# US 11,443,654 B2 · Dynamic injection of medical training scenarios based on patient similarity cohort identification

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- Number: US 11,443,654 B2
- Document ID: US11443654B2
- Status: Granted
- Kind: B2
- Application number: 16/522,812
- Filed: 2019-07-26
- Published: 2020-08-27
- Granted: 2022-09-13
- Earliest claimed: 2019-02-27
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Robert H. Grant; Maruthi P. Koiloth; Daniel Ruiz; Zachary Silverstein
- Web page: https://patentgrep.com/patent/US11443654B2

## CPC

- G09B 23/285: {for injections, endoscopy, bronchoscopy, sigmoidscopy, insertion of contraceptive devices or enemas}
- A61B 34/10: Computer-aided planning, simulation or modelling of surgical operations
- B33Y 50/00: Data acquisition or data processing for additive manufacturing
- G06N 3/09: Supervised learning
- G09B 23/30: Anatomical models {(G09B23/281 - G09B23/288 take precedence)}
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 2034/101: {Computer-aided simulation of surgical operations}
- A61B 2034/105: {Modelling of the patient, e.g. for ligaments or bones}
- A61B 2090/365: {augmented reality, i.e. correlating a live optical image with another image}
- B33Y 80/00: Products made by additive manufacturing
- G06N 3/045: Combinations of networks
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G06T 2210/41: Medical
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- B33Y: ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

Disclosed embodiments provide a computer-implemented technique for selection and/or modification of surgical simulation scenarios based on patient similarity cohort identification. Prior to undergoing surgery, medical information of a patient, including genomic, physiological, and/or environmental data, is used to identify a patient cohort. The patient cohort represents a statistically significant sample size of similar patients, and complications that may have arose during similar surgical procedures to the procedure planned for the patient. Relevant scenarios are identified based on the patient cohort. These scenarios are then input to a surgical simulation system. The surgical simulation system may be implemented by virtual reality, augmented reality, and/or physical workpieces used for surgical practice and training. This can increase the probability of a successful outcome for the patient, and furthermore save costs by reducing the risk of malpractice lawsuits, thereby potentially lowering overall healthcare costs.

## Claims

### Claim 1

A computer-implemented method, comprising:
retrieving a medical information record for a patient from a patient medical database:
receiving a procedure request for a patient;
identifying, by a machine learning system, a cohort for the patient based on the medical information record:
identifying, by the machine learning system, a cohort risk factor that is related to the procedure request based on the identified cohort:
determining a probability of occurrence for the cohort risk factor;
automatically altering a surgical simulation corresponding to the procedure request to include an occurrence of the cohort risk factor in response to a probability of occurrence exceeding a predetermined threshold;
executing a surgical simulation scenario that performs an interactive simulation of the procedure that is adapted to the patient by including the altered surgical simulation, wherein the surgical simulation scenario is performed by a medical professional in preparation for the procedure;
updating the patient medical database with an outcome of a real-world surgery corresponding to the procedure request;
incorporating the outcome into future identifications by the machine learning system,
making a customized workplace in which the surgical simulation scenario is performed using a 3D printing process; and
altering the surgical simulation by generating a physical element of the customized workplace that has characteristics that are specific to the identified cohort by controlling the 3D printing process using parameters pertaining to the characteristics.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein receiving a medical information record comprises receiving genotypic information.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein receiving a medical information record comprises receiving physiological information.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein identifying a cohort comprises utilizing a Random Forest process.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein identifying a cohort comprises performing a geographic correlation technique.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, further comprising:
determining a severity for the cohort risk factor; and
altering the surgical simulation corresponding to the procedure request in response to a severity exceeding a predetermined level.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein identifying a cohort for the patient is based on abnormal values.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1, wherein altering the surgical simulation comprises requesting an organ failure scenario.

### Claim 9 (depends on 1)

The computer-implemented method of claim 1, wherein controlling the 3D printing process includes printing a leg model that includes compartment syndrome characteristics.