Machine Learning Systems and Methods for Assessing Medical Interventions for Utilization Review

Abstract

Systems and methods are disclosed for determining the appropriateness of medical interventions. In one embodiment, a machine learning system for determining the appropriateness of a selected medical intervention includes health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type. A machine learning module is configured to receive the first and second data files, perform a normalization procedure on at least one of the first and second data files, and apply at least one previously trained machine learning model to the normalized data files to produce a prediction output. The prediction output may include a confidence level associated with an appropriateness of the selected medical intervention.

1 . A machine learning system for determining the appropriateness of a selected medical intervention, the system comprising:
 a plurality of health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type;   a normalization module configured to receive the first and second data files and perform a normalization procedure on at least one of the first and second data files; and   a previously trained machine learning model configured to receive the normalized data files and produce a prediction output, wherein the prediction output includes a confidence level associated with an appropriateness of the selected medical intervention.   
     
     
         2 . The machine learning system of  claim 1 , wherein the at least one machine learning model is an artificial neural network 
     
     
         3 . The machine learning system of  claim 1 , wherein the at least one machine learning model is a probabilistic neural network. 
     
     
         4 . The machine learning system of  claim 1 , wherein the at least one machine learning model is a convolutional neural network. 
     
     
         5 . The machine learning system of  claim 1 , wherein the at least one machine learning model is a decision tree. 
     
     
         6 . The machine learning system of  claim 1 , wherein the first data file is a two-dimensional image file, and the normalization procedure includes producing an input vector based on the two-dimensional image file. 
     
     
         7 . The machine learning system of  claim 6 , wherein the two-dimensional image file is selected from the group comprising an X-ray image, a cat-scan (CT) image, and a magnetic resonance image (MRI). 
     
     
         8 . The machine learning system of  claim 1 , wherein the first data file is a time-varying real value parameter, and the normalization procedure produces an input vector based on the time-varying real value parameter. 
     
     
         9 . The machine learning system of  claim 8 , wherein the time-varying real value parameter is a heart-beat audio file. 
     
     
         10 . The machine learning system of  claim 8 , wherein the time-varying real parameter is a spoken utterance. 
     
     
         11 . The machine learning system of  claim 1 , wherein the first data file is a text file, and the normalization procedure includes producing an input vector by applying natural language processing (NLP) to the text file. 
     
     
         12 . The machine learning system of  claim 1 , wherein the prediction output is further processed to determine a selected health-care provider for the selected medical intervention. 
     
     
         13 . The machine learning system of  claim 1 , wherein the data sources are selected from the group consisting of diagnostic image sources, radiological reports, lab studies, exam findings, survey results, and office notes. 
     
     
         14 . A method for determining the appropriateness of a selected medical intervention utilizing a machine learning system, the method comprising:
 receiving, from a plurality of health-related data sources, at least one data file of a first type, and a second data file of a second type;   performing a normalization procedure on at least one of the first and second data files; and   applying at least one previously trained machine learning model to the normalized data files to produce a prediction output; wherein the prediction output includes a confidence level associated with an appropriateness of the selected medical intervention.   
     
     
         15 . The method of  claim 14 , wherein the at least one machine learning model is an artificial neural network. 
     
     
         16 . The method of  claim 14 , wherein the at least one machine learning model is a probabilistic neural network. 
     
     
         17 . The method of  claim 14 , wherein the at least one machine learning model is a convolutional neural network. 
     
     
         18 . The method of  claim 14 , wherein the at least one machine learning model is a decision tree. 
     
     
         19 . The method of  claim 14 , wherein the first data file is a two-dimensional image file, and the normalization procedure includes producing an input vector based on the two-dimensional image file. 
     
     
         20 . The method of  claim 19 , wherein the two-dimensional image file is selected from the group comprising an X-ray image, a cat-scan (CT) image, and a magnetic resonance image (MRI).