Abstract: A system and method for predicting resource usage using machine-learning models. The method entails collecting a dataset from a variety of data sources such as electronic medical/health records or medical registries. The method identifies if an outreach communication occurred or is scheduled to occur for a subject. A set of features are extracted from the dataset and the method generates derived features from one or more extracted features. The extracted features and generated set of derived features are collated into a candidate feature vector used for training the machine-learning models. The models generate a predicted likelihood of the subject seeking care at the medical facility within a defined time period. Based on the predicted likelihoods of the subjects seeking care at the medical facility, the method predicts an upcoming resource demand at the medical facility. The method generates a recommended action in case predicted resource demand exceeds a threshold.

Abstract: Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method generating a feature for a machine learning model to use to forecast a time interval between receipt of first data at a staging area of a data warehouse and receipt of the first data at a target database of the data warehouse based at least in part on second data received from the staging area and third data received from the target database. The method can further include generating, using the machine learning model, a forecasted time interval based at least in part on the feature. The method can further include comparing the forecasted time interval with an expected time interval for fourth data received at the staging area. The method can further include updating a priority of the first data based at least in part on the comparison. The method can further include transmitting the first data to the target database based at least in part on the updated priority.

Abstract: Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method includes a device receiving historical data from a staging area and a target database of a data warehouse. The device can generate a feature for a machine learning model based at least in part on the historical data. The device can generate a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database. The device can compare the forecasted time interval with an expected time interval. The device can generate a work ticket based at least in part on the comparison.

Abstract: Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method includes a device receiving historical data from a staging area and a target database of a data warehouse. The device can generate a feature for a machine learning model based at least in part on the historical data. The device can generate a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database. The device can compare the forecasted time interval with an expected time interval. The device can generate a work ticket based at least in part on the comparison.

Abstract: Methods, systems, and computer-readable media are disclosed that the likelihood that a medical order for multiple pharmaceutical drugs may be stolen or diverted. Generally, a current data set for a medical order for pharmaceutical drugs is received. Each of the drugs is associated with a set of features. An impact score is generated for each of the features for each drug based on historical effects. Test data is also used to evaluate the diversion prediction accuracy of a plurality of machine learning models, when compared to historical diversion data for the drugs. The most accurate machine learning model is utilized to make a diversion probability prediction for those features having the highest impact scores, for the drugs in the medical order. A recommended action is generated and provided based on the diversion probability.