System and method for using AI/ML and telemedicine for invasive surgical treatment to determine a cardiac treatment plan that uses an electromechanical machine
A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.
1 . A computer-implemented system for controlling an electromechanical machine, the computer-implemented system comprising:
the electromechanical machine that is manipulated by a user while the user performs a treatment plan;
an interface comprising a display that presents information pertaining to the treatment plan; and
a processing device that:
receives, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels;
controls, based on the first treatment plan, the electromechanical machine in order to implement the at least two exercise sessions that, based on the invasive surgical-related health issue of the user, instruct the user to perform the exercise at the different exertion levels; and
receives a second treatment plan generated by one or more machine learning models, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a measure of perceived exertion and on the invasive surgical-related health issue of the user.
2 . The computer-implemented system of claim 1 , wherein the processing device receives data from one or more sensors configured to measure the data associated with the invasive surgical-related health issues of the user, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.
3 . The computer-implemented system of claim 1 , wherein the invasive surgical-related health issue further comprises an oncologic health issue, another surgery-related health issue, or some combination thereof.
4 . The computer-implemented system of claim 1 , wherein the second treatment plan comprises a modified parameter pertaining to the electromechanical machine, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof, and the computer-implemented system further comprises:
controlling the electromechanical machine based on the modified parameter.
5 . The computer-implemented system of claim 1 , wherein the measure of perceived exertion comprises a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).
6 . The computer-implemented system of claim 1 , wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the at least one exertion level desired for each session, and the one or more machine learning models are trained using data pertaining to the measure of perceived exertion, other users' data, and other users' invasive surgical-related health issues.
7 . The computer-implemented system of claim 1 , wherein the first treatment plan is generated based on attribute data comprising an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, information pertaining to invasive surgical-related health issues of other users, or some combination thereof.
8 . The computer-implemented system of claim 1 , wherein the processing device transmits the data by to a second computing device that relays the data to a third computing device of a healthcare professional.
9 . The computer-implemented system of claim 1 , wherein the processing device receives data associated with a user and the data comprises other procedures performed on the user, an electronic medical record associated with the user, a weight of the user, a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a pulmonary diagnosis of the user, an oncologic diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof.
10 . The computer-implemented system of claim 1 , wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.
11 . The computer-implemented system of claim 10 , wherein one of more types of the invasive surgical-related health issue are grouped into a plurality of surgery groups and the one or more machine learning models generate the second treatment plan based on one or more of the plurality of surgery groups including the invasive surgical-related health issue.
12 . The computer-implemented system of claim 1 , wherein the one or more machine learning models generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.
13 . The computer-implemented system of claim 12 , wherein the at least one pain level includes a plurality of pain levels each associated with one of a plurality of predetermined movements executed by the user.
14 . A computer-implemented method for controlling an electromechanical machine, the computer-implemented method comprising:
receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable instruct the user to perform an exercise at different exertion levels;
controlling, based on the first treatment plan, the electromechanical machine in order to implement the at least two exercise sessions that, based on the invasive surgical-related health issue of the user, instruct the user to perform the exercise at the different exertion levels; and
receiving a second treatment plan generated by one or more machine learning models, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a measure of perceived exertion and on the invasive surgical-related health issue of the user.
15 . The computer-implemented method of claim 14 , further comprising receiving data associated with a user, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.
16 . The computer-implemented method of claim 14 , wherein the invasive surgical-related health issue further comprises an oncologic health issue, another surgery-related health issue, or some combination thereof.
17 . The computer-implemented method of claim 14 , wherein the second treatment plan comprises a modified parameter pertaining to the electromechanical machine, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof, and the computer-implemented method further comprises:
controlling the electromechanical machine based on the modified parameter.
18 . The computer-implemented method of claim 14 , wherein the measure of perceived exertion comprises a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).
19 . The computer-implemented method of claim 14 , wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the at least one exertion level desired for each session, and the one or more machine learning models are trained using data pertaining to the measure of perceived exertion, other users' data, and other users' invasive surgical-related health issues.
20 . A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
receive, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, instruct the user to perform an exercise at different exertion levels;
control, based on the first treatment plan, an electromechanical machine in order to implement the at least two exercise sessions that, based on the invasive surgical-related health issue of the user, instruct the user to perform the exercise at the different exertion levels; and
receive a second treatment plan generated by one or more machine learning models, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a measure of perceived exertion and on the invasive surgical-related health issue of the user.