Abstract: A method (100) for determining a baseline creatinine value for a subject, comprising: obtaining (130) a set of features about the subject; analyzing (140), using a trained baseline creatinine determination model, the obtained set of features to generate a baseline creatinine value for the subject; reporting (150), via a user interface, the generated baseline creatinine value for the subject.

Abstract: A machine learning model, including: a categorical input feature, having a defined set of values; a plurality of non-categorical input features; a word embedding layer configured to convert the categorical input feature into an output in a word space having two dimensions; and a machine learning network configured to receive the output of the word embedding layer and the plurality of non-categorical input features and to produce a machine learning model output.

Abstract: Methods and systems for determining the blood pressure of a patient. The system may include a first device configured to collect a first plurality of blood pressure measurements of the patient, a second device configured to collect a second plurality of blood pressure measurements of the patient, and a processor configured to identify a divergence between the first plurality and the second plurality, retrieve, from a memory, a clinical event, compare the first plurality and the second plurality to the clinical event, and determine that the first plurality is more accurate than the second plurality based on the comparison.

Abstract: A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.

Abstract: Techniques are described herein for using time series data such as vital signs data and laboratory data or other time series data as input across machine learning models to predict a change in stage of a medical condition of a patient. In various embodiments, patient data comprising vital signs data of a patient and laboratory data or other time series data of the patient corresponding to an observation window may be received. A time series model may be used to predict a change in stage of a medical condition in the patient in a prediction window based on the patient data. The predicted change in stage of the medical condition may be output.

Abstract: A machine learning model, including: a categorical input feature, having a defined set of values; a plurality of non-categorical input features; a word embedding layer configured to convert the categorical input feature into an output in a word space having two dimensions; and a machine learning network configured to receive the output of the word embedding layer and the plurality of non-categorical input features and to produce a machine learning model output.

Abstract: A method and system method for determining a patient's acute kidney injury (AKI) stage, including: receiving a patient's current AKI stage; calculating a patient's new AKI stage; comparing the new AKI stage to the current AKI stage; updating the patient's AKI stage to the new AKI stage when the new AKI stage is greater than the current AKI stage; calculating AKI stage exit criteria and an AKI exit stage value; determining whether the AKI stage exit criteria are satisfied; and reducing the patient's AKI stage to the exit AKI stage when the AKI stage exit criteria are satisfied.

Abstract: A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.