Methods for automatically determining injury treatment relation to a motor vehicle accident and devices thereof

1. A method for automatically determining injury treatment relation to a motor vehicle accident, the method comprising:

obtaining, by an insurance claim analysis device, a plurality of images of a damaged motor vehicle involved in a motor vehicle accident, vehicle data describing the motor vehicle, occupant data describing an occupant who occupied the motor vehicle during the motor vehicle accident, and injury data from an electronic insurance claim associated with the motor vehicle accident, the injury data specifying an injury to the occupant;

determining, by the insurance claim analysis device, a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the plurality of images of the damaged motor vehicle and the vehicle data, wherein the first machine learning model has been trained using historical sets of images and vehicle data along with delta velocity values obtained from corresponding historical sets of images and vehicle data;

determining, by the insurance claim analysis device, an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data, wherein the second machine learning model has been trained using historical sets of delta velocity values, vehicle data, and occupant data, along with injury severity scores obtained from corresponding historical sets of delta velocity values, vehicle data, and occupant data;

identifying, by the insurance claim analysis device, a first set of one or more condition indications based on a correlation of the injury severity score with a stored mapping of condition indications to injury severity scores;

comparing, by the insurance claim analysis device, one or more of the condition indications in the first set of condition indications to one or more condition indications in a second set of condition indications in the injury data;

generating, by the insurance claim analysis device, a likelihood value based on the comparing, the likelihood value indicating a likelihood that the injury to the occupant resulted from the motor vehicle accident;

automatically adjudicating, by the insurance claim analysis device, the electronic insurance claim based on the generated likelihood value;

obtaining, by the insurance claim analysis device, further injury data from a medical bill of the occupant;

comparing, by the insurance claim analysis device, one or more of the condition indications in the first set of condition indications to one or more condition indications in a third set of condition indications in the further injury data;

generating, by the insurance claim analysis device, a further likelihood value based on the comparing, the further likelihood value indicating a further likelihood that the injury to the occupant resulted from the motor vehicle accident;

automatically adjudicating, by the insurance claim analysis device, the electronic insurance claim based on the generated further likelihood value;

providing the adjudicated electronic insurance claim to a claims adjusters;

retraining the first machine learning model using the delta velocity value determined using the first machine learning model; and

retraining the second machine learning model using the injury severity score determined using the second machine learning model.

2. The method of claim 1 , further comprising:

providing, by the insurance claim analysis device, a graphical user interface (GUI) comprising a plurality of display elements, wherein a first one of the display elements represents the generated likelihood value, and wherein a second one of the display elements is operable by a user to select whether the injury should be considered in the adjudication of the insurance claim; and

receiving, by the insurance claim analysis device and via the GUI, a selection regarding whether the injury should be considered in the adjudication of the insurance claim.

3. The method of claim 1 , wherein the condition indications comprise International Statistical Classification of Diseases and Related Health Problems (ICD) codes and the injury severity score comprises an Abbreviated Injury Scale (AIS) score.

4. The method of claim 1 , wherein the vehicle data comprises one or more of a type of the motor vehicle, an age of the motor vehicle, a size of the motor vehicle, a weight of the motor vehicle, an area of impact on the motor vehicle, a damage extent, one or more crush measurements, or whether the motor vehicle was drivable subsequent to the motor vehicle accident.

5. The method of claim 1 , wherein the occupant data comprises one or more of demographic data regarding the occupant comprising one or more of an occupant age, weight, height, or gender, where the occupant was sitting in the motor vehicle, a point of impact on the motor vehicle, or whether an airbag deployed as a result of the motor vehicle accident.

6. An insurance claim analysis device comprising memory comprising programmed instructions stored thereon and one or more processors configured to execute the stored programmed instructions to preform a method comprising:

obtaining, by an insurance claim analysis device, a plurality of images of a damaged motor vehicle involved in a motor vehicle accident, vehicle data describing the motor vehicle, occupant data describing an occupant who occupied the motor vehicle during the motor vehicle accident, and injury data from an electronic insurance claim associated with the motor vehicle accident, the injury data specifying an injury to the occupant;

determining, by the insurance claim analysis device, a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the plurality of images of the damaged motor vehicle and the vehicle data;

determining, by the insurance claim analysis device, an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data;

identifying, by the insurance claim analysis device, a first set of one or more condition indications based on a correlation of the injury severity score with a stored mapping of condition indications to injury severity scores;

comparing, by the insurance claim analysis device, one or more of the condition indications in the first set of condition indications to one or more condition indications in a second set of condition indications in the injury data;

generating, by the insurance claim analysis device, a likelihood value based on the comparing, the likelihood value indicating a likelihood that the injury to the occupant resulted from the motor vehicle accident;

automatically adjudicating, by the insurance claim analysis device, the electronic insurance claim based on the generated likelihood value;

obtaining, by the insurance claim analysis device, further injury data from a medical bill of the occupant;

comparing, by the insurance claim analysis device, one or more of the condition indications in the first set of condition indications to one or more condition indications in a third set of condition indications in the further injury data;

generating, by the insurance claim analysis device, a further likelihood value based on the comparing, the further likelihood value indicating a further likelihood that the injury to the occupant resulted from the motor vehicle accident;

automatically adjudicating, by the insurance claim analysis device, the electronic insurance claim based on the generated further likelihood value;

providing the adjudicated electronic insurance claim to a claims adjusters;

retraining the first machine learning model using the delta velocity value determined using the first machine learning model; and

retraining the second machine learning model using the injury severity score determined using the second machine learning model.

7. The insurance claim analysis device of claim 6 , the method further comprising:

providing, by the insurance claim analysis device, a graphical user interface (GUI) comprising a plurality of display elements, wherein a first one of the display elements represents the generated likelihood value, and wherein a second one of the display elements is operable by a user to select whether the injury should be considered in the adjudication of the insurance claim; and

receiving, by the insurance claim analysis device and via the GUI, a selection regarding whether the injury should be considered in the adjudication of the insurance claim.

8. The insurance claim analysis device of claim 6 , wherein the condition indications comprise International Statistical Classification of Diseases and Related Health Problems (ICD) codes and the injury severity score comprises an Abbreviated Injury Scale (AIS) score.

9. The insurance claim analysis device of claim 6 , wherein the vehicle data comprises one or more of a type of the motor vehicle, an age of the motor vehicle, a size of the motor vehicle, a weight of the motor vehicle, an area of impact on the motor vehicle, a damage extent, one or more crush measurements, or whether the motor vehicle was drivable subsequent to the motor vehicle accident.

10. The insurance claim analysis device of claim 6 , wherein the occupant data comprises one or more of demographic data regarding the occupant comprising one or more of an occupant age, weight, height, or gender, where the occupant was sitting in the motor vehicle, a point of impact on the motor vehicle, or whether an airbag deployed as a result of the motor vehicle accident.

11. A non-transitory machine readable medium having stored thereon instructions for automatically determining injury treatment relation to a motor vehicle accident comprising executable code that, when executed by one or more processors, causes the processors to perform a method comprising:

obtaining, by an insurance claim analysis device, a plurality of images of a damaged motor vehicle involved in a motor vehicle accident, vehicle data describing the motor vehicle, occupant data describing an occupant who occupied the motor vehicle during the motor vehicle accident, and injury data from an electronic insurance claim associated with the motor vehicle accident, the injury data specifying an injury to the occupant;

determining, by the insurance claim analysis device, a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the plurality of images of the damaged motor vehicle and the vehicle data;

determining, by the insurance claim analysis device, an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data;

identifying, by the insurance claim analysis device, a first set of one or more condition indications based on a correlation of the injury severity score with a stored mapping of condition indications to injury severity scores;

comparing, by the insurance claim analysis device, one or more of the condition indications in the first set of condition indications to one or more condition indications in a second set of condition indications in the injury data;

generating, by the insurance claim analysis device, a likelihood value based on the comparing, the likelihood value indicating a likelihood that the injury to the occupant resulted from the motor vehicle accident;

automatically adjudicating, by the insurance claim analysis device, the electronic insurance claim based on the generated likelihood value;

obtaining, by the insurance claim analysis device, further injury data from a medical bill of the occupant;

comparing, by the insurance claim analysis device, one or more of the condition indications in the first set of condition indications to one or more condition indications in a third set of condition indications in the further injury data;

generating, by the insurance claim analysis device, a further likelihood value based on the comparing, the further likelihood value indicating a further likelihood that the injury to the occupant resulted from the motor vehicle accident;

automatically adjudicating, by the insurance claim analysis device, the electronic insurance claim based on the generated further likelihood value;

providing the adjudicated electronic insurance claim to a claims adjusters;

retraining the first machine learning model using the delta velocity value determined using the first machine learning model; and

retraining the second machine learning model using the injury severity score determined using the second machine learning model.

12. The non-transitory machine readable medium of claim 11 , the method further comprising:

providing, by the insurance claim analysis device, a graphical user interface (GUI) comprising a plurality of display elements, wherein a first one of the display elements represents the generated likelihood value, and wherein a second one of the display elements is operable by a user to select whether the injury should be considered in the adjudication of the insurance claim; and

receiving, by the insurance claim analysis device and via the GUI, a selection regarding whether the injury should be considered in the adjudication of the insurance claim.

13. The non-transitory machine readable medium of claim 11 , wherein the condition indications comprise International Statistical Classification of Diseases and Related Health Problems (ICD) codes and the injury severity score comprises an Abbreviated Injury Scale (AIS) score.

14. The non-transitory machine readable medium of claim 11 , wherein:

the vehicle data comprises one or more of a type of the motor vehicle, an age of the motor vehicle, a size of the motor vehicle, a weight of the motor vehicle, an area of impact on the motor vehicle, a damage extent, one or more crush measurements, or whether the motor vehicle was drivable subsequent to the motor vehicle accident; and

the occupant data comprises one or more of demographic data regarding the occupant comprising one or more of an occupant age, weight, height, or gender, where the occupant was sitting in the motor vehicle, a point of impact on the motor vehicle, or whether an airbag deployed as a result of the motor vehicle accident.