Abstract: A system for adapting a machine learning model for a specific population. The system includes a processor and memory devices storing instructions that configure the memory devices to perform operations. The operations may include receive a local dataset comprising local records of patients associated with the healthcare facility, perform a clustering function, and retrieving a template dataset comprising template records organized in clusters with variable centroids. The operations may also include calculating a similarity metric between the local and template records, generating a synthetic dataset by combining template and local records, segregating the synthetic dataset into a training synthetic dataset and a testing synthetic dataset, and generating and/or validating a machine learning predictive model by tuning a template model according to the training synthetic dataset and/or generating a new predictive model.

Abstract: A method for making dynamic risk predictions is provided. The method includes receiving a dataset with a first data field and a second data field. The first data field is populated with a measured value. The method also includes imputing a first predicted value to the second data field, generating a first risk score and a first set of associated metrics based on the measured value and the first predicted value, and imputing a second predicted value to the second data field. The method also includes calculating a statistically derived metric and determining whether the statistically derived metric exceeds a predetermined threshold, wherein a predetermined action is recommended if the statistically derived metric exceeds the predetermined threshold. A system and a non-transitory, computer readable medium storing instructions to cause the system to perform the above method are also provided.

Abstract: A method for ranking an unmeasured feature for an instance given at least one feature is measured is provided. The method includes imputing a first value to the unmeasured feature in the instance while holding the other remaining unmeasured features constant and evaluating a first outcome with a model using the first value in the instance. The method includes imputing a second value to the unmeasured feature in the dataset while holding the other remaining unmeasured features constant, evaluating a second outcome with the model using the second value in the instance, and determining a statistical parameter with the first outcome and the second outcome. The method also includes assigning the unmeasured feature a ranking corresponding to the determined statistical parameter. A system and a non-transitory, computer readable medium storing instructions to perform the above method are also presented.