# US 2019/0378618 A1 · Machine Learning Systems For Surgery Prediction and Insurer Utilization Review

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- Number: US 2019/0378618 A1
- Document ID: US20190378618A1
- Status: Published application
- Kind: A1
- Application number: 16/004,205
- Filed: 2018-06-08
- Published: 2019-12-12
- Earliest claimed: 2018-06-08
- Inventors: Daniel M. Lieberman
- Web page: https://patentgrep.com/patent/US20190378618A1

## CPC

- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 3/09: Supervised learning
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 5/04: Inference or reasoning models
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- G06Q 40/08: Insurance
- G16H 10/20: for electronic clinical trials or questionnaires
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Systems and methods are disclosed for surgery prediction. One method includes receiving, from a patient interacting with a survey user interface, a set of survey results, then applying at least one previously trained machine learning model (e.g., one or more artificial neural networks) to the survey results to generate a prediction output. The prediction output includes (i) a first confidence level associated with whether the patient is a surgical candidate for a particular surgical procedure; and, optionally, (ii) a set of second confidence levels associated with a respective set of surgical outcomes. Such systems and methods may be used, for example, by surgeons, health care providers, and insurers performing utilization review.

## Claims

### Claim 1

A machine learning system for surgical prediction, the system comprising:
a survey module configured to generate a survey user interface and receive, from a patient interacting with the survey user interface, a set of survey results;
a machine learning module configured to receive the survey results, apply at least one previously trained machine learning model to the survey results, and produce a prediction output;
wherein the prediction output includes a first confidence level associated with whether the patient is a surgical candidate for a proposed surgical procedure.

### Claim 2 (depends on 1)

The system of claim 1, wherein the prediction output further includes a set of second confidence levels associated with a respective set of surgical outcomes.

### Claim 3 (depends on 1)

The machine learning system of claim 1, wherein the machine learning module is configured to further receive, and consider in producing the prediction output, at least one of: medical images, past medical history, lab reports, radiology reports.

### Claim 4 (depends on 1)

The machine learning system of claim 1, wherein the at least one previously trained machine learning model includes:
a first machine learning model configured to receive a first survey input comprising a first subset of the survey results; and
a second machine learning model configured to receive a second survey input comprising a first subset of the survey results;

### Claim 5 (depends on 4)

The machine learning system of claim 4, wherein the first machine learning model is a shallow artificial neural network and the second machine learning model is a probabilistic neural network.

### Claim 6 (depends on 4)

The machine learning system of claim 4, wherein the first confidence level is produced by the first machine learning model, and the set of second confidence levels associated with the respective set of surgical outcomes is produced by a combination of an intermediate output of the second machine learning model and the first confidence level.

### Claim 6 (depends on 1)

The machine learning system of claim 1, wherein the set of surgical outcomes are associated with spine surgical procedures.

### Claim 7 (depends on 1)

The machine learning system of claim 1, wherein:
the survey user interface is configured to receive, in response to at least one survey question, a text input, and;
the survey module is configured to convert the text input, via natural language processing, to a numerical value.

### Claim 8

A method for performing insurer utilization review comprising:
receiving, at an insurer system, a preauthorization request associated with a patient and a requested treatment;
receiving, from the patient, a set of survey results; and
applying at least one previously trained machine learning model to the survey results to generate a prediction output, wherein the prediction output includes a first confidence level associated with whether the patient is a surgical candidate for a surgical procedure;
selectably denying or approving the preauthorization request based on the requested treatment and the prediction output.

### Claim 9 (depends on 8)

The method of claim 8, wherein applying the at least one previously trained machine learning model to the survey results includes:
applying a first subset of the survey results to a first machine learning model; and
applying a second subset of the survey results to a second machine learning model;
wherein the first machine learning model is a shallow artificial neural network and the second machine learning model is a probabilistic neural network.

### Claim 10 (depends on 9)

The method of claim 9, wherein:
the prediction output further includes a set of second confidence levels associated with a respective set of surgical outcomes;
the first confidence level is produced by the first machine learning model; and
the set of second confidence levels associated with the respective set of surgical outcomes is produced by a combination of an intermediate output of the second machine learning model and the first confidence level.

### Claim 11 (depends on 9)

The method of claim 9, wherein the set of surgical outcomes are associated with spine surgical procedures.

### Claim 12 (depends on 11)

The method of claim 11, wherein the spine surgical procedures include laminectomy, direct visual rhizotomy, and microdiscectomy.

### Claim 13 (depends on 8)

The method of claim 8, wherein:
the survey user interface is configured to receive, in response to at least one survey question, a text input, and;
the survey module is configured to convert the text input, via natural language processing, to a numerical value.

### Claim 14

A method for surgical prediction, the method comprising:
training at least one machine learning model based on previously performed surgical procedures;
generating a survey user interface;
receiving, from a patient interacting with the survey user interface, a set of survey results;
applying the at least one previously trained machine learning model to the survey results to produce a prediction output that includes (i) a first confidence level associated with whether the patient is a surgical candidate; and (ii) a set of second confidence levels associated with a respective set of surgical outcomes.

### Claim 15 (depends on 14)

The method of claim 14, wherein the at least one previously trained machine learning model includes:
a first machine learning model configured to receive a first survey input comprising a first subset of the survey results; and
a second machine learning model configured to receive a second survey input comprising a first subset of the survey results;

### Claim 16 (depends on 15)

The machine learning system of claim 15, wherein the first machine learning model is a shallow artificial neural network and the second machine learning model is a probabilistic neural network.

### Claim 17 (depends on 15)

The method of claim 15, wherein the first confidence level is produced by the first machine learning model, and the set of second confidence levels associated with the respective set of surgical outcomes is produced by a combination of an intermediate output of the second machine learning model and the first confidence level.

### Claim 18 (depends on 14)

The method of claim 14, wherein the set of surgical outcomes are related to spine surgical procedures.

### Claim 19 (depends on 18)

The method of claim 18, wherein the spine surgical procedures include laminectomy, direct visual rhizotomy, and microdiscectomy.

### Claim 20 (depends on 14)

The method of claim 14, wherein:
the survey user interface is configured to receive, in response to at least one survey question, a text input, and;
the survey module is configured to convert the text input, via natural language processing, to a numerical value.