Systems and methods for using artificial intelligence and machine learning to improve cardiovascular health such that the need for a cardiac intervention is mitigated
Systems, methods, and computer-readable media for improving cardiovascular health such that the need for a cardiac intervention is mitigated. The system includes one or more processing devices and an electromechanical machine. The one or more processing device are configured to receive a plurality of risk factors associated with a cardiac-related event for a user. The one or more processing device are also configured to generate a selected set of the risk factors. The one or more processing device are further configured to determine, based on the selected set of the risk factors, a probability that a cardiac intervention will occur. The one or more processing device are also configured to generate, based on the probability and the selected set of the risk factors, a treatment plan including exercises directed to reducing the probability that the cardiac intervention will occur. The electromechanical machine is configured to implement the treatment plan.
1 . A computer-implemented system, comprising:
one or more processing devices configured to:
receive a plurality of risk factors associated with a cardiac-related event for a user, generate a selected set of the risk factors,
determine, based on the selected set of the risk factors, a probability that a cardiac intervention will occur,
generate, based on the probability and the selected set of the risk factors, a treatment plan including one or more exercises directed to reducing the probability that the cardiac intervention will occur, and
control, based on the treatment plan, operation of at least one physical component of an electromechanical machine, wherein the operation is controlled by modifying one or more operating parameters associated with the at least one physical component; and
the electromechanical machine configured to implement the treatment plan while the electromechanical machine is being manipulated by the user.
2 . The computer-implemented system of claim 1 , wherein the one or more processing devices are configured to execute a risk factor model, and wherein, to generate the selected set of the risk factors, the risk factor model is configured to at least one of assign weights to the risk factors, rank the risk factors, and filter the risk factors.
3 . The computer-implemented system of claim 2 , wherein the one or more processing devices are configured to execute a risk factor model, and wherein, to generate the selected set of the risk factors, the risk factor model is configured to at least one of assign weights to the risk factors, rank the risk factors, and filter the risk factors.
4 . The computer-implemented system of claim 3 , wherein the probability model is configured to determine the probability based on respective probabilities associated with individual ones of the selected set of the risk factors.
5 . The computer-implemented system of claim 3 , wherein the one or more processing devices are configured to execute a treatment plan model, wherein the treatment plan model is configured to generate the treatment plan based on individual probabilities of the cardiac intervention of respective ones of the selected set of the risk factors.
6 . The computer-implemented system of claim 5 , wherein the treatment plan model is configured to generate the treatment plan based on an identified one of the selected set of the risk factors having a largest contribution to the probability that the cardiac intervention will occur.
7 . The computer-implemented system of claim 6 , wherein, subsequent to implementing the treatment plan using the electromechanical machine, the one or more processing devices are configured to modify the treatment plan based on a determination of whether the treatment plan reduced either one of (i) the probability that the cardiac intervention will occur and (ii) the identified one of the selected set of the risk factors.
8 . The computer-implemented system of claim 7 , wherein the one or more processing devices are configured to transmit the modified treatment plan to cause the electromechanical machine to implement at least one modified exercise of the modified treatment plan.
9 . The computer-implemented system of claim 1 , wherein the cardiac intervention is for minimizing one or more negative effects of the cardiac-related event.
10 . The computer-implemented system of claim 1 , wherein the one or more processing devices are configured to initiate, while the user performs the treatment plan, a telemedicine session between a computing device of the user and a computing device of a healthcare professional.
11 . The computer-implemented system of claim 1 , wherein the one or more risk factors comprise modifiable risk factors and non-modifiable risk factors.
12 . A computer-implemented method, comprising:
receiving a plurality of risk factors associated with a cardiac-related event for a user;
generating a selected set of the risk factors;
determining, based on the selected set of the risk factors, a probability that a cardiac intervention will occur;
generating, based on the probability and the selected set of the risk factors, a treatment plan including one or more exercises directed to reducing the probability that the cardiac intervention will occur;
controlling, based on the treatment plan, operation of at least one physical component of an electromechanical machine, wherein the operation is controlled by modifying one or more operating parameters associated with the at least one physical component; and
using electromechanical machine to implement the treatment plan while the electromechanical machine is being manipulated by the user.
13 . The computer-implemented method of claim 12 , further comprising:
using a risk factor machine learning model to generate the selected set of the risk factors, wherein the risk factor machine learning model is configured to at least one of assign weights to the risk factors, rank the risk factors, and filter the risk factors; and
using a probability machine learning model to determine the probability that the cardiac intervention will occur.
14 . The computer-implemented method of claim 13 , further comprising using the probability machine learning model to determine the probability based on respective probabilities associated with individual ones of the selected set of the risk factors.
15 . The computer-implemented method of claim 14 , further comprising using a treatment plan machine learning model to generate the treatment plan based on individual probabilities of the cardiac intervention of respective ones of the selected set of the risk factors.
16 . The computer-implemented method of claim 14 , further comprising generating the treatment plan based on an identified one of the selected set of the risk factors having a largest contribution to the probability that the cardiac intervention will occur.
17 . The computer-implemented method of claim 16 , further comprising, subsequent to implementing the treatment plan using the treatment apparatus, modifying the treatment plan based on a determination of whether the treatment plan reduced either one of (i) the probability that the cardiac intervention will occur and (ii) the identified one of the selected set of the risk factors.
18 . The computer-implemented method of claim 12 , wherein the cardiac intervention is for minimizing one or more negative effects of the cardiac-related event.
19 . The computer-implemented method of claim 12 , wherein the one or more risk factors comprise modifiable risk factors and non-modifiable risk factors.
20 . One or more tangible, non-transitory computer-readable media storing instructions that, when executed, cause one or more processing devices to:
receive a plurality of risk factors associated with a cardiac-related event for a user;
generate a selected set of the risk factors;
determine, based on the selected set of the risk factors, a probability that a cardiac intervention will occur;
generate, based on the probability and the selected set of the risk factors, a treatment plan including one or more exercises directed to reducing the probability that the cardiac intervention will occur;
control, based on the treatment plan, operation of at least one physical component of an electromechanical machine, wherein the operation is controlled by modifying one or more operating parameters associated with the at least one physical component; and
use the electromechanical machine to implement the treatment plan while the electromechanical machine is being manipulated by the user.