Abstract: A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.

Abstract: A machine learning system is provided for training a data model to predict data states. The machine learning server is configured to receive a first portion of historical pharmaceutical data. The machine learning server is configured to apply a deep learning variable importance method to the first portion to identify at least one salient variable. The machine learning server is also configured to apply the model generation algorithm to the first portion and the at least one salient variable to generate predictive models for the forecast of the target variable. The machine learning server is also configured to receive a second portion of the historical pharmaceutical data to test the predictive models. The machine learning server is also configured to obtain a portion of current pharmaceutical data and apply the portion of current pharmaceutical data to the candidate predictive model to obtain the forecast of the target variable.

Abstract: A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.

Abstract: A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.