Abstract: A system includes first, second and third input data sets. The first input data set includes demographic information characterizing a patient. The second and third input data sets characterize a healthcare treatment history of the patient. A neural network includes first, second and third neural subnetworks. The first neural subnetwork is configured to process the first input data set to produce a first output data set. The second neural subnetwork is configured to process the second input data set to produce a second output data set. The third neural subnetwork is configured to process the third input data set to produce a third output data set. An autoencoder layer has an input layer comprising the first, second and third output data sets and is configured to process the first, second and third output data sets to produce a secondary output data set.

Abstract: A unified framework for healthcare workflows to introduce multiple integrated approaches to error analysis. A first approach uses machine learning to extend probabilistic record linkage and apply it to the task of reconciliation, classifying changes between datasets as intentional or unintentional. A second approach uses process mining to extract maximum information about process diagrams and process bottlenecks.

Abstract: A system includes first, second and third input data sets. The first input data set includes demographic information characterizing a patient. The second and third input data sets characterize a healthcare treatment history of the patient. A neural network includes first, second and third neural subnetworks. The first neural subnetwork is configured to process the first input data set to produce a first output data set. The second neural subnetwork is configured to process the second input data set to produce a second output data set. The third neural subnetwork is configured to process the third input data set to produce a third output data set. An autoencoder layer has an input layer comprising the first, second and third output data sets and is configured to process the first, second and third output data sets to produce a secondary output data set.

Abstract: A unified framework for healthcare workflows to introduce multiple integrated approaches to error analysis. A first approach uses machine learning to extend probabilistic record linkage and apply it to the task of reconciliation, classifying changes between datasets as intentional or unintentional. A second approach uses process mining to extract maximum information about process diagrams and process bottlenecks.