization units for a petroleum based feedstock. Hydrogen from a second reaction zone may be provide to the first reaction zone, without or without the use of a compressor.

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: Processes for converting existing refinery units and equipment to enable processing of renewable triglyceride feedstock to provide hydrocarbon fuels. Originally, the existing refinery units may have bene configured as hydrotreating, hydrocracking, fixed bed reforming, or isomerization units for a petroleum based feedstock. Hydrogen from a second reaction zone may be provide to the first reaction zone, without or without the use of a compressor.

Abstract: A method for generating a chart suggestion list using data present in a data table includes parsing cells of the data table to identify one or more columns and one or more data types of the cells, performing a statistical analysis of the one or more columns to obtain one or more statistics about the one or more columns, and creating one or more suggested charts based on the one or more statistics about the one or more columns and the one or more data types of the cells. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.