Abstract: A method includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving alignment data associated with the user while the user engages in at least one activity and receiving at least one alignment characteristic associated with the user and determining, using at least the at least one alignment characteristic, whether the at least one alignment characteristic correlates with at least one secondary condition of the user. The method also includes generating secondary condition information indicating at least the secondary condition and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input. The treatment plan input includes at least one modification to the at least one aspect of the treatment plan and wherein, further, the treatment plan input is generated based on the secondary condition information.

Abstract: Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, an exercise program comprising a first user energy score, wherein the method comprises generating, by the artificial intelligence engine, the exercise program including an exercise plan including a plurality of exercises. Each respective exercise is associated with user energy consumption metrics based at least on a metabolic equivalent of task (MET) value, and based on the user energy consumption metrics, the first user energy score is associated with the exercise program. The method includes receiving data pertaining to a plurality of users. The data includes physical activity goals the plurality of users desires to achieve. The method includes determining second user energy scores for the physical activity goals, and based on the first and second user energy scores, assigning, by the artificial intelligence engine, at least a subset of the plurality of users to the exercise program.

Abstract: A system for processing medical claims, comprising a processor configured to receive device-generated information from a medical device. The device-generated information includes performance information. The processor generates a first biometric signature, and using the device-generated information, generates a second biometric signature, wherein the second biometric signature uses the performance information. Using the first and second biometric signatures, the processor generates a signature comparison. Using the signature comparison, the processor generates a signature indicator and transmits the signature indicator.

Abstract: A system that comprises a memory device storing instructions, and a processing device communicatively coupled to the memory device. The processing device executes the instructions to: receive user data obtained from records associated with a user; generate a modified treatment plan based on the user data; and send, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.

Abstract: In one aspect, a method is disclosed for using dialects to generate candidate drug compounds. The dialects describe sequences of the candidate drug compounds and activities associated with the sequences. The method includes receiving a data set, training, using the data set, first layers of a machine learning model to determine relationships of components of a portion of a string described by a first dialect. The components pertain to amino acids associated with first activity level information of the sequences. The method includes training, using the data set and the portion of the string, a final layer to generate a remainder of the string. The remainder pertains to second activity level information of the sequences. The method includes generating, using the first and final layer, the string comprising the portion and the remainder. The string represents a candidate drug compound.

Abstract: A method for generating, by an artificial intelligence engine, a treatment plan and a billing sequence associated with the treatment plan is disclosed. The method includes receiving information pertaining to a patient. The information includes a medical diagnosis code of the patient. The method includes generating, based on the information, the treatment plan for the patient. The treatment plan includes instructions for the patient to follow. The method includes receiving a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The method includes generating, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The method includes transmitting the treatment plan and the billing sequence to a computing device.

Abstract: A method is disclosed for generating treatment plans for optimizing patient outcome and monetary value amount generated. The method includes receiving a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients, receiving a set of monetary value amounts associated with the set of treatment plans, receiving a set of constraints, where the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans. The method includes generating, by the artificial intelligence engine, optimal treatment plans for a patient, where the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints. Each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. The method includes transmitting the optimal treatment plans to a computing device.

Abstract: Systems and methods for identifying a condition of a user. A treatment apparatus is configured to be manipulated by the user for performing an exercise, and an interface is communicably coupled to the treatment apparatus. One or more sensors are configured to sense one or more characteristics of an anatomical structure of the user. A processing device and a memory is communicatively coupled to the processing device. The memory includes computer readable instructions, that when executed by the processing device, cause the processing device to: receive, from the sensors, one or more sensor inputs representative of the one or more of characteristics of the anatomical structures; calculate an infection probability of a disease based on the one or more characteristics of the anatomical structures; and output, to the interface, a representation of the infection probability.

Abstract: Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, an alignment plan capable of enabling, during a treatment session, the aligning of a user's body. The method comprises generating machine learning models trained to identify alignment plans. The method also comprises receiving treatment data that comprises a first position of the user's body, aspects of a treatment plan, and one or more attributes of the user. The method further comprises generating the alignment plan by using the machine learning models. The generating is based on at least the aspects of the treatment plan and at least one of the one or more attributes of the user. The alignment plan may comprise a target position of the user's body and one or more elements for adjusting the user's body from the first position to the target position. The method also comprises transmitting the plan to a computing device.

Abstract: The present disclosure provides a method comprising the steps: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan including an exercise; receiving first measurement data, where the first measurement data is associated with a first performance data; receiving second measurement data, where the second measurement data is associated with at least one of a second performance data and a second patient identifier; one of anonymiztion of and pseudonymization of the second patient identifier; determining, via an artificial intelligence engine and based on one or more differences between the first and the second measurement data, differential data; and presenting, with a patient interface, at least one of the differential data, first measurement data, second measurement data, and the first patient data.

Abstract: A method includes receiving treatment data pertaining to a user capable of using a treatment device to perform a treatment plan and receiving activity data pertaining to the user while the user engages in at least one activity. The method also includes generating treatment information using the treatment data and the activity data and writing to an associated memory, for access by a healthcare professional, the treatment information. The method also includes modifying at least one aspect of the treatment plan in response to receiving, from the healthcare professional, treatment plan input including at least one modification to the at least one aspect of the treatment plan.

Abstract: A method is disclosed for generating an improved exercise plan for a user to perform using at least an exercise machine. The method includes receiving data pertaining to the user. The data includes a physical activity goal the user desires to achieve and the physical activity goal includes levels of attainment to achieve. The method includes generating, by an artificial intelligence engine, the improved exercise plan. The improved exercise plan includes a set of exercises to be performed by the user to achieve the levels of attainment associated with the physical activity goal. The artificial intelligence engine uses at least one data source configured to include information pertaining to one or more exercises and at least one of the levels of attainment associated with the physical activity goal. The method includes transmitting the improved exercise plan to a computing device.

Abstract: A method includes receiving treatment data pertaining to a user capable of using a treatment device to perform a treatment plan and receiving activity data pertaining to the user while the user engages in at least one activity. The method also includes generating treatment information using the treatment data and the activity data and writing to an associated memory, for access by a healthcare professional, the treatment information. The method also includes modifying at least one aspect of the treatment plan in response to receiving, from the healthcare professional, treatment plan input including at least one modification to the at least one aspect of the treatment plan.

Abstract: A multidimensional exercise control system is disclosed herein. In one embodiment, a control system of an exercise machine comprises a memory device storing instructions, a user interface for presenting a video game with an avatar, and a processing device operatively coupled to the memory device and the user interface. The processing device is configured to execute the instructions to receive sensor data from a sensor operatively coupled to an exercise machine, wherein the sensor data comprises one or more measurements; responsive to the one or more measurements, cause the avatar to change a position in the video game; compare the one or more measurements to a target threshold; determine whether the one or more measurement exceed the target threshold; and responsive to determining that the one or more measurements exceed the target threshold, cause the user interface to present a modification to the video game.

Abstract: A method for generating an exercise session for a user using an exercise machine is disclosed herein. The method includes receiving a plurality of inputs, wherein the plurality of inputs comprise an indication of a level of pain of the user and a range of motion of a body part of the user. The method also includes determining, based on the plurality of inputs, an exercise level of the user, generating, using a machine learning model, the exercise session for the user by selecting, based on the exercise level of the user, one or more exercises to be performed by the user using an exercise machine, and causing initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session.

Abstract: Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, an exercise plan for a user to perform. The method comprises receiving data pertaining to the user and generating user energy consumption metrics for a plurality of exercises. Each of the user energy consumption metrics is generated for a respective one of the plurality of exercises based at least on a metabolic equivalent of task (MET) value for the respective one of the plurality of exercises and user fitness test results. The method also includes generating the exercise plan based at least on the user energy consumption metrics and a user energy score. The exercise plan includes at least a subset of the plurality of exercises to be performed by the user to attempt to achieve the user energy score. The method further includes transmitting the exercise plan to a computing device.

Abstract: Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors.

Abstract: A method includes receiving first data pertaining to a first user using a treatment apparatus to perform a treatment plan, wherein the first data comprises an attribute of the first user and an occupational task associated with the first user; receiving second data pertaining to a second user, wherein the second data comprises an attribute of the second user and an occupational task associated with the second user; determining whether an attribute and occupational task of the second user matches an attribute and occupational task of the first user; and responsive to determining that the attribute and the occupational task of the second user matches the attribute and occupational task of the first user, predicting, via an artificial intelligence engine, an estimate of when the second user performing the treatment plan would be capable of performing the occupational task associated with the second user.

Abstract: A method is disclosed for generating a treatment plan to modify an attribute of a user, wherein the attribute of the user is associated with an attribute of an occupational task. The method includes receiving data associated with the user, wherein the data comprises the modified attribute of the user and the attribute of the occupational task; accessing a database comprising data associated with exercises associated with the modified attribute of the user and the attribute of the occupational task; and generating, via an artificial intelligence engine, the treatment plan comprising an exercise of the exercises associated with the modified attribute of the first user and the attribute of the occupational task, wherein the artificial intelligence engine generates the treatment plan comprising the exercise based on the data associated with the user and the data associated with the exercises.

Abstract: A computer-implemented system for processing medical claims is disclosed. The system includes a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor. During the telemedicine session, the processor is configured to receive information from a medical device. Using the device-generated information, the processor is further configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server.