Systems and methods of using artificial intelligence and machine learning in a telemedical environment to predict user disease states
Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises, while the user uses a treatment apparatus to perform a first treatment plan for the user, receiving measurement data associated with the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.
1. A method for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome, the method comprising:
receiving attribute data associated with a user, wherein the attribute data comprises one or more symptoms associated with the user;
while the user uses an electromechanical machine to perform a first treatment plan for the user, receiving measurement data associated with the user;
generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user, wherein the generating is based on at least the attribute data associated with the user and the measurement data associated with the user, and wherein the second treatment plan comprises a description of one or more predicted disease states of the user; and
transmitting, to a computing device, the second treatment plan for the user.
2. The method of claim 1 , wherein the method further comprises:
determining, by the artificial intelligence engine, one or more associations between one or more confirmed disease states of the user and at least one selected from the group consisting of the attribute data associated with the user, the measurement data associated with the user, and the one or more predicted disease states of the user;
generating, by the artificial intelligence engine, a set of training data based on at least the one or more associations; and
generating, by training the one or more machine learning models with the set of training data, one or more updated machine learning models.
3. The method of claim 2 , wherein the user is a first user, wherein the method further comprises generating, by the artificial intelligence engine configured to use the one or more updated machine learning models, a third treatment plan for a second user, and wherein the generating is based on at least attribute data associated with the second user and measurement data associated with the second user.
4. The method of claim 1 , wherein the method further comprises:
generating, by the artificial intelligence engine configured to use the one or more machine learning models, a set of questions related to the one of more symptoms of the user; and
prompting the user to provide one or more answers to the set of questions,
wherein, based on the one or more answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan for the user.
5. The method of claim 4 , wherein the method further comprises:
generating, by the artificial intelligence engine configured to use the one or more machine learning models, a set of additional questions concerning the one of more symptoms of the user, wherein the generating is based on at least the one or more answers provided by the user; and
prompting the user to provide one or more additional answers to the set of additional questions,
wherein, based on the one or more additional answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan.
6. The method of claim 1 , wherein each of the one or more predicted disease states of the user has a corresponding probability score.
7. The method of claim 1 , wherein the method further comprises:
sending one or more control signals to the electromechanical machine; and
adjusting, in response to the electromechanical machine receiving the one or more control signals, one or more portions of the electromechanical machine, wherein such adjustment complies with one or more operating parameters specified in the second treatment plan.
8. The method of claim 1 , wherein the computing device comprises a clinical portal of a healthcare professional, and wherein the second treatment plan is transmitted to the clinical portal, in real-time or near real-time during a telemedicine session in which the clinical portal is engaged with a user portal of the user, of the healthcare professional.
9. The method of claim 1 , wherein the computing device comprises a user portal of the user, and wherein the second treatment plan is transmitted to the user portal, in real-time or near real-time during a telemedicine session in which the user portal is engaged with a clinical portal of a healthcare professional, of the user.
10. The method of claim 1 , wherein the second treatment plan is for at least one selected from the group consisting of habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, pliability training, flexibility training, weight stability, weight gain, weight loss, cardiovascular health, endurance improvement, and pulmonary health.
11. A system for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome, the system comprising:
a memory device for storing instructions; and
a processing device communicable coupled to the memory device, the processing device configured to execute the instructions to:
receive attribute data associated with a user, wherein the attribute data comprises one or more symptoms associated with the user,
while the user uses an electromechanical machine to perform a first treatment plan for the user, receive measurement data associated with the user,
generate, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user, wherein the generating is based on at least the attribute data associated with the user and the measurement data associated with the user, and wherein the second treatment plan comprises a description of one or more predicted disease states of the user, and
transmit, to a computing device, the second treatment plan.
12. The system of claim 11 , wherein the processing device is further configured to execute the instructions to:
determine, by the artificial intelligence engine, one or more associations between one or more confirmed disease states of the user and at least one selected from the group consisting of the attribute data associated with the user, the measurement data associated with the user, and the one or more predicted disease states of the user,
generate, by the artificial intelligence engine, a set of training data based on at least the one or more associations, and
generate, by training the one or more machine learning models with the set of training data, one or more updated machine learning models.
13. The system of claim 12 , wherein the user is a first user, wherein the processing device is further configured to execute the instructions to generate, by the artificial intelligence engine configured to use the one or more updated machine learning models, a third treatment plan for a second user, and wherein the generating is based on at least attribute data associated with the second user and measurement data associated with the second user.
14. The system of claim 11 , wherein the processing device is further configured to execute the instructions to:
generate, by the artificial intelligence engine configured to use the one or more machine learning models, a set of questions concerning the one of more symptoms of the user, and
prompt the user to provide one or more answers to the set of questions,
wherein, based on the one or more answers provided to the user, the artificial intelligence engine is further configured to generate the second treatment plan for the user.
15. The system of claim 14 , wherein the processing device is further configured to execute the instructions to:
determine, by the artificial intelligence engine configured to use the one or more machine learning models, a set of additional questions concerning the one of more symptoms of the user, wherein the determining is based on at least the one or more answers provided by the user, and
prompt the user to provide one or more additional answers to the set of additional questions,
wherein, based on the one or more additional answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan.
16. The system of claim 11 , wherein each of the one or more predicted disease states of the user has a corresponding probability score.
17. The system of claim 11 , wherein the processing device is further configured to execute the instructions to send one or more control signals to the electromechanical machine, wherein the electromechanical machine is configured to adjust, in response to the electromechanical machine receiving the one or more control signals, one or more portions of the electromechanical machine, and wherein such adjustment complies with one or more operating parameters specified in the second treatment plan.
18. The system of claim 11 , wherein the computing device comprises a clinical portal of a healthcare professional, and wherein the second treatment plan is transmitted to the clinical portal, in real-time or near real-time during a telemedicine session in which the clinical portal is engaged with a user portal of the user, of the healthcare professional.
19. The system of claim 11 , wherein the computing device comprises a user portal of the user, and wherein the second treatment plan is transmitted to the user portal, in real-time or near real-time during a telemedicine session in which the user portal is engaged with a clinical portal of a healthcare professional, of the user.
20. The system of claim 11 , wherein the second treatment plan is for at least one selected from the group consisting of habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, pliability training, flexibility training, weight stability, weight gain, weight loss, cardiovascular health, endurance improvement, and pulmonary health.
21. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
receive attribute data associated with a user, wherein the attribute data comprises one or more symptoms associated with the user;
while the user uses an electromechanical machine to perform a first treatment plan for the user, receive measurement data associated with the user;
generate, by an artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user, wherein the generating is based on at least the attribute data associated with the user and the measurement data associated with the user, and wherein the second treatment plan comprises a description of one or more predicted disease states of the user; and
transmit, to a computing device, the second treatment plan.
22. The non-transitory computer-readable medium of claim 21 , wherein the instructions further cause the processing device to:
determine, by the artificial intelligence engine, one or more associations between one or more confirmed disease states of the user and at least one selected from the group consisting of the attribute data associated with the user, the measurement data associated with the user, and the one or more predicted disease states of the user,
generate, by the artificial intelligence engine, a set of training data based on at least the one or more associations, and
generate, by training the one or more machine learning models with the set of training data, one or more updated machine learning models.
23. The non-transitory computer-readable medium of claim 22 , wherein the user is a first user, wherein the instructions further cause the processing device to generate, by the artificial intelligence engine configured to use the one or more updated machine learning models, a third treatment plan for a second user, and wherein the generating is based on at least attribute data associated with the second user and measurement data associated with the second user.
24. The non-transitory computer-readable medium of claim 21 , wherein the instructions further cause the processing device to:
generate, by the artificial intelligence engine configured to use the one or more machine learning models, a set of questions concerning the one of more symptoms of the user, and
prompt the user to provide one or more answers to the set of questions,
wherein, based on the one or more answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan for the user.
25. The non-transitory computer-readable medium of claim 24 , wherein the instructions further cause the processing device to:
determine, by the artificial intelligence engine configured to use the one or more machine learning models, a set of additional questions concerning the one of more symptoms of the user, wherein the determining is based on at least the one or more answers provided by the user, and
prompt the user to provide one or more additional answers to the set of additional questions,
wherein, based on the one or more additional answers provided by the user, the artificial intelligence engine is further configured to generate the second treatment plan.
26. The non-transitory computer-readable medium of claim 21 , wherein each of the one or more predicted disease states of the user has a corresponding probability score.
27. The non-transitory computer-readable medium of claim 21 , wherein the instructions further cause the processing device to send one or more control signals to the electromechanical machine, wherein the electromechanical machine is configured to adjust, in response to the electromechanical machine receiving the one or more control signals, one or more portions of the electromechanical machine, and wherein such adjustment complies with one or more operating parameters specified in the second treatment plan.
28. The non-transitory computer-readable medium of claim 21 , wherein the computing device comprises a clinical portal of a healthcare professional, and wherein the second treatment plan is transmitted to the clinical portal, in real-time or near real-time during a telemedicine session in which the clinical portal is engaged with a user portal of the user, of the healthcare professional.
29. The non-transitory computer-readable medium of claim 21 , wherein the computing device comprises a user portal of the user, and wherein the second treatment plan is transmitted to the user portal, in real-time or near real-time during a telemedicine session in which the user portal is engaged with a clinical portal of a healthcare professional, of the user.
30. The non-transitory computer-readable medium of claim 21 , wherein the second treatment plan is for at least one selected from the group consisting of habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, pliability training, flexibility training, weight stability, weight gain, weight loss, cardiovascular health, endurance improvement, and pulmonary health.