Machine learning systems and methods for assessing medical interventions for utilization review

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, including a processor, 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, via the processor, produce a prediction output, wherein the prediction output includes a confidence level associated with an appropriateness of the selected medical intervention, wherein the previously trained machine learning model is trained based on a population of patients that have previously undergone the selected medical intervention, wherein the training utilizes, as a training parameter, a jury-produced determination of the appropriateness, in the form of a numeric appropriateness value, of the selected medical intervention, and the medical intervention corresponds to a form of surgery;

wherein the prediction output is used during an insurance utilization review to produce a Boolean determination of whether to perform the selected medical intervention on a particular patient.

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, with a processor, 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, wherein the previously trained machine learning model is trained based on a population of patients that have previously undergone the selected medical intervention, wherein the training utilizes, as a training parameter, a jury-produced determination of the appropriateness, in the form of a numeric appropriateness value, of the selected medical intervention, and the medical intervention corresponds to a form of surgery;

using the prediction output during an insurance utilization review to produce a Boolean determination of whether to perform the selected medical intervention on a particular patient.

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).