Application US20190259499 AI Biotech/Diagnostics: General Diagnostics

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Peach Intellihealth, Inc.

Application US20190259499

Published 2019-08-22

System And Method For Predicting Sequential Organ Failure Assessment (sofa) Scores Using Artificial Intelligence And Machine Learning

Various aspects of the subject technology related to systems and methods for predicting sequential organ failure assessment (SOFA) scores using machine learning. A system may be configured to receive patient data including one or more features associated with one or more patients. The system may process the features using one or more SOFA score prediction models derived from at least one machine learning process to output respective predicted SOFA scores. One of the prediction models has been trained to output a first SOFA component score for a first amount of time into the future and a second prediction model has been trained to output a second SOFA component score for the first amount of time into the future. The system may output on a graphical user interface, a total SOFA score, the first SOFA component score, and the second SOFA components score predicted for the respective patient.

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5 Independent Claims

1. A computer-implemented method for predicting sequential organ failure assessment (SOFA) scores using machine learning, the method comprising: receiving patient data, the patient data including a plurality features associated with each of one or more patients; processing the plurality of features for each patient using a plurality of SOFA score prediction models derived from at least one machine learning process to output a plurality of respective predicted SOFA scores, wherein a first of the prediction models has been trained to output a first SOFA component score for a first amount of time into the future and a second of the prediction models has been trained to output a second SOFA component score for the first amount of time into the future; and outputting on a graphical user interface, for each of the patients, a total SOFA score and at least one of the first SOFA component score and the second SOFA component score predicted for the respective patient.
11. A system for predicting sequential organ failure assessment (SOFA) scores using machine learning, the system comprising: a memory storing computer-readable instructions and a plurality of SOFA score prediction models; and a processor, the processor configured to execute the computer-readable instructions, which when executed carry out the method comprising: receiving patient data, the patient data including a plurality features associated with one or more patients; processing the plurality of features for each patient using a plurality of SOFA score prediction models derived from at least one machine learning process to output a plurality of respective predicted SOFA scores, wherein a first of the prediction models has been trained to output a first SOFA component score for a first amount of time into the future and a second of the prediction models has been trained to output a second SOFA component score for the first amount of time into the future; and outputting on a graphical user interface, for each of the patients, a total SOFA score and at least the first SOFA component score and the second SOFA component score predicted for the respective patient.
21. A system for predicting a total sequential organ failure assessment (SOFA) score, the system comprising: a memory storing computer-readable instructions and a total SOFA score prediction model; and a processor, the processor configured to execute the computer-readable instructions, which when executed carry out the method comprising: receiving patient data, the patient data including a plurality features associated with each of one or more patients; processing the plurality of features for each patient using a total SOFA score prediction model derived from at least one machine learning process to output a predicted total SOFA score for the patient for a first amount of time into the future, wherein the total SOFA score prediction model takes as input the patient's current values of at least three physiological parameters, including a Braden Score and at least two of Glasgow Coma Scale, platelet level, and creatinine level; and outputting on a graphical user interface, for each of the patients, the total SOFA score predicted for the respective patients for the first amount of time into the future.
31. A computer-implemented method for predicting a total sequential organ failure assessment (SOFA) score, the method comprising: receiving patient data, the patient data including a plurality features associated with each of one or more patients; processing the plurality of features for each patient using a total SOFA score prediction model derived from at least one machine learning process to output a predicted total SOFA score for the patient for a first amount of time into the future, wherein the total SOFA score prediction model takes as input the patient's current values of at least three physiological parameters, including a Braden Score and at least two of Glasgow Coma Scale, platelet level, and creatinine level; and outputting on a graphical user interface, for each of the patients, the total SOFA score predicted for the respective patients for the first amount of time into the future.
41. A computer readable storage medium containing program instructions for causing a computer to predict sequential organ failure assessment (SOFA) scores using machine learning performed by the method of: receiving patient data, the patient data including a plurality features associated with one or more patients; processing the plurality of features for each patient using a plurality of SOFA score prediction models derived from at least one machine learning process to output a plurality of respective predicted SOFA scores, wherein a first of the prediction models has been trained to output a first SOFA component score for a first amount of time into the future and a second of the prediction models has been trained to output a second SOFA component score for the first amount of time into the future; and outputting on a graphical user interface, for each of the patients, a total SOFA score and at least the first SOFA component score and the second SOFA component score predicted for the respective patient.