CN111105859A - Method and device for determining rehabilitation therapy, storage medium and electronic equipment - Google Patents
Method and device for determining rehabilitation therapy, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN111105859A CN111105859A CN201911106979.XA CN201911106979A CN111105859A CN 111105859 A CN111105859 A CN 111105859A CN 201911106979 A CN201911106979 A CN 201911106979A CN 111105859 A CN111105859 A CN 111105859A
- Authority
- CN
- China
- Prior art keywords
- therapy
- warning information
- target
- early warning
- target index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
The disclosure belongs to the technical field of computers, and relates to a rehabilitation therapy determination method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring a target index and an object identifier, and sending a parameter acquisition request carrying the object identifier; receiving a target index parameter corresponding to a target index based on the parameter acquisition request; inputting the target index parameters into a machine learning model trained in advance so that the machine learning model outputs a target therapy; acquiring an evaluation standard corresponding to a target index, and if the target index parameter does not meet the evaluation standard, generating early warning information corresponding to a target therapy; and adjusting the target therapy according to the early warning information so as to perform corresponding rehabilitation training. On one hand, medical care personnel monitor target index parameters of the object in real time and know the health condition of the object; on the other hand, accurate treatment measures and methods are recommended for medical staff, passive rescue is changed into active intervention treatment, and the method has great influence on prevention of serious diseases.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for determining a rehabilitation therapy, a computer-readable storage medium, and an electronic device.
Background
The disease can be delayed by means of prevention, or the disease is not serious to a certain degree. However, due to the weak prevention consciousness of people, not only the physical health of the people is seriously damaged, but also great burden is caused to family members and social economy. Therefore, prevention is more than treatment, which should be focused on, and should also become a scientific medical means, advocating people to prevent the disease in advance.
The medical staff can judge the patient's state of an illness timely and accurately, and play a vital role in judging whether the patient can obtain timely and effective treatment and whether the patient's life can be recovered. With the change of disease spectrum, the main diseases threatening the survival of human beings are not infectious diseases any more, but are chronic diseases such as cancers and cardiovascular and cerebrovascular diseases which are difficult to cure, and the prevention and treatment of the chronic diseases become problems to be solved urgently.
In view of the above, there is a need in the art to develop a new method and apparatus for determining rehabilitation therapy.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a method of determining a rehabilitation therapy, a device for determining a rehabilitation therapy, a computer-readable storage medium, and an electronic apparatus, thereby overcoming, at least to some extent, the problem of not selecting an appropriate rehabilitation therapy for preventing a disease due to the limitations of the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of embodiments of the present invention, there is provided a method of determining rehabilitation therapy, the method comprising: acquiring a target index and an object identifier, and sending a parameter acquisition request carrying the object identifier; receiving a target index parameter corresponding to the target index based on the parameter acquisition request; inputting the target index parameters into a machine learning model trained in advance so that the machine learning model outputs a target therapy; acquiring an evaluation standard corresponding to the target index, and if the target index parameter does not meet the evaluation standard, generating early warning information corresponding to the target therapy; and adjusting the target therapy according to the early warning information so as to perform corresponding rehabilitation training.
In an exemplary embodiment of the invention, before the inputting the target index parameter into the machine learning model trained in advance, the method further includes: acquiring an index parameter sample and a therapy sample corresponding to the index parameter sample; wherein the machine learning model is formed based on the index parameter samples and the therapy samples training; inputting the index parameter sample into a machine learning model to be trained, and acquiring a therapy corresponding to the index parameter sample and output by the machine learning model to be trained; if the therapy does not match the therapy sample, adjusting the parameters of the machine learning model to be trained so that the therapy is the same as the therapy sample.
In an exemplary embodiment of the present invention, the adjusting the target therapy according to the warning information to perform the corresponding rehabilitation training includes: if the early warning information is first early warning information, reducing the frequency or the intensity of rehabilitation training corresponding to the target therapy; if the early warning information is second early warning information, suspending rehabilitation training corresponding to the target therapy; and if the early warning information is third early warning information, stopping the rehabilitation training corresponding to the target therapy.
In an exemplary embodiment of the present invention, if the warning information is first warning information, the reducing the frequency or intensity of rehabilitation training corresponding to the target therapy includes: acquiring the variation of the target index parameter corresponding to the target therapy; if the variable quantity meets a first preset condition, determining that the early warning information is first early warning information; and generating and sending a first early warning report corresponding to the first early warning information so as to reduce the frequency or intensity of rehabilitation training according to the first early warning report.
In an exemplary embodiment of the present invention, if the warning information is second warning information, suspending the rehabilitation training corresponding to the target therapy includes: acquiring the variation of the target index parameter corresponding to the target therapy; if the variable quantity meets a second preset condition, determining that the early warning information is second early warning information; and generating and sending a second early warning report corresponding to the second early warning information so as to suspend rehabilitation training according to the second early warning report.
In an exemplary embodiment of the present invention, if the warning information is third warning information, terminating the rehabilitation training corresponding to the target therapy includes: acquiring the variation of the target index parameter corresponding to the target therapy; if the variable quantity meets a third preset condition, determining that the early warning information is third early warning information; and generating and sending a third early warning report corresponding to the third early warning information so as to terminate rehabilitation training according to the third early warning report.
In an exemplary embodiment of the present invention, the generating of the early warning information corresponding to the target therapy includes: generating candidate early warning information corresponding to the target therapy, and acquiring the number of the candidate early warning information; if the number of the candidate early warning information is multiple, acquiring preset priorities corresponding to the candidate early warning information; and comparing the preset priority and determining early warning information according to a comparison result.
According to a second aspect of embodiments of the present invention, there is provided a rehabilitation therapy determining apparatus, the apparatus including: the index acquisition module is configured to acquire a target index and an object identifier and send a parameter acquisition request carrying the object identifier; a parameter receiving module configured to receive a target index parameter corresponding to the target index based on the parameter obtaining request; a therapy determination module configured to input the target index parameter into a pre-trained machine learning model to cause the machine learning model to output a target therapy; the information generation module is configured to acquire an evaluation standard corresponding to the target index, and if the target index parameter does not meet the evaluation standard, generate early warning information corresponding to the target therapy; and the therapy adjusting module is configured to adjust the target therapy according to the early warning information so as to perform corresponding rehabilitation training.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus including: a processor and a memory; wherein the memory has stored thereon computer readable instructions which, when executed by the processor, implement the method of determining a rehabilitation regimen of any of the exemplary embodiments described above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of determining a rehabilitation therapy in any of the exemplary embodiments described above.
As can be seen from the above technical solutions, the rehabilitation therapy determining method, the rehabilitation therapy determining apparatus, the computer storage medium and the electronic device in the exemplary embodiments of the present invention have at least the following advantages and positive effects:
in the method and the device provided by the exemplary embodiment of the disclosure, the target therapy suitable for the patient can be determined through the acquired target index parameter corresponding to the object identifier, and the basis of rehabilitation training is provided for the patient. On one hand, medical care personnel can monitor target index parameters of the patient in real time and know the health condition of the patient; on the other hand, accurate treatment measures and methods are recommended to medical staff in time, passive rescue is changed into active intervention treatment, and the method has great influence on prevention of serious diseases of patients.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically illustrates a flow chart of a method of determining a rehabilitation therapy in an exemplary embodiment of the present disclosure;
fig. 2 schematically illustrates a flow chart of a method of determining early warning information in an exemplary embodiment of the disclosure;
fig. 3 schematically shows a flow diagram of a method of adjusting a target therapy in an exemplary embodiment of the disclosure;
fig. 4 schematically illustrates a flow chart of a method of adjusting a target therapy according to first warning information in an exemplary embodiment of the disclosure;
fig. 5 schematically illustrates a flow chart of a method of adjusting a target therapy according to second warning information in an exemplary embodiment of the disclosure;
fig. 6 schematically illustrates a flow chart of a method of adjusting a target therapy according to third warning information in an exemplary embodiment of the disclosure;
FIG. 7 is a schematic flow chart diagram illustrating a method of training a machine learning model to be trained in an exemplary embodiment of the present disclosure;
fig. 8 schematically shows a schematic configuration diagram of a rehabilitation therapy determination apparatus in an exemplary embodiment of the present disclosure;
fig. 9 schematically shows a flow chart of a method of determining a rehabilitation therapy in an application scenario in an exemplary embodiment of the present disclosure;
fig. 10 schematically illustrates an electronic device for implementing a determination method of rehabilitation therapy in an exemplary embodiment of the present disclosure;
fig. 11 schematically illustrates a computer-readable storage medium for implementing a determination method of a rehabilitation therapy in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In response to the problems in the related art, the present disclosure provides a method for determining a rehabilitation therapy. Fig. 1 shows a flow chart of a method for determining a rehabilitation therapy, which method comprises at least the following steps, as shown in fig. 1:
and S110, acquiring the target index and the object identifier, and sending a parameter acquisition request carrying the object identifier.
And S120, receiving target index parameters corresponding to the target indexes based on the parameter acquisition requests.
And S130, inputting the target index parameters into a machine learning model trained in advance so that the machine learning model outputs a target therapy.
And S140, acquiring an evaluation standard corresponding to the target index, and if the target index parameter does not meet the evaluation standard, generating early warning information corresponding to the target therapy.
And S150, adjusting the target therapy according to the early warning information so as to perform corresponding rehabilitation training.
In an exemplary embodiment of the disclosure, through the acquired target index parameter corresponding to the object identifier, a target therapy suitable for the patient can be determined, and a basis for rehabilitation training is provided for the patient. On one hand, medical care personnel can monitor target index parameters of the object in real time and know the health condition of the object; on the other hand, accurate treatment measures and methods are recommended to medical staff in time, passive rescue is changed into active intervention treatment, and the method has great influence on prevention of serious diseases of patients.
The respective steps of the determination method of the rehabilitation therapy will be described in detail below.
In step S110, a target index and an object identifier are obtained, and a parameter obtaining request carrying the object identifier is sent.
In one example embodiment of the present disclosure, an index with a health index abnormality detection rate ranked in the front may be determined as the target index. The target indicators may include overweight, dyslipidemia, fatty liver, elevated blood uric acid, elevated blood pressure, and elevated fasting glucose. Among them, the cause of overweight can be related to unhealthy eating habits and lack of physical activity, thus, hypertension, diabetes, coronary heart disease, etc. can be caused; dyslipidemia can be the "genuine" cause of atherosclerosis, a high risk factor for dyslipidemia or chronic cardiovascular and cerebrovascular diseases; the main reasons for the appearance of fatty liver may be long-term alcohol consumption or obesity, fatty liver also being a risk factor for diabetes and hypertension; elevated blood uric acid may be an indicator associated with hypertension, hyperglycemia, and gouty arthritis; the blood pressure rise can be the most main high-risk factor for diseases such as cerebral apoplexy, coronary heart disease, myocardial infarction and the like; when fasting blood sugar is increased, stroke, coronary heart disease and diabetes are prone to occur. In addition, other health indicators may be determined as the target indicator according to actual conditions and settings, which is not particularly limited in the present exemplary embodiment.
The object identification may be any direct or indirect information relevant to identifying the patient and identifying the patient, among others. For example, the object identifier may be one or more unique identifiers, one or more serial numbers, one or more encrypted codes related to the name of the patient, one or more encrypted codes related to the identity of the patient, or medical records of one or more patients, etc., which is not limited in this exemplary embodiment.
When a therapy is to be recommended for a patient, a parameter acquisition request carrying an identification of the subject of the patient may be sent. The parameter obtaining request may be sent through the same terminal, or may be sent through different terminals, so that different rehabilitation centers can obtain the target index parameters of the patient, which is not particularly limited in this exemplary embodiment.
In step S120, a target index parameter corresponding to a target index is received based on the parameter acquisition request.
In an exemplary embodiment of the disclosure, after the parameter obtaining request is sent, the returned target index parameter corresponding to the target index of the patient can be obtained as a basis for recommending the rehabilitation therapy.
In step S130, the target index parameter is input to the machine learning model trained in advance, so that the machine learning model outputs the target therapy.
In an exemplary embodiment of the present disclosure, at the time of machine learning, the sample may be generally divided into three separate parts, a training set, a validation set, and a test set. The training set is used for estimating a model, the verification set is used for determining a network structure or parameters for controlling the complexity of the model, and the test set is used for testing how to finally select the optimal model. The machine learning model can be obtained by training through various machine learning algorithms and is used for obtaining a model of the target therapy according to the target index parameters. The machine learning algorithm used may be any one of a random forest algorithm, a support vector machine algorithm, a rogers regression algorithm, and a convolutional neural network algorithm, and may also be other machine algorithms, which is not particularly limited in this exemplary embodiment. The effect of machine learning modeling can be represented by a true class rate and a negative positive class rate. The true class rate (TPR) is calculated as: TPR is TP/(TP + FN) to represent the proportion of positive instances identified by the classifier to all positive instances; the negative positive class rate (FPR) is calculated as: FPR ═ FP/(FP + TN), used to characterize the proportion of negative instances where the classifier mistakenly considered a positive class. In general, the greater the Kolmogorov-Smirnov value (abbreviated KS value), the greater the degree to which a characterization model can separate positive and negative classes. For example, considering the performance of KS value and TPR in four machine learning models, a convolutional neural network algorithm can be selected as the machine learning model for determining the target therapy. In addition, other machine learning algorithms may be selected according to other evaluation criteria, which is not particularly limited in the present exemplary embodiment. And evaluating the target index parameters through the machine learning model to obtain the target therapy corresponding to the target index parameters.
By way of example, the target therapy may include exercise therapy, physical therapy, task therapy, cognitive training, and physical therapy. Wherein, the exercise therapy can utilize the apparatus, free hand or the self strength of the patient, and the exercise therapy can lead the patient to obtain the training method of the whole body or local exercise function and the recovery of the sense function through certain exercise modes (active or passive exercise, etc.); physical therapy can be a method which utilizes artificial or natural physical factors to act on human bodies to generate favorable reaction so as to achieve the purpose of preventing and treating diseases; the task therapy may be a process of evaluating, treating, and training patients who have lost self-care and labor abilities to varying degrees due to physical, mental, and developmental dysfunction or disability, using purposeful, selected task activities; the cognitive training can be a series of training systems designed by combining psychology professional theories, paradigms and game thinking, the system is combined with the current situation and the psychological development characteristics of a trainee, and mainly trains six cognitive abilities such as attention, perception, memory, thinking power, emotional ability, cognitive flexibility and the like to help the trainee to improve the cognitive level; the physical therapy may be a method of restoring the original physiological functions of the body by non-invasive and non-medicinal treatment for local or systemic dysfunction or lesion of the human body by using physical factors including sound, light, cold, heat, electricity, force (motion and pressure) and the like for treatment.
For example, the target therapy of overweight can be exercise therapy, which is used for achieving the effects of adjusting the body and mind and recovering the health and labor capacity; the target therapy of hypertension can be physiotherapy, and the target therapy can soften blood vessels, purify blood and clear garbage in the blood vessels while reducing blood pressure so as to achieve the effects of reducing and stabilizing blood pressure; the target therapy for preventing stroke, brain trauma, cerebral palsy and the like can be operation treatment and cognitive training; the target therapy for diabetes may be physical therapy such as acupuncture, magnetic therapy, massage or bio-electrotherapy.
In step S140, an evaluation criterion corresponding to the target index is obtained, and if the target index parameter does not satisfy the evaluation criterion, the warning information corresponding to the target therapy is generated.
In an exemplary embodiment of the present disclosure, an evaluation criterion corresponding to a target index is acquired, and whether a target index parameter satisfies the evaluation criterion is compared. For example, if the target index is blood pressure increase, the corresponding assessment criteria may be obtained as systolic pressure being greater than or equal to 140 mm hg and diastolic pressure being greater than or equal to 90 mm hg; if the target index is an increase in fasting glucose, a corresponding assessment criterion of 3.9-6.1mmol/L can be obtained, and this exemplary embodiment is not further illustrated. The target index parameters of the patient are compared with the corresponding evaluation standards, and the abnormal condition of the patient can be determined according to the comparison result and used as the basis for starting an early warning mechanism.
And if the target index parameter does not meet the evaluation standard, determining that the target index parameter meets the preset condition, and generating early warning information corresponding to the target therapy. When the target index parameter of the patient does not meet the corresponding evaluation standard, corresponding early warning information can be generated according to the target index parameter. The early warning information can be information used for sending emergency signals to medical staff and reporting dangerous conditions, and can be used as a basis for the medical staff to adjust the target therapy.
In an alternative embodiment, fig. 2 shows a flowchart of a method for determining warning information, and as shown in fig. 2, the method at least includes the following steps: in step S210, candidate warning information corresponding to the target therapy is generated, and the number of candidate warning information is acquired. In the process of generating the pre-warning information, one or more target index parameters may not meet corresponding evaluation criteria, and thus one or more candidate pre-warning information may be generated. In view of this, the number of candidate early warning information may be counted to obtain the number of candidate early warning information.
In step S220, if there are a plurality of candidate early warning information, preset priorities corresponding to the candidate early warning information are obtained. When there are multiple candidate early warning information, preset priorities corresponding to the multiple candidate early warning information respectively may be further obtained, where the preset priorities may be set in advance or generated according to target index parameters of the patient, and this is not particularly limited in this exemplary embodiment.
In step S230, the preset priorities are compared, and the warning information is determined according to the comparison result.
In the present exemplary embodiment, for example, if the priority of the first candidate warning information is 1, and the priority of the second candidate warning information is 2, it may be determined that the first candidate warning information is determined as the corresponding warning information.
In the exemplary embodiment, a method for determining early warning information is provided, the comparison mode is simple, the determination mode is accurate, and the practicability is strong.
In step S150, the target therapy is adjusted according to the early warning information to perform corresponding rehabilitation training.
In an alternative embodiment, fig. 3 shows a flowchart of a method of adjusting a target therapy, as shown in fig. 3, the method comprising at least the steps of: in step S310, if the warning information is the first warning information, the frequency and/or intensity of the rehabilitation training corresponding to the target therapy is reduced. Fig. 4 shows a flow diagram of a method of adjusting a targeted therapy according to first warning information, as shown in fig. 4, the method comprising at least the steps of: in step S410, the amount of change in the target index parameter corresponding to the target therapy is acquired. For example, the target index parameter may be systolic blood pressure. Therefore, the systolic pressure of the patient can be measured separately before and after the target therapy is performed to obtain the variation of the systolic pressure. In addition, the target index parameter may be other parameters, and this exemplary embodiment is not particularly limited thereto.
In step S420, if the variation satisfies a first preset condition, the warning information is determined to be first warning information. The first preset condition may be a variation threshold preset for different target index parameters. For example, if the amount of decrease in the systolic pressure of the patient exceeds the corresponding threshold, it may be determined that the amount of change satisfies a first preset condition, and first warning information may be generated.
In step S430, a first warning report corresponding to the first warning information is generated to reduce the frequency and/or intensity of rehabilitation training according to the first warning report. In order to enable medical staff to clearly obtain and conveniently store the treatment progress condition of the patient, a corresponding first early warning report can be generated according to the first early warning information. Target index parameters and abnormal items of the patient and adjustment suggestions corresponding to the first warning information can be displayed in the first warning report, namely, the frequency and/or intensity of rehabilitation training of the patient is reduced. For example, the number of motor therapies for the patient may be reduced, or the strength of cognitive training for the patient may be reduced, etc., and this is not a particular limitation in the present exemplary embodiment.
In the exemplary embodiment, a way of adjusting the target therapy according to the first warning information is specifically described, so as to avoid physical and psychological burdens on the patient caused by too high intensity or too fast frequency of the therapy, avoid accidents occurring under the condition that medical staff do not know or prepare the therapy insufficiently, and reduce the harm to the greatest extent.
In step S320, if the warning information is the second warning information, the rehabilitation training corresponding to the target therapy is suspended. In an alternative embodiment, fig. 5 shows a flow chart of a method for adjusting a target therapy according to second warning information, as shown in fig. 5, the method at least comprises the following steps: in step S510, the amount of change in the target index parameter corresponding to the target therapy is acquired. For example, the target index parameter may be systolic blood pressure. Therefore, the systolic pressure of the patient can be measured separately before and after the target therapy is performed to obtain the variation of the systolic pressure. In addition, the target index parameter may be other parameters, and this exemplary embodiment is not particularly limited thereto.
In step S520, if the variation satisfies a second predetermined condition, the warning information is determined to be second warning information. The first preset condition may be a variation threshold preset for different target index parameters. For example, if the decrease amount of the systolic pressure of the patient exceeds the corresponding threshold value, and the patient suffers from discomfort such as chest tightness and dyspnea, it may be determined that the change amount satisfies the second preset condition, and the second warning information may be generated.
In step S530, a second warning report corresponding to the second warning information is generated to suspend rehabilitation training according to the second warning report. In order to enable medical staff to clearly obtain and conveniently store the treatment progress condition of the patient, a corresponding second early warning report can be generated according to the second early warning information. In the second warning report, target index parameters and abnormal items of the patient and adjustment suggestions corresponding to the second warning information can be displayed, namely, the rehabilitation training of the patient is suspended. The specific pause duration may be determined according to subsequent target index parameters of the patient and experience judgment of medical staff, which is not particularly limited in this exemplary embodiment.
In this exemplary embodiment, a way of adjusting the target therapy according to the second warning information is specifically described, so as to avoid burden on the patient due to rehabilitation training when the patient is not comfortable, avoid accidents when the medical care personnel do not know or prepare the patient enough, and reduce harm to the greatest extent.
In step S330, if the warning information is the third warning information, the rehabilitation training corresponding to the target therapy is terminated. In an alternative embodiment, fig. 6 shows a flow chart of a method for adjusting a target therapy according to third warning information, as shown in fig. 6, the method at least comprises the following steps: in step S610, the amount of change in the target index parameter corresponding to the target therapy is acquired. For example, the target index parameter may be systolic blood pressure. Therefore, the systolic pressure of the patient can be measured separately before and after the target therapy is performed to obtain the variation of the systolic pressure. In addition, the target index parameter may be other parameters such as pulse, which is not particularly limited in the present exemplary embodiment.
In step S620, if the variation satisfies a third preset condition, the warning information is determined to be third warning information. The first preset condition may be a variation threshold preset for different target index parameters. For example, if the decrease amount of the systolic pressure of the patient exceeds the corresponding threshold, and the pulse of the patient is above 100 times/min when the patient is quiet at the same time, or the pulse of the training process is 140 times/min at 135-.
In step S630, a third warning report corresponding to the third warning information is generated to terminate the rehabilitation training according to the third warning report. In order to enable medical staff to clearly view and conveniently store the therapy progress condition of the patient, a corresponding third early warning report can be generated according to the third early warning information. In the third warning report, target index parameters and abnormal items of the patient and adjustment suggestions corresponding to the third warning information can be displayed, that is, the rehabilitation training of the patient is stopped.
In the exemplary embodiment, the method for adjusting the target therapy according to the early warning information of different levels is specifically described, so that not only can the target index condition of the patient be known in real time, but also the medical staff can provide timely and accurate treatment measures and services.
In an alternative embodiment, fig. 7 is a flow chart illustrating a method for training a machine learning model to be trained, and as shown in fig. 7, the method at least includes the following steps: in step S710, index parameter samples and therapy samples corresponding to the index parameter samples are obtained; wherein, the machine learning model is formed based on the index parameter sample and the therapy sample training. The index parameter sample and the therapy sample corresponding to the index parameter sample may be selected from a set of target index parameters and corresponding target therapies of known corresponding therapies, which is not particularly limited in the present exemplary embodiment.
In step S720, the index parameter samples are input into a machine learning model to be trained, and a therapy output by the machine learning model to be trained and corresponding to the index parameter samples is obtained.
In step S730, if the therapy does not match the therapy sample, the parameters of the machine learning model to be trained are adjusted so that the therapy is the same as the therapy sample. After the machine learning model to be trained outputs the therapy, the therapy can be matched with the therapy sample, whether the output therapy is the same as the therapy sample or not is judged, and whether the machine learning model to be trained is trained or not is judged according to a matching result.
If the therapy is not matched with the therapy sample, the machine learning model to be trained is not trained well, so that the parameters of the machine learning model to be trained need to be adjusted, the therapy is the same as the corresponding therapy sample, and the training of the machine learning model to be trained is completed.
The accuracy of target therapy output is guaranteed through the complete training of the machine learning model, and further, the accuracy of rehabilitation training of a patient is guaranteed.
Further, in an exemplary embodiment of the present disclosure, a determination device of rehabilitation therapy is also provided. Fig. 8 shows a schematic configuration diagram of a rehabilitation therapy determination device, and as shown in fig. 8, the rehabilitation therapy determination device 800 may include: index acquisition module 810, parameter receiving module 820, therapy determination module 830, information generation module 840, and therapy adjustment module 850. Wherein:
the index obtaining module 810 is configured to obtain a target index and an object identifier, and send a parameter obtaining request carrying the object identifier; a parameter receiving module 820 configured to receive a target index parameter corresponding to a target index based on the parameter obtaining request; a therapy determination module 830 configured to input the target index parameter into a machine learning model trained in advance, so that the machine learning model outputs the target therapy; an information generating module 840 configured to obtain an evaluation criterion corresponding to a target index, and if the target index parameter does not meet the evaluation criterion, generate early warning information corresponding to a target therapy; and the therapy adjusting module is configured to adjust the target therapy according to the early warning information so as to perform corresponding rehabilitation training.
The details of the above-mentioned rehabilitation therapy determination device have been described in detail in the corresponding rehabilitation therapy determination method, and therefore, the details are not described herein.
It should be noted that although in the above detailed description several modules or units of the determination device 800 of the rehabilitation therapy are mentioned, this division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
The following describes the method for determining rehabilitation therapy in the embodiment of the present disclosure in detail with reference to an application scenario.
Fig. 9 is a flow chart illustrating a method for determining a rehabilitation therapy in an application scenario, and as shown in fig. 9, the rehabilitation system 920 may obtain a target index from the medical system 910, for example, overweight, dyslipidemia, fatty liver, elevated blood uric acid, elevated blood pressure, and elevated fasting blood glucose. In addition, other indexes may be used, and this exemplary embodiment is not particularly limited thereto. The rehabilitation system 920 may send a parameter acquisition request carrying the object identification to the data center 930. After receiving the parameter obtaining request, the data center 930 may determine a target index parameter corresponding to the target index and feed back the target index parameter to the rehabilitation system 920. The rehabilitation system 920 may input the received target index parameters into a pre-trained machine learning model and output the target therapy. The target therapy may include, for example, exercise therapy, physical therapy, task therapy, cognitive training, and physical therapy. And, the target therapy is recommended to the medical care system 910 for the medical care personnel to provide a more scientific rehabilitation training method for the patient.
Specifically, the rehabilitation system 920 obtains that the target index parameter measured by the old exceeds the normal range in three different time periods, calculates the risk of overweight or obesity of the old, recommends a target therapy for the old, and pushes the target therapy to the medical care system 910, so that the medical care personnel can perform appropriate exercise therapy for the old. After a certain follow-up exercise therapy, the old people have the condition that the systolic pressure is obviously reduced, the risk early warning mechanism reports the observed data to the early warning information of the dangerous condition to medical personnel, and the medical personnel selectively suspend the exercise therapy rehabilitation training of the old people according to the early warning information so as to reduce the harm.
Through the acquired target index parameters corresponding to the object identification, the target therapy suitable for the patient can be determined, and the basis of rehabilitation training is provided for the patient. On one hand, medical care personnel can monitor target index parameters of the patient in real time and know the health condition of the patient; on the other hand, accurate treatment measures and methods are recommended to medical staff in time, passive rescue is changed into active intervention treatment, and the method has great influence on prevention of serious diseases of patients.
It should be noted that although the above exemplary embodiment implementations describe the various steps of the method in the present disclosure in a particular order, this does not require or imply that these steps must be performed in that particular order, or that all of the steps must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
An electronic device 1000 according to such an embodiment of the invention is described below with reference to fig. 10. The electronic device 1000 shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 10, the electronic device 1000 is embodied in the form of a general purpose computing device. The components of the electronic device 1000 may include, but are not limited to: the at least one processing unit 1010, the at least one memory unit 1020, a bus 1030 connecting different system components (including the memory unit 1020 and the processing unit 1010), and a display unit 1040.
Wherein the storage unit stores program code that is executable by the processing unit 1010 to cause the processing unit 1010 to perform steps according to various exemplary embodiments of the present invention as described in the "exemplary methods" section above in this specification.
The memory unit 1020 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)1021 and/or a cache memory unit 1022, and may further include a read-only memory unit (ROM) 1023.
The electronic device 1000 may also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1000, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 1050. Also, the electronic device 1000 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1060. As shown, the network adapter 1040 communicates with other modules of the electronic device 1000 via the bus 1030. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 11, a program product 1100 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method of determining rehabilitation therapy, the method comprising:
acquiring a target index and an object identifier, and sending a parameter acquisition request carrying the object identifier;
receiving a target index parameter corresponding to the target index based on the parameter acquisition request;
inputting the target index parameters into a machine learning model trained in advance so that the machine learning model outputs a target therapy;
acquiring an evaluation standard corresponding to the target index, and if the target index parameter does not meet the evaluation standard, generating early warning information corresponding to the target therapy;
and adjusting the target therapy according to the early warning information so as to perform corresponding rehabilitation training.
2. The method of determining rehabilitation therapy according to claim 1, wherein before said inputting said target index parameter into a machine learning model trained in advance, said method further comprises:
acquiring an index parameter sample and a therapy sample corresponding to the index parameter sample; wherein the machine learning model is formed based on the index parameter samples and the therapy samples training;
inputting the index parameter sample into a machine learning model to be trained, and acquiring a therapy corresponding to the index parameter sample and output by the machine learning model to be trained;
if the therapy does not match the therapy sample, adjusting the parameters of the machine learning model to be trained so that the therapy is the same as the therapy sample.
3. The method for determining rehabilitation therapy according to claim 1, wherein the adjusting the target therapy according to the warning information for performing corresponding rehabilitation training comprises:
if the early warning information is first early warning information, reducing the frequency and/or the intensity of rehabilitation training corresponding to the target therapy;
if the early warning information is second early warning information, suspending rehabilitation training corresponding to the target therapy;
and if the early warning information is third early warning information, stopping the rehabilitation training corresponding to the target therapy.
4. The method for determining rehabilitation therapy according to claim 3, wherein if the warning information is the first warning information, reducing the frequency and/or intensity of rehabilitation training corresponding to the target therapy comprises:
acquiring the variation of the target index parameter corresponding to the target therapy;
if the variable quantity meets a first preset condition, determining that the early warning information is first early warning information;
and generating a first early warning report corresponding to the first early warning information so as to reduce the frequency and/or intensity of rehabilitation training according to the first early warning report.
5. The method for determining rehabilitation therapy according to claim 3, wherein the suspending rehabilitation training corresponding to the target therapy if the warning information is the second warning information comprises:
acquiring the variation of the target index parameter corresponding to the target therapy;
if the variable quantity meets a second preset condition, determining that the early warning information is second early warning information;
and generating a second early warning report corresponding to the second early warning information so as to suspend rehabilitation training according to the second early warning report.
6. The method for determining rehabilitation therapy according to claim 3, wherein if the warning information is third warning information, terminating the rehabilitation training corresponding to the target therapy comprises:
acquiring the variation of the target index parameter corresponding to the target therapy;
if the variable quantity meets a third preset condition, determining that the early warning information is third early warning information;
and generating a third early warning report corresponding to the third early warning information so as to terminate rehabilitation training according to the third early warning report.
7. The method of determining rehabilitation therapy according to claim 1, wherein the generating early warning information corresponding to the target therapy includes:
generating candidate early warning information corresponding to the target therapy, and acquiring the number of the candidate early warning information;
if the number of the candidate early warning information is multiple, acquiring preset priorities corresponding to the candidate early warning information;
and comparing the preset priority and determining early warning information according to a comparison result.
8. A rehabilitation therapy determining apparatus, comprising:
the index acquisition module is configured to acquire a target index and an object identifier and send a parameter acquisition request carrying the object identifier;
a parameter receiving module configured to receive a target index parameter corresponding to the target index based on the parameter obtaining request;
a therapy determination module configured to input the target index parameter into a pre-trained machine learning model to cause the machine learning model to output a target therapy;
the information generation module is configured to acquire an evaluation standard corresponding to the target index, and if the target index parameter does not meet the evaluation standard, generate early warning information corresponding to the target therapy;
and the therapy adjusting module is configured to adjust the target therapy according to the early warning information so as to perform corresponding rehabilitation training.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of determining a rehabilitation therapy according to any one of claims 1-7.
10. An electronic device, comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of determining a rehabilitation therapy of any one of claims 1-7 via execution of the executable instructions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911106979.XA CN111105859A (en) | 2019-11-13 | 2019-11-13 | Method and device for determining rehabilitation therapy, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911106979.XA CN111105859A (en) | 2019-11-13 | 2019-11-13 | Method and device for determining rehabilitation therapy, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111105859A true CN111105859A (en) | 2020-05-05 |
Family
ID=70420710
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911106979.XA Pending CN111105859A (en) | 2019-11-13 | 2019-11-13 | Method and device for determining rehabilitation therapy, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111105859A (en) |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111785348A (en) * | 2020-06-30 | 2020-10-16 | 金勋杰 | Spinal function training method, device, equipment and storage medium |
| CN112086166A (en) * | 2020-09-18 | 2020-12-15 | 常州市中医医院 | Orthopedic patient skin traction nursing method and system |
| CN112309578A (en) * | 2020-11-03 | 2021-02-02 | 南通市第一人民医院 | Method and system for improving recovery efficiency of osteoporotic vertebral fracture patient |
| CN112992312A (en) * | 2021-03-30 | 2021-06-18 | 中国人民解放军空军军医大学 | Spinal cord injury rehabilitation training qualification monitoring method and system |
| CN113241166A (en) * | 2020-05-18 | 2021-08-10 | 罗姆科技股份有限公司 | Method and system for assigning patients and dynamically controlling treatment devices using artificial intelligence |
| CN113515561A (en) * | 2021-07-20 | 2021-10-19 | 苏州景昱医疗器械有限公司 | Parameter comparison method and device, electronic equipment and computer readable storage medium |
| CN113936772A (en) * | 2021-12-17 | 2022-01-14 | 北京因数健康科技有限公司 | Exercise recommendation method, device and system and computer readable storage medium |
| US11471729B2 (en) | 2019-03-11 | 2022-10-18 | Rom Technologies, Inc. | System, method and apparatus for a rehabilitation machine with a simulated flywheel |
| US11508482B2 (en) | 2019-10-03 | 2022-11-22 | Rom Technologies, Inc. | Systems and methods for remotely-enabled identification of a user infection |
| US11515021B2 (en) | 2019-10-03 | 2022-11-29 | Rom Technologies, Inc. | Method and system to analytically optimize telehealth practice-based billing processes and revenue while enabling regulatory compliance |
| US11515028B2 (en) | 2019-10-03 | 2022-11-29 | Rom Technologies, Inc. | Method and system for using artificial intelligence and machine learning to create optimal treatment plans based on monetary value amount generated and/or patient outcome |
| US11596829B2 (en) | 2019-03-11 | 2023-03-07 | Rom Technologies, Inc. | Control system for a rehabilitation and exercise electromechanical device |
| US11701548B2 (en) | 2019-10-07 | 2023-07-18 | Rom Technologies, Inc. | Computer-implemented questionnaire for orthopedic treatment |
| US11752391B2 (en) | 2019-03-11 | 2023-09-12 | Rom Technologies, Inc. | System, method and apparatus for adjustable pedal crank |
| US11756666B2 (en) | 2019-10-03 | 2023-09-12 | Rom Technologies, Inc. | Systems and methods to enable communication detection between devices and performance of a preventative action |
| US11801423B2 (en) | 2019-05-10 | 2023-10-31 | Rehab2Fit Technologies, Inc. | Method and system for using artificial intelligence to interact with a user of an exercise device during an exercise session |
| US11830601B2 (en) | 2019-10-03 | 2023-11-28 | Rom Technologies, Inc. | System and method for facilitating cardiac rehabilitation among eligible users |
| US11826613B2 (en) | 2019-10-21 | 2023-11-28 | Rom Technologies, Inc. | Persuasive motivation for orthopedic treatment |
| US11887717B2 (en) | 2019-10-03 | 2024-01-30 | Rom Technologies, Inc. | System and method for using AI, machine learning and telemedicine to perform pulmonary rehabilitation via an electromechanical machine |
| US11904207B2 (en) | 2019-05-10 | 2024-02-20 | Rehab2Fit Technologies, Inc. | Method and system for using artificial intelligence to present a user interface representing a user's progress in various domains |
| US11915815B2 (en) | 2019-10-03 | 2024-02-27 | Rom Technologies, Inc. | System and method for using artificial intelligence and machine learning and generic risk factors to improve cardiovascular health such that the need for additional cardiac interventions is mitigated |
| US11915816B2 (en) | 2019-10-03 | 2024-02-27 | Rom Technologies, Inc. | Systems and methods of using artificial intelligence and machine learning in a telemedical environment to predict user disease states |
| US11923065B2 (en) | 2019-10-03 | 2024-03-05 | Rom Technologies, Inc. | Systems and methods for using artificial intelligence and machine learning to detect abnormal heart rhythms of a user performing a treatment plan with an electromechanical machine |
| US11923057B2 (en) | 2019-10-03 | 2024-03-05 | Rom Technologies, Inc. | Method and system using artificial intelligence to monitor user characteristics during a telemedicine session |
| US11942205B2 (en) | 2019-10-03 | 2024-03-26 | Rom Technologies, Inc. | Method and system for using virtual avatars associated with medical professionals during exercise sessions |
| US11955223B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for using artificial intelligence and machine learning to provide an enhanced user interface presenting data pertaining to cardiac health, bariatric health, pulmonary health, and/or cardio-oncologic health for the purpose of performing preventative actions |
| US11955221B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for using AI/ML to generate treatment plans to stimulate preferred angiogenesis |
| US11955220B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for using AI/ML and telemedicine for invasive surgical treatment to determine a cardiac treatment plan that uses an electromechanical machine |
| US11955222B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for determining, based on advanced metrics of actual performance of an electromechanical machine, medical procedure eligibility in order to ascertain survivability rates and measures of quality-of-life criteria |
| US11950861B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | Telemedicine for orthopedic treatment |
| US11955218B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for use of telemedicine-enabled rehabilitative hardware and for encouraging rehabilitative compliance through patient-based virtual shared sessions with patient-enabled mutual encouragement across simulated social networks |
| US11961603B2 (en) | 2023-05-31 | 2024-04-16 | Rom Technologies, Inc. | System and method for using AI ML and telemedicine to perform bariatric rehabilitation via an electromechanical machine |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102473203A (en) * | 2009-07-15 | 2012-05-23 | 皇家飞利浦电子股份有限公司 | Method for automatic setting time varying parameter alert and alarm limits |
| CN102715905A (en) * | 2012-02-09 | 2012-10-10 | 嘉兴恒怡科技有限公司 | Hand and wrist rehabilitation evaluating and monitoring device and method |
| CN104189997A (en) * | 2014-07-25 | 2014-12-10 | 深圳市艾尔曼医疗电子仪器有限公司 | Intelligent feedback high-potential therapeutic apparatus and therapeutic method |
| CN105488330A (en) * | 2015-11-23 | 2016-04-13 | 北京全域医疗技术有限公司 | Remote quality control method of cloud radiotherapy |
| CN106462658A (en) * | 2014-02-27 | 2017-02-22 | 德尔格医疗系统有限责任公司 | Medical device alarm management |
| TW201725526A (en) * | 2015-09-30 | 2017-07-16 | 伊佛曼基因體有限公司 | Systems and methods for predicting treatment-regimen-related outcomes |
| CN107224678A (en) * | 2017-07-17 | 2017-10-03 | 上海联影医疗科技有限公司 | One kind treats assessment system and storage medium |
| CN109069858A (en) * | 2015-12-21 | 2018-12-21 | 医科达股份有限公司 | System and method for optimizing treatment plan |
| US20190019573A1 (en) * | 2015-03-24 | 2019-01-17 | Ares Trading S.A. | Patient care system |
| CN109509518A (en) * | 2018-10-27 | 2019-03-22 | 平安医疗健康管理股份有限公司 | Management method, server and the computer storage medium of electronic health record |
| CN109961836A (en) * | 2017-12-26 | 2019-07-02 | 深圳市智游人科技有限公司 | Workout data processing method, device, computer equipment and storage medium |
| CN110120256A (en) * | 2019-04-29 | 2019-08-13 | 吴焰南 | Intelligent acupuncture and moxibustion system |
-
2019
- 2019-11-13 CN CN201911106979.XA patent/CN111105859A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102473203A (en) * | 2009-07-15 | 2012-05-23 | 皇家飞利浦电子股份有限公司 | Method for automatic setting time varying parameter alert and alarm limits |
| CN102715905A (en) * | 2012-02-09 | 2012-10-10 | 嘉兴恒怡科技有限公司 | Hand and wrist rehabilitation evaluating and monitoring device and method |
| CN106462658A (en) * | 2014-02-27 | 2017-02-22 | 德尔格医疗系统有限责任公司 | Medical device alarm management |
| CN104189997A (en) * | 2014-07-25 | 2014-12-10 | 深圳市艾尔曼医疗电子仪器有限公司 | Intelligent feedback high-potential therapeutic apparatus and therapeutic method |
| US20190019573A1 (en) * | 2015-03-24 | 2019-01-17 | Ares Trading S.A. | Patient care system |
| TW201725526A (en) * | 2015-09-30 | 2017-07-16 | 伊佛曼基因體有限公司 | Systems and methods for predicting treatment-regimen-related outcomes |
| CN105488330A (en) * | 2015-11-23 | 2016-04-13 | 北京全域医疗技术有限公司 | Remote quality control method of cloud radiotherapy |
| CN109069858A (en) * | 2015-12-21 | 2018-12-21 | 医科达股份有限公司 | System and method for optimizing treatment plan |
| CN107224678A (en) * | 2017-07-17 | 2017-10-03 | 上海联影医疗科技有限公司 | One kind treats assessment system and storage medium |
| CN109961836A (en) * | 2017-12-26 | 2019-07-02 | 深圳市智游人科技有限公司 | Workout data processing method, device, computer equipment and storage medium |
| CN109509518A (en) * | 2018-10-27 | 2019-03-22 | 平安医疗健康管理股份有限公司 | Management method, server and the computer storage medium of electronic health record |
| CN110120256A (en) * | 2019-04-29 | 2019-08-13 | 吴焰南 | Intelligent acupuncture and moxibustion system |
Cited By (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11471729B2 (en) | 2019-03-11 | 2022-10-18 | Rom Technologies, Inc. | System, method and apparatus for a rehabilitation machine with a simulated flywheel |
| US11904202B2 (en) | 2019-03-11 | 2024-02-20 | Rom Technolgies, Inc. | Monitoring joint extension and flexion using a sensor device securable to an upper and lower limb |
| US11752391B2 (en) | 2019-03-11 | 2023-09-12 | Rom Technologies, Inc. | System, method and apparatus for adjustable pedal crank |
| US11596829B2 (en) | 2019-03-11 | 2023-03-07 | Rom Technologies, Inc. | Control system for a rehabilitation and exercise electromechanical device |
| US11541274B2 (en) | 2019-03-11 | 2023-01-03 | Rom Technologies, Inc. | System, method and apparatus for electrically actuated pedal for an exercise or rehabilitation machine |
| US11904207B2 (en) | 2019-05-10 | 2024-02-20 | Rehab2Fit Technologies, Inc. | Method and system for using artificial intelligence to present a user interface representing a user's progress in various domains |
| US11801423B2 (en) | 2019-05-10 | 2023-10-31 | Rehab2Fit Technologies, Inc. | Method and system for using artificial intelligence to interact with a user of an exercise device during an exercise session |
| US11756666B2 (en) | 2019-10-03 | 2023-09-12 | Rom Technologies, Inc. | Systems and methods to enable communication detection between devices and performance of a preventative action |
| US11915815B2 (en) | 2019-10-03 | 2024-02-27 | Rom Technologies, Inc. | System and method for using artificial intelligence and machine learning and generic risk factors to improve cardiovascular health such that the need for additional cardiac interventions is mitigated |
| US11508482B2 (en) | 2019-10-03 | 2022-11-22 | Rom Technologies, Inc. | Systems and methods for remotely-enabled identification of a user infection |
| US11515021B2 (en) | 2019-10-03 | 2022-11-29 | Rom Technologies, Inc. | Method and system to analytically optimize telehealth practice-based billing processes and revenue while enabling regulatory compliance |
| US11515028B2 (en) | 2019-10-03 | 2022-11-29 | Rom Technologies, Inc. | Method and system for using artificial intelligence and machine learning to create optimal treatment plans based on monetary value amount generated and/or patient outcome |
| US11955218B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for use of telemedicine-enabled rehabilitative hardware and for encouraging rehabilitative compliance through patient-based virtual shared sessions with patient-enabled mutual encouragement across simulated social networks |
| US11950861B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | Telemedicine for orthopedic treatment |
| US11955223B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for using artificial intelligence and machine learning to provide an enhanced user interface presenting data pertaining to cardiac health, bariatric health, pulmonary health, and/or cardio-oncologic health for the purpose of performing preventative actions |
| US11942205B2 (en) | 2019-10-03 | 2024-03-26 | Rom Technologies, Inc. | Method and system for using virtual avatars associated with medical professionals during exercise sessions |
| US11923057B2 (en) | 2019-10-03 | 2024-03-05 | Rom Technologies, Inc. | Method and system using artificial intelligence to monitor user characteristics during a telemedicine session |
| US11923065B2 (en) | 2019-10-03 | 2024-03-05 | Rom Technologies, Inc. | Systems and methods for using artificial intelligence and machine learning to detect abnormal heart rhythms of a user performing a treatment plan with an electromechanical machine |
| US11915816B2 (en) | 2019-10-03 | 2024-02-27 | Rom Technologies, Inc. | Systems and methods of using artificial intelligence and machine learning in a telemedical environment to predict user disease states |
| US11955222B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for determining, based on advanced metrics of actual performance of an electromechanical machine, medical procedure eligibility in order to ascertain survivability rates and measures of quality-of-life criteria |
| US11830601B2 (en) | 2019-10-03 | 2023-11-28 | Rom Technologies, Inc. | System and method for facilitating cardiac rehabilitation among eligible users |
| US11955221B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for using AI/ML to generate treatment plans to stimulate preferred angiogenesis |
| US11955220B2 (en) | 2019-10-03 | 2024-04-09 | Rom Technologies, Inc. | System and method for using AI/ML and telemedicine for invasive surgical treatment to determine a cardiac treatment plan that uses an electromechanical machine |
| US11887717B2 (en) | 2019-10-03 | 2024-01-30 | Rom Technologies, Inc. | System and method for using AI, machine learning and telemedicine to perform pulmonary rehabilitation via an electromechanical machine |
| US11701548B2 (en) | 2019-10-07 | 2023-07-18 | Rom Technologies, Inc. | Computer-implemented questionnaire for orthopedic treatment |
| US11826613B2 (en) | 2019-10-21 | 2023-11-28 | Rom Technologies, Inc. | Persuasive motivation for orthopedic treatment |
| CN113241166A (en) * | 2020-05-18 | 2021-08-10 | 罗姆科技股份有限公司 | Method and system for assigning patients and dynamically controlling treatment devices using artificial intelligence |
| CN113241166B (en) * | 2020-05-18 | 2023-03-10 | 罗姆科技股份有限公司 | Method and system for assigning patients and dynamically controlling treatment devices using artificial intelligence |
| CN111785348A (en) * | 2020-06-30 | 2020-10-16 | 金勋杰 | Spinal function training method, device, equipment and storage medium |
| CN112086166A (en) * | 2020-09-18 | 2020-12-15 | 常州市中医医院 | Orthopedic patient skin traction nursing method and system |
| CN112086166B (en) * | 2020-09-18 | 2022-11-15 | 常州市中医医院 | Orthopedic patient skin traction nursing method and system |
| CN112309578A (en) * | 2020-11-03 | 2021-02-02 | 南通市第一人民医院 | Method and system for improving recovery efficiency of osteoporotic vertebral fracture patient |
| CN112992312B (en) * | 2021-03-30 | 2023-06-06 | 中国人民解放军空军军医大学 | Qualified monitoring method and system for spinal cord injury rehabilitation training |
| CN112992312A (en) * | 2021-03-30 | 2021-06-18 | 中国人民解放军空军军医大学 | Spinal cord injury rehabilitation training qualification monitoring method and system |
| CN113515561B (en) * | 2021-07-20 | 2023-12-12 | 苏州景昱医疗器械有限公司 | Parameter comparison method, device, electronic equipment and computer readable storage medium |
| CN113515561A (en) * | 2021-07-20 | 2021-10-19 | 苏州景昱医疗器械有限公司 | Parameter comparison method and device, electronic equipment and computer readable storage medium |
| US11957960B2 (en) | 2021-08-06 | 2024-04-16 | Rehab2Fit Technologies Inc. | Method and system for using artificial intelligence to adjust pedal resistance |
| CN113936772A (en) * | 2021-12-17 | 2022-01-14 | 北京因数健康科技有限公司 | Exercise recommendation method, device and system and computer readable storage medium |
| US11961603B2 (en) | 2023-05-31 | 2024-04-16 | Rom Technologies, Inc. | System and method for using AI ML and telemedicine to perform bariatric rehabilitation via an electromechanical machine |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111105859A (en) | Method and device for determining rehabilitation therapy, storage medium and electronic equipment | |
| Alberdi et al. | Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review | |
| JP6294946B2 (en) | System, computer medium, and computer-implemented method for monitoring and improving biometric health of employees | |
| JP6298500B2 (en) | Floor mat system and associated computer media for monitoring and improving employee health and productivity, and computer-implemented methods | |
| JP6049711B2 (en) | Chair pad system and associated computer medium and computer-implemented method for monitoring and improving employee health and productivity | |
| JP6018631B2 (en) | Systems, computer media, and computer-implemented methods for monitoring and improving employee health and productivity | |
| JP2019057301A (en) | Systems, computer media and computer-implemented methods for monitoring and improving cognitive and emotive health of employees | |
| Pierleoni et al. | An android-based heart monitoring system for the elderly and for patients with heart disease | |
| US20160038043A1 (en) | Assessing Effectiveness of CPR | |
| US20160089038A1 (en) | System, method, and computer program product for the real-time mobile evaluation of physiological stress | |
| JP6001068B2 (en) | System, computer medium, and computer-implemented method for teaching employees based on health status monitored using an avatar | |
| EP3079571A1 (en) | Methods and systems for arrhythmia tracking and scoring | |
| JP2017097903A (en) | System for monitoring and improving biomechanical health of employee, computer medium, and method to be executed by computer | |
| KR20200071647A (en) | Biofeedback method based on virtual/augmented reality contents and bio-signal for diagnosis and healing of mental illness | |
| JP2014523039A (en) | System, computer medium and computer-implemented method for monitoring and improving biomechanical health of employees | |
| Dosani et al. | A vibro-tactile display for clinical monitoring: real-time evaluation | |
| Sannino et al. | Short term heart rate variability to predict blood pressure drops due to standing: a pilot study | |
| KR20150102880A (en) | System and method for depression monitoring based on user information | |
| Solomon et al. | Quantifying energy and fatigue: classification and assessment of energy and fatigue using subjective, objective, and mixed methods towards health and quality of life | |
| US20210308415A1 (en) | Massage apparatus, system and method capable of deriving a parameter of an individual | |
| Khan et al. | Severe analysis of cardiac disease detection using the wearable device by artificial intelligence | |
| WO2022187686A1 (en) | System and method for personalized biofeedback from a wearable device | |
| Anwar | Bipolar disorder predictive model: A study to analyze and predict emotional change using physiological signals | |
| CN112086164A (en) | Physical condition feedback method, system and storage medium | |
| Amira et al. | Stress level classification using heart rate variability |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |