Abstract: A system for disease prediction includes processing circuitry configured to receive a dataset including data of a pa -tient population, the data including for each of a plurality of patients of the patient population, values for a plurality of features, and a diagnosis value indicating whether a disease has been diagnosed. The processing circuitry is configured to, based on correlations between the values, select from the dataset a plurality of subsets of the features, and, for each of at least one of the subsets, execute a machine learning process with the respective subset and the diagnosis values as input parameters, the execution generating a respective prediction model. The processing circuitry is configured to output the respective prediction model.

Abstract: Systems and methods are disclosed for predicting values for health parameters traditionally sampled at higher frequencies (such as vital signs) and at lower frequencies (such as lab results). The higher frequency parameter predictions can take the form of a time series of predicted vital sign values and the lower frequency parameter predictions can be single predicted values based on statistical measures of past collected input features. Suitable statistical measures can include the maximum, minimum and median values.

Abstract: 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.