System and method for using AI, machine learning and telemedicine to perform pulmonary rehabilitation via an electromechanical machine
A computer-implemented system includes one or more processing devices configured to receive attribute data associated with a user, generate, based on a pulmonary condition of the user, a selected set of the attribute data, determine, based on the selected set of the attribute data, a first probability of improving a pulmonary condition of the user subsequent to at least one of a pulmonary procedure being performed on the user, a pulmonary treatment being performed on the user, and a pulmonary diagnosis, and generate, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability. A treatment apparatus is configured to enable implementation of the treatment plan.
1. A computer-implemented system, comprising:
one or more processing devices configured to
receive attribute data associated with a user,
generate, based on a pulmonary condition of the user, a selected set of the attribute data,
determine, based on the selected set of the attribute data, a first probability of improving a pulmonary condition of the user subsequent to at least one of a pulmonary procedure being performed on the user, a pulmonary treatment being performed on the user, and a pulmonary diagnosis, and
generate, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability; and
a treatment apparatus configured to enable implementation of the treatment plan.
2. The computer-implemented system of claim 1 , wherein the attribute data includes data associated with a pulmonary health of the user.
3. The computer-implemented system of claim 1 , wherein the one or more processing devices are configured to execute an attribute data model, and wherein, to generate the selected set of the attribute data, the attribute model is configured to at least one of assign weights to the attribute data, rank the attribute data, and filter the attribute data.
4. The computer-implemented system of claim 3 , wherein the one or more processing devices are configured to execute a probability model, wherein the probability model is configured to determine the first probability.
5. The computer-implemented system of claim 4 , 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 to modify the first probability.
6. The computer-implemented system of claim 1 , wherein the one or more processing devices are further configured, based on the selected set of the attribute data, to generate a second probability that the user will be eligible for the at least one of the pulmonary procedure and the pulmonary treatment.
7. The computer-implemented system of claim 6 , wherein the one or more processing devices are configured to at least one of (i) generate the treatment plan further to modify the second probability and (ii) generate a recommendation of whether the user should undergo the at least one of the pulmonary procedure and the pulmonary treatment.
8. The computer-implemented system of claim 7 , wherein, subsequent to implementing the treatment plan using the treatment apparatus, the one or more processing devices are configured, based on the recommendation, to modify the treatment plan.
9. The computer-implemented system of claim 8 , wherein the one or more processing devices are configured to transmit the modified treatment plan to cause the treatment apparatus to implement at least one modified exercise of the modified treatment plan.
10. The computer-implemented system of claim 1 , wherein, while the user performs the treatment plan, the one or more processing devices are configured to initiate 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 processing devices are configured, based on a respiratory exertion threshold associated with dyspnoea experienced by the user, to modify the treatment plan.
12. The computer-implemented system of claim 11 , wherein the one or more processing devices are configured, based on performance information indicative of characteristics of the user measured while the user performs the treatment plan, to modify the respiratory exertion threshold.
13. A method, comprising:
using one or more processing devices,
receiving attribute data associated with a user,
generating, based on a pulmonary condition of the user, a selected set of the attribute data,
determining, based on the selected set of the attribute data, a first probability of improving a pulmonary condition of the user subsequent to at least one of a pulmonary procedure being performed on the user, a pulmonary treatment being performed on the user, and a pulmonary diagnosis, and
generating, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability; and
using a treatment apparatus to implement the treatment plan.
14. The method of claim 13 , wherein the attribute data includes data associated with a pulmonary health of the user.
15. The method of claim 13 , further comprising, using the one or more processing devices, executing an attribute data model, and wherein, to generate the selected set of the attribute data, the attribute model at least one of assigns weights to the attribute data, ranks the attribute data, and filters the attribute data.
16. The method of claim 15 , further comprising, using the one or more processing devices, executing a probability model, wherein the probability model is configured to determine the first probability.
17. The method of claim 16 , further comprising, using the one or more processing devices, executing a treatment plan model, wherein the treatment plan model is configured to generate the treatment plan to modify the first probability.
18. The method of claim 13 , further comprising, using the one or more processing devices, generating, based on the selected set of the attribute data, a second probability that the user will be eligible for the at least one of the pulmonary procedure and the pulmonary treatment.
19. The method of claim 18 , further comprising, using the one or more processing devices, at least one of (i) generating the treatment plan further to modify the second probability and (ii) generating a recommendation of whether the user should undergo the at least one of the pulmonary procedure and the pulmonary treatment.
20. The method of claim 19 , further comprising, using the one or more processing devices subsequent to implementing the treatment plan using the treatment apparatus, modifying, based on the recommendation, the treatment plan.
21. The method of claim 20 , further comprising, using the one or more processing devices, transmitting the modified treatment plan to cause the treatment apparatus to implement at least one modified exercise of the modified treatment plan.
22. The method of claim 13 , further comprising, while the user performs the treatment plan, using the one or more processing devices to initiate a telemedicine session between a computing device of the user and a computing device of a healthcare professional.
23. The method of claim 13 , further comprising using the one or more processing devices to, based on a respiratory exertion threshold associated with dyspnoea experienced by the user, modify the treatment plan.
24. The method of claim 23 , further comprising using the one or more processing devices to, based on performance information indicative of characteristics of the user measured while the user performs the treatment plan, modify the respiratory exertion threshold.