CN113593670A - Prescription generation method and system for household direct current stimulation medical equipment - Google Patents
Prescription generation method and system for household direct current stimulation medical equipment Download PDFInfo
- Publication number
- CN113593670A CN113593670A CN202110898759.6A CN202110898759A CN113593670A CN 113593670 A CN113593670 A CN 113593670A CN 202110898759 A CN202110898759 A CN 202110898759A CN 113593670 A CN113593670 A CN 113593670A
- Authority
- CN
- China
- Prior art keywords
- user
- prescription
- treatment
- sample
- treated
- 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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- 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
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Physical Education & Sports Medicine (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention provides a prescription generation method and a system of household direct current stimulation medical equipment, and relates to the technical field of medical equipment, wherein the prescription generation method of the household direct current stimulation medical equipment comprises the following steps: establishing a prescription database, wherein the prescription database is used for storing the relevant information and treatment prescriptions of a plurality of sample users and the relevant information and treatment prescriptions of a plurality of sample diseases; acquiring relevant information of a user to be treated, wherein the relevant information of the user to be treated comprises type information of a disease to be treated; judging whether at least one sample user corresponding to the user to be treated exists in a prescription database or not based on the relevant information of the user to be treated and the relevant information of a plurality of sample users; if so, generating a recommended prescription based on the treatment prescription of the at least one sample user; if not, acquiring the corresponding sample diseases based on the type information of the diseases to be treated and the related information of the multiple sample diseases, and taking the treatment prescriptions of the sample diseases as recommended prescriptions.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a prescription generating method and system of household direct current stimulation medical equipment.
Background
The direct current stimulation medical equipment gradually enters all hospitals in China, and practice proves that the direct current stimulation medical equipment has obvious deterministic effect on treating diseases such as cerebral apoplexy later-stage rehabilitation, depression, insomnia and the like. The household direct current stimulation medical equipment is popular with a plurality of patients who are inconvenient to go to hospital for medical treatment or receive further electrical stimulation treatment at home after being discharged, is low in price and fills the gap of the market due to the small size, the lightness and the capability of providing stable discharge.
However, patients receiving treatment at home do not have a doctor prescribed as in a hospital, and emergencies while receiving treatment can be corrected in time. The household direct current stimulation medical equipment needs a current value and treatment time set by a patient, is not guided by people, cannot take good rescue measures when the current burns, and is easy to cause personal safety problems and cause medical disputes.
Therefore, it is desirable to provide a prescription generating method and system for a household direct current stimulation medical device, which can automatically prescribe a proper prescription for each patient, improve the treatment effect of the medical device, and avoid personal safety accidents as much as possible.
Disclosure of Invention
The invention aims to provide a prescription generation method and a prescription generation system of household direct current stimulation medical equipment, which have the advantage of effectively and safely managing the entrance and exit of an oil and gas field station.
The embodiment of the invention is realized by the following steps:
one of the embodiments of the present specification provides a prescription generation method for a household direct current stimulation medical device, including:
establishing a prescription database, wherein the prescription database is used for storing the related information and treatment prescriptions of a plurality of sample users and the related information and treatment prescriptions of a plurality of sample diseases;
acquiring relevant information of a user to be treated, wherein the relevant information of the user to be treated comprises type information of a disease to be treated;
judging whether at least one sample user corresponding to the user to be treated exists in the prescription database or not based on the relevant information of the user to be treated and the relevant information of the plurality of sample users;
if yes, generating a recommended prescription based on the treatment prescription of at least one sample user;
if not, acquiring a sample disease corresponding to the type of the disease to be treated of the user to be treated based on the information of the type of the disease to be treated and the related information of the multiple sample diseases, and taking a treatment prescription of the sample disease as a recommended prescription.
In some embodiments of the present invention, the information related to the user to be treated includes an age of the user to be treated, a disease state of the user to be treated, and a gender of the user to be treated, and the information related to the sample user includes an age of the sample user, a disease state of the sample user, and a gender of the sample user.
In some embodiments of the present invention, the determining whether at least one sample user corresponding to the user to be treated exists in the prescription database based on the relevant information of the user to be treated and the relevant information of the plurality of sample users includes:
for each sample user, calculating the user similarity of the relevant information of the user to be treated and the relevant information of the sample user based on the age of the user to be treated, the illness state and the sex of the user to be treated, the age of the sample user, the illness state of the sample user and the sex of the sample user;
and judging whether at least one sample user corresponding to the user to be treated exists in the prescription database or not based on the user similarity.
In some embodiments of the invention, the sample user's treatment prescription includes the sample user's primary and secondary treatment prescriptions;
the generating a recommended prescription based on the treatment prescription of at least one of the sample users comprises:
judging whether at least one primary treatment prescription corresponding to at least one sample user exists in the prescription database, if so, generating a recommended prescription based on at least one primary treatment prescription;
if not, generating a recommended prescription based on at least one secondary treatment prescription corresponding to at least one sample user.
In some embodiments of the invention, the treatment prescription includes a treatment time and a treatment current;
said generating a recommended prescription based on at least one of said primary treatment prescriptions, comprising:
calculating a mean treatment time based on the treatment times of at least one of the primary treatment prescriptions;
calculating a mean treatment current value based on the treatment current of at least one primary treatment prescription;
generating a recommended prescription based on the treatment time mean and the treatment current mean.
In some embodiments of the invention, the generating a recommended prescription based on at least one of the secondary treatment prescriptions corresponding to at least one of the sample users comprises:
calculating a mean treatment time based on the treatment times of at least one of the secondary treatment prescriptions;
calculating a mean treatment current value based on the treatment current of at least one of the secondary treatment prescriptions;
generating recommended treatment time based on the treatment time mean value and a preset time adjustment value;
generating a recommended treatment current based on the treatment current mean value and a preset current adjustment value;
and generating a recommended prescription based on the recommended treatment time and the recommended treatment current.
In some embodiments of the invention, the generating a recommended prescription based on the recommended treatment time and the recommended treatment current comprises:
judging whether the recommended treatment time exceeds a treatment time threshold value, if so, generating a recommended prescription based on the treatment time threshold value, and if not, generating the recommended prescription based on the recommended treatment time;
and judging whether the recommended treatment current exceeds a treatment current threshold value, if so, generating a recommended prescription based on the treatment current threshold value, and if not, generating the recommended prescription based on the recommended treatment current.
In some embodiments of the present invention, the calculating the user similarity between the information related to the user to be treated and the information related to the sample user includes:
for each sample user, calculating age similarity based on the age of the user to be treated and the age of the sample user, calculating gender similarity based on the gender of the user to be treated and the gender of the sample user, and calculating illness similarity based on the illness state of the user to be treated and the illness state of the sample user;
calculating the user similarity based on the age similarity, the gender similarity, and the condition similarity.
In some embodiments of the invention, further comprising:
based on the user similarity, sequencing the plurality of sample users to generate a sequencing result;
and determining at least one sample user corresponding to the user to be treated based on the sequencing result.
One of the embodiments of the present specification provides a prescription generation system for a home-use direct current stimulation medical device, including:
the prescription database is used for storing the related information of a plurality of sample users, the related information of a plurality of sample diseases, the treatment prescriptions of the plurality of sample users and the treatment prescriptions of the plurality of sample diseases;
the information acquisition module is used for acquiring the relevant information of the user to be treated, wherein the relevant information of the user to be treated comprises the type information of the disease to be treated;
and the recommended prescription generating module is used for judging whether at least one sample user corresponding to the user to be treated exists in the prescription database or not based on the related information of the user to be treated and the related information of the plurality of sample users, if so, generating a recommended prescription based on the treatment prescription of the at least one sample user, if not, acquiring the sample disease corresponding to the type of the disease to be treated of the user to be treated based on the type information of the disease to be treated and the related information of the plurality of sample diseases, and taking the treatment prescription of the sample disease as the recommended prescription.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
1) the invention can generate the corresponding recommended prescription for each user to be treated, and has the advantage of improving the treatment effect of the medical equipment;
2) the invention can avoid overlong treatment time and overlarge treatment current of medical equipment and has the advantage of avoiding personal safety accidents as much as possible.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic block diagram of a prescription generation system for a home-use DC electro-stimulation medical device according to some embodiments of the present invention;
fig. 2 is a schematic flow chart of a prescription generation method of a household direct current stimulation medical device according to some embodiments of the present disclosure;
FIG. 3 is a flow diagram of generating a recommended prescription based on a treatment prescription of at least one sample user provided by some embodiments of the present description;
FIG. 4 is a flow diagram providing for generating a recommended prescription based on at least one primary treatment prescription according to some embodiments of the present description;
FIG. 5 is a flow diagram of generating a recommended prescription based on at least one secondary therapy prescription corresponding to at least one sample user provided by some embodiments of the present description;
FIG. 6 is a flow diagram of generating a recommended prescription based on a recommended treatment time and a recommended treatment current provided by some embodiments of the present description;
fig. 7 is a block diagram of a prescription generating system of a household direct current stimulation medical device according to some embodiments of the present invention.
In the figure, 100-prescription generating system of household direct current stimulation medical equipment; 110-a processing device; 120-a network; 130-terminal device; 140-a storage device; 200-a prescription generating method of household direct current stimulation medical equipment; 700-prescription generating system of household direct current stimulation medical equipment; 710-prescription database; 720-information acquisition module; 730-recommended recipe Generation Module.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "device," "unit," and/or "module" as used herein is a system for distinguishing different components, elements, components, parts, or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a system or apparatus may include other steps or elements.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the process elements and sequences are described in this specification, the use of alphanumeric characters, or other designations, is not intended to limit the order of the processes and systems described in this specification, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. However, this system of disclosure does not imply that more features are required than are recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Examples
Fig. 1 is a schematic block diagram of a prescription generation system 100 of a home-use electrical direct current stimulation medical device according to some embodiments of the present invention.
As shown in fig. 1, a prescription generating system 100 for a home-use direct current stimulation medical device may include a processing device 110, a network 120, a terminal device 130, and a storage device 140.
In some embodiments, the prescription generating system 100 of a home-use direct current stimulation medical device may provide assistance in patient treatment. In some embodiments, the prescription generating system 100 of the home-use dc stimulation medical device can be applied in the medical device for automatically making an appropriate prescription for each patient, thereby improving the therapeutic effect of the medical device. For example, the prescription generating system 100 of a home-use direct current stimulation medical device may be applied to a transcranial direct current stimulator that automatically prescribes an appropriate prescription for each patient, which regulates cerebral cortical neuron activity with constant, low-intensity direct current according to the prescription. It should be noted that the prescription generating system 100 of the home-use dc electrical stimulation medical device may also be applied to other devices, scenarios and applications that require treatment of a patient, and is not limited herein, and any device, scenario and/or application that may use the prescription generating method 200 of the home-use dc electrical stimulation medical device included in the present application is within the scope of the present application.
In some embodiments, the processing device 110 may be used to process information and/or data related to the generation of a prescription for a home dc electrical stimulation medical device. For example, processing device 110 may establish prescription database 710, prescription database 710 for storing information and treatment prescriptions associated with a plurality of sample users, information and treatment prescriptions associated with a plurality of sample diseases; the processing device 110 may also obtain relevant information of the user to be treated, where the relevant information of the user to be treated includes information of the type of disease to be treated; processing device 110 may further determine whether at least one sample user corresponding to the user to be treated exists in prescription database 710 based on the relevant information of the user to be treated and the relevant information of the plurality of sample users; if so, the processing device 110 may also generate a recommended prescription based on the treatment prescription of the at least one sample user; if not, the processing device 110 may further obtain a sample disease corresponding to the type of the disease to be treated of the user to be treated based on the information of the type of the disease to be treated and the related information of the plurality of sample diseases, and use a treatment prescription of the sample disease as a recommended prescription.
In some embodiments, the processing device 110 may be regional or remote. For example, processing device 110 may access information and/or material stored in terminal device 130 and storage device 140 via network 120. In some embodiments, processing device 110 may interface directly with terminal device 130 and storage device 140 to access information and/or material stored therein. In some embodiments, the processing device 110 may execute on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like.
In some embodiments, the processing device 110 may comprise a processor. The processor may process data and/or information related to employee training management to perform one or more of the functions described herein. For example, the processor may receive a prescription generation request sent by the terminal device 130. For another example, the processor may obtain information and treatment prescriptions related to a plurality of sample users, information and treatment prescriptions related to a plurality of sample diseases, stored by the storage device 140. In some embodiments, a processor may include one or more sub-processors (e.g., a single core processing device or a multi-core processing device). Merely by way of example, a processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, and the like or any combination thereof.
In some embodiments, the terminal device 130 may obtain information or data in the prescription generating system 100 of the home dc-stimulated medical device. In some embodiments, the user may obtain the recommended prescription generated by the processing device 110 through the terminal device 130, for example, the user may view the recommended prescription generated by the processing device 110 through the terminal device 130, and the terminal device 130 may prompt the user for the recommended prescription generated by the processing device 110 in a voice broadcast manner.
In some embodiments, the terminal device 130 may include one or any combination of a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, and the like. In some embodiments, the mobile device 130-1 may include a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, smart footwear, smart glasses, smart helmet, smart watch, smart clothing, smart backpack, smart accessory, smart handle, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a POS device, and the like, or any combination thereof. In some embodiments, the metaverse device and/or the augmented reality device may include metaverse helmets, metaverse glasses, metaverse eyewear, augmented reality helmets, augmented reality glasses, augmented reality eyewear, and the like, or any combination thereof.
In some embodiments, storage device 140 may be connected to network 120 to enable communication with one or more components of prescription generating system 100 (e.g., processing device 110, terminal device 130, etc.) of a home dc-stimulated medical device. One or more components of the prescription generating system 100 of the home dc-stimulated medical device may access data or instructions stored in the storage device 140 via the network 120. In some embodiments, the storage device 140 may be directly connected to or in communication with one or more components (e.g., processing device 110, terminal device 130) in the prescription generating system 100 of the home dc-stimulated medical device. In some embodiments, the storage device 140 may be part of the processing device 110. In some embodiments, the processing device 110 may also be located in the terminal device 130.
It should be noted that the foregoing description is provided for illustrative purposes only, and is not intended to limit the scope of the present application. Many variations and modifications will occur to those skilled in the art in light of the teachings herein. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the storage device 140 may be a data storage device comprising a cloud computing platform, such as a public cloud, a private cloud, a community and hybrid cloud, and the like. However, such changes and modifications do not depart from the scope of the present application.
Fig. 2 is a flowchart illustrating a method 200 for generating a prescription of a home-use electrical stimulation medical device according to some embodiments of the present disclosure.
As shown in fig. 2, a prescription generation method 200 for a household direct current stimulation medical device may include:
In some embodiments, the related information of the sample user may include at least one of a name, an age, a condition of the sample user, and a gender of the sample user. In some embodiments, the information related to the plurality of sample diseases may include at least one of a name, a type, and the like of the sample disease.
In some embodiments, the related information of the user to be treated may include at least one of a name, an age, a condition of the user to be treated, and a gender of the user to be treated.
In some embodiments, it may be determined whether there is at least one sample user corresponding to the user to be treated in the prescription database 710 based on at least one of the name, age, condition of the sample user, and gender of the sample user and at least one of the name, age, condition of the user to be treated, and gender of the user to be treated. For example, when the name of the sample user a coincides with the name of the user to be treated, the sample user a may be regarded as the sample user corresponding to the user to be treated.
In some embodiments, determining whether there is at least one sample user corresponding to the user to be treated in the prescription database 710 based on the relevant information of the user to be treated and the relevant information of the plurality of sample users may include: for each sample user, based on the age of the user to be treated, the illness state and the sex of the user to be treated, the age of the sample user, the illness state of the sample user and the sex of the sample user, the user similarity of the relevant information of the user to be treated and the relevant information of the sample user is calculated.
In some embodiments, for each sample user, an age similarity is calculated based on the age of the user to be treated and the age of the sample user, a gender similarity is calculated based on the gender of the user to be treated and the gender of the sample user, and a condition similarity is calculated based on the condition of the user to be treated and the condition of the sample user.
In some embodiments, the age similarity may be calculated based on the age of the user to be treated and the age of the sample user by the following formula,
wherein, the age interval takes the value of 0 if the age interval takes the value of [0, 6) years; the age interval takes the value of [6, 10) years, then takes the value of 1; the age interval takes the value of [10, 18) years, then takes the value of 2; the age interval takes the value of [18, 30) years, then takes the value of 3; the age interval takes the value of [40, 50) years, then takes the value of 4; the age interval takes the value of [50, 60) years, then takes the value of 5; the age interval takes the value of [60, 70) years, then takes the value of 6; the age interval takes the value of [70, 80) years, then takes the value of 7; the age interval is [80, + ∞) years, then 8.
In some embodiments, when the gender of the sample user and the gender of the user to be treated are both male, the gender similarity is 1; when the gender of the sample user and the gender of the user to be treated are both female, the gender similarity is 1; when one of the sex of the sample user and the sex of the user to be treated is male, and the other of the sex of the sample user and the sex of the user to be treated is female, the sex similarity is 0.
In some embodiments, the similarity of the disease conditions may be calculated based on the disease conditions of the user to be treated and the disease conditions of the sample user by the following formula,
wherein, the disease degree of the user is scored by the user through questions and answers, and the value is in the interval [1,10 ].
In some embodiments, the user similarity may be calculated based on the age similarity, the gender similarity, and the condition similarity, wherein the user similarity is the age similarity, the condition similarity. In some embodiments, it may be determined whether the sample user is a sample user corresponding to the user to be treated based on the user similarity, for example, if the user similarity between the sample user a and the user to be treated is 0.8 and is greater than a preset threshold (e.g., 0.7), the sample user a is the sample user corresponding to the user to be treated.
In some embodiments, when the number of sample users corresponding to the user to be treated is greater than a preset threshold (e.g., 10), a plurality of sample users may be ranked based on the user similarity, and a ranking result may be generated; and determining at least one sample user corresponding to the user to be treated based on the sequencing result. For example, when the number of sample users corresponding to the user to be treated is 12, the 12 sample users may be ranked from high to low according to the user similarity, and the ten sample users located before the eleventh position may be taken as the sample users corresponding to the user to be treated.
If so, a recommended prescription is generated based on the treatment prescription of the at least one sample user.
In some embodiments, the sample user's treatment prescription includes a sample user's primary treatment prescription and a secondary treatment prescription, wherein the primary treatment prescription can be a better treatment prescription and the secondary treatment prescription can be a general treatment prescription.
With reference to fig. 3, fig. 3 is a flow chart of generating a recommended prescription based on a treatment prescription of at least one sample user according to some embodiments of the present disclosure, and in some embodiments, generating a recommended prescription based on a treatment prescription of at least one sample user may include:
and step 330, if not, generating a recommended prescription based on at least one secondary treatment prescription corresponding to at least one sample user.
Referring to fig. 4, fig. 4 is a flow chart of generating a recommended prescription based on at least one primary treatment prescription according to some embodiments of the present disclosure, which may include a treatment time and a treatment current in some embodiments. Generating a recommended prescription based on the at least one primary treatment prescription may include:
For example, the sample users corresponding to the users to be treated include a sample user a, a sample user B, and a sample user C, where the treatment time of the primary treatment prescription of the sample user a is 30 minutes, the treatment time of the primary treatment prescription of the sample user B is 50 minutes, and the treatment time of the primary treatment prescription of the sample user C is 40 minutes, and then the treatment time average is (30+50+40)/3 is 40 minutes; the treatment current prescribed for the primary treatment of the sample user a is 100mA, the treatment current prescribed for the primary treatment of the sample user B is 120mA, the treatment current prescribed for the primary treatment of the sample user C is 110mA, and the treatment current mean value is (100+120+110)/3 is 110mA, then the recommended prescription may include the treatment time as the treatment time mean value, i.e., 40 minutes, and the treatment current as the treatment current mean value, i.e., 110 mA.
With reference to fig. 5, fig. 5 is a schematic flow chart of generating a recommended prescription based on at least one secondary therapy prescription corresponding to at least one sample user according to some embodiments of the present description, where in some embodiments, generating a recommended prescription based on at least one secondary therapy prescription corresponding to at least one sample user includes:
For example, the sample users corresponding to the users to be treated include a sample user a, a sample user B, and a sample user C, the treatment time of the secondary treatment prescription of the sample user a is 30 minutes, the treatment time of the secondary treatment prescription of the sample user B is 50 minutes, the treatment time of the secondary treatment prescription of the sample user C is 40 minutes, the treatment time average is (30+50+40)/3 is 40 minutes, and the preset time adjustment value is 10 minutes, and the recommended treatment time average is the treatment time average + the preset time adjustment value is 40 minutes +10 minutes is 50 minutes; the treatment current of the secondary treatment prescription of the sample user a is 100mA, the treatment current of the secondary treatment prescription of the sample user B is 120mA, the treatment current of the secondary treatment prescription of the sample user C is 110mA, the treatment current mean value is (100+120+110)/3 is 110mA, the preset current adjustment value is 10mA, the recommended treatment current mean value is the treatment current mean value + the preset current adjustment value is 110mA +10mA is 120mA, the recommended prescription may include the treatment time as the recommended treatment time, i.e., 50 minutes, and the treatment current as the recommended treatment current, i.e., 120 mA.
With reference to fig. 6, fig. 6 is a schematic flow chart of generating a recommended prescription based on a recommended treatment time and a recommended treatment current according to some embodiments of the present disclosure, where in some embodiments, generating the recommended prescription based on the recommended treatment time and the recommended treatment current may include:
step 630, if not, generating a recommended prescription based on the recommended treatment time;
if not, a recommended prescription is generated based on the recommended treatment current, step 660.
For example, the recommended treatment current is 120mA, which is greater than the treatment current threshold (e.g., 110mA), then the recommended prescribed treatment current is the treatment current threshold, i.e., 110 mA. Also for example, if the recommended treatment current is 120mA, which is less than the treatment current threshold (e.g., 150mA), then the recommended prescribed treatment current is the recommended treatment current, i.e., 120 mA. Also for example, if the recommended treatment time is 50 minutes and is greater than a treatment time threshold (e.g., 40 minutes), then the recommended prescribed treatment time is the treatment time threshold, i.e., 40 minutes. Also for example, if the recommended treatment time is 50 minutes and is less than a treatment time threshold (e.g., 60 minutes), then the recommended prescribed treatment time is the recommended treatment time, i.e., 50 minutes.
And step 250, if not, acquiring the sample diseases corresponding to the types of the diseases to be treated of the user to be treated based on the information of the types of the diseases to be treated and the related information of the various sample diseases, and taking the treatment prescriptions of the sample diseases as recommended prescriptions.
In some embodiments, a sample disease corresponding to the type of the disease to be treated of the user to be treated may be obtained based on the type of the disease to be treated of the user to be treated and the types of the plurality of sample diseases, and a treatment prescription for the sample disease may be taken as the recommended prescription. For example, when the type of the disease to be treated of the user to be treated coincides with the types of the plurality of sample diseases, the sample disease may be regarded as a sample disease corresponding to the type of the disease to be treated of the user to be treated.
Referring to fig. 7, fig. 7 is a block diagram illustrating a prescription generating system 700 of a home-use electrical stimulation medical device according to some embodiments of the present invention.
As shown in fig. 7, a prescription generation system 700 for a home-use dc electrical stimulation medical device may include: a prescription database 710, an information acquisition module 720, and a recommended prescription generation module 730.
The prescription database 710 may be used to store information related to a plurality of sample users, information related to a plurality of sample diseases, prescriptions for treatment of a plurality of sample users, and prescriptions for treatment of a plurality of sample diseases.
The information obtaining module 720 may be configured to obtain relevant information of the user to be treated, where the relevant information of the user to be treated includes information of types of diseases to be treated.
The recommended prescription generating module 730 may be configured to determine whether at least one sample user corresponding to the user to be treated exists in the prescription database 710 based on the related information of the user to be treated and the related information of the plurality of sample users, if yes, generate a recommended prescription based on the treatment prescription of the at least one sample user, if no, obtain a sample disease corresponding to the type of the disease to be treated of the user to be treated based on the information of the type of the disease to be treated and the related information of the plurality of sample diseases, and use the treatment prescription of the sample disease as the recommended prescription.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A prescription generating method for a household direct current stimulation medical device, comprising:
establishing a prescription database, wherein the prescription database is used for storing the related information and treatment prescriptions of a plurality of sample users and the related information and treatment prescriptions of a plurality of sample diseases;
acquiring relevant information of a user to be treated, wherein the relevant information of the user to be treated comprises type information of a disease to be treated;
judging whether at least one sample user corresponding to the user to be treated exists in the prescription database or not based on the relevant information of the user to be treated and the relevant information of the plurality of sample users;
if yes, generating a recommended prescription based on the treatment prescription of at least one sample user;
if not, acquiring a sample disease corresponding to the type of the disease to be treated of the user to be treated based on the information of the type of the disease to be treated and the related information of the multiple sample diseases, and taking a treatment prescription of the sample disease as a recommended prescription.
2. The prescription generating method for household DC stimulation medical equipment as claimed in claim 1, wherein the information related to the user to be treated comprises the age of the user to be treated, the medical condition of the user to be treated and the sex of the user to be treated, and the information related to the sample user comprises the age of the sample user, the medical condition of the sample user and the sex of the sample user.
3. The prescription generating method of a household direct current stimulation medical device according to claim 2, wherein the determining whether at least one sample user corresponding to the user to be treated exists in the prescription database based on the information related to the user to be treated and the information related to the plurality of sample users comprises:
for each sample user, calculating the user similarity of the relevant information of the user to be treated and the relevant information of the sample user based on the age of the user to be treated, the illness state and the sex of the user to be treated, the age of the sample user, the illness state of the sample user and the sex of the sample user;
and judging whether at least one sample user corresponding to the user to be treated exists in the prescription database or not based on the user similarity.
4. The prescription generating method of a domestic d.c. electro-stimulation medical device as claimed in any of the claims 1-3, wherein the treatment prescription of the sample user comprises a primary treatment prescription and a secondary treatment prescription of the sample user;
the generating a recommended prescription based on the treatment prescription of at least one of the sample users comprises:
judging whether at least one primary treatment prescription corresponding to at least one sample user exists in the prescription database, if so, generating a recommended prescription based on at least one primary treatment prescription;
if not, generating a recommended prescription based on at least one secondary treatment prescription corresponding to at least one sample user.
5. The prescription generating method of a domestic d.c. electro-stimulation medical device as claimed in claim 4, wherein said treatment prescription comprises a treatment time and a treatment current;
said generating a recommended prescription based on at least one of said primary treatment prescriptions, comprising:
calculating a mean treatment time based on the treatment times of at least one of the primary treatment prescriptions;
calculating a mean treatment current value based on the treatment current of at least one primary treatment prescription;
generating a recommended prescription based on the treatment time mean and the treatment current mean.
6. The prescription generating method of a domestic d.c. electro-stimulation medical device according to claim 4, wherein said generating a recommended prescription based on at least one said secondary therapy prescription corresponding to at least one said sample user comprises:
calculating a mean treatment time based on the treatment times of at least one of the secondary treatment prescriptions;
calculating a mean treatment current value based on the treatment current of at least one of the secondary treatment prescriptions;
generating recommended treatment time based on the treatment time mean value and a preset time adjustment value;
generating a recommended treatment current based on the treatment current mean value and a preset current adjustment value;
and generating a recommended prescription based on the recommended treatment time and the recommended treatment current.
7. The prescription generating method of a domestic dc electrical stimulation medical device according to claim 6, wherein said generating a recommended prescription based on said recommended treatment time and said recommended treatment current comprises:
judging whether the recommended treatment time exceeds a treatment time threshold value, if so, generating a recommended prescription based on the treatment time threshold value, and if not, generating the recommended prescription based on the recommended treatment time;
and judging whether the recommended treatment current exceeds a treatment current threshold value, if so, generating a recommended prescription based on the treatment current threshold value, and if not, generating the recommended prescription based on the recommended treatment current.
8. The prescription generating method for household DC stimulation medical equipment as claimed in claim 3, wherein the calculating the user similarity of the information related to the user to be treated and the information related to the sample user comprises:
for each sample user, calculating age similarity based on the age of the user to be treated and the age of the sample user, calculating gender similarity based on the gender of the user to be treated and the gender of the sample user, and calculating illness similarity based on the illness state of the user to be treated and the illness state of the sample user;
calculating the user similarity based on the age similarity, the gender similarity, and the condition similarity.
9. The prescription generating method of a domestic dc electrical stimulation medical device according to claim 8, further comprising:
based on the user similarity, sequencing the plurality of sample users to generate a sequencing result;
and determining at least one sample user corresponding to the user to be treated based on the sequencing result.
10. A prescription generating system for a domestic dc stimulating medical device, comprising:
the prescription database is used for storing the related information of a plurality of sample users, the related information of a plurality of sample diseases, the treatment prescriptions of the plurality of sample users and the treatment prescriptions of the plurality of sample diseases;
the information acquisition module is used for acquiring the relevant information of the user to be treated, wherein the relevant information of the user to be treated comprises the type information of the disease to be treated;
and the recommended prescription generating module is used for judging whether at least one sample user corresponding to the user to be treated exists in the prescription database or not based on the related information of the user to be treated and the related information of the plurality of sample users, if so, generating a recommended prescription based on the treatment prescription of the at least one sample user, if not, acquiring the sample disease corresponding to the type of the disease to be treated of the user to be treated based on the type information of the disease to be treated and the related information of the plurality of sample diseases, and taking the treatment prescription of the sample disease as the recommended prescription.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110898759.6A CN113593670A (en) | 2021-08-05 | 2021-08-05 | Prescription generation method and system for household direct current stimulation medical equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110898759.6A CN113593670A (en) | 2021-08-05 | 2021-08-05 | Prescription generation method and system for household direct current stimulation medical equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113593670A true CN113593670A (en) | 2021-11-02 |
Family
ID=78255577
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110898759.6A Pending CN113593670A (en) | 2021-08-05 | 2021-08-05 | Prescription generation method and system for household direct current stimulation medical equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113593670A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934226A (en) * | 2017-03-02 | 2017-07-07 | 成都华信高科医疗器械有限责任公司 | A kind of data management system and method based on electro photoluminescence terminal |
| CN107656952A (en) * | 2016-12-30 | 2018-02-02 | 青岛中科慧康科技有限公司 | The modeling method of parallel intelligent case recommended models |
| CN108198631A (en) * | 2018-02-05 | 2018-06-22 | 南昌医软科技有限公司 | Evidence-based medical outcome generation method and device |
| CN108744275A (en) * | 2018-06-22 | 2018-11-06 | 暨南大学 | A kind of wearable transcutaneous electrical stimulation medical apparatus for treating menalgia |
| CN109493940A (en) * | 2018-11-06 | 2019-03-19 | 大国创新智能科技(东莞)有限公司 | Prescription personalized recommendation method and system based on deep learning and knowledge base |
| CN110880361A (en) * | 2019-10-16 | 2020-03-13 | 平安科技（深圳）有限公司 | Personalized accurate medication recommendation method and device |
| CN110880362A (en) * | 2019-11-12 | 2020-03-13 | 南京航空航天大学 | Large-scale medical data knowledge mining and treatment scheme recommending system |
| CN111816309A (en) * | 2020-07-13 | 2020-10-23 | 国家康复辅具研究中心 | Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning |
-
2021
- 2021-08-05 CN CN202110898759.6A patent/CN113593670A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107656952A (en) * | 2016-12-30 | 2018-02-02 | 青岛中科慧康科技有限公司 | The modeling method of parallel intelligent case recommended models |
| CN106934226A (en) * | 2017-03-02 | 2017-07-07 | 成都华信高科医疗器械有限责任公司 | A kind of data management system and method based on electro photoluminescence terminal |
| CN108198631A (en) * | 2018-02-05 | 2018-06-22 | 南昌医软科技有限公司 | Evidence-based medical outcome generation method and device |
| CN108744275A (en) * | 2018-06-22 | 2018-11-06 | 暨南大学 | A kind of wearable transcutaneous electrical stimulation medical apparatus for treating menalgia |
| CN109493940A (en) * | 2018-11-06 | 2019-03-19 | 大国创新智能科技(东莞)有限公司 | Prescription personalized recommendation method and system based on deep learning and knowledge base |
| CN110880361A (en) * | 2019-10-16 | 2020-03-13 | 平安科技（深圳）有限公司 | Personalized accurate medication recommendation method and device |
| CN110880362A (en) * | 2019-11-12 | 2020-03-13 | 南京航空航天大学 | Large-scale medical data knowledge mining and treatment scheme recommending system |
| CN111816309A (en) * | 2020-07-13 | 2020-10-23 | 国家康复辅具研究中心 | Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Amatya et al. | Rehabilitation for people with multiple sclerosis: an overview of Cochrane Reviews | |
| Vora et al. | Home-based exercise system for patients using IoT enabled smart speaker | |
| KR20190107905A (en) | Hospital reservation recommendation method and device | |
| Bao et al. | Automatically evaluating balance: a machine learning approach | |
| Hao et al. | Effects of virtual reality-based telerehabilitation for stroke patients: A systematic review and meta-analysis of randomized controlled trials | |
| McReynolds et al. | Psychosocial adjustment to multiple sclerosis: Implications for rehabilitation professionals | |
| US20210098109A1 (en) | Virtual community system for elderly health service based on big data technology | |
| Summerfield et al. | Cost-effectiveness considerations in pediatric cochlear implantation | |
| CN107273705A (en) | The determination of human body implantation type medical treatment device parameter, method to set up and equipment | |
| CN113593670A (en) | Prescription generation method and system for household direct current stimulation medical equipment | |
| CN112116971A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN109255257A (en) | A kind of organ donation matching process and system based on block chain | |
| JP6206891B1 (en) | Psychotherapy device, psychotherapy system, and psychotherapy method | |
| US20060106436A1 (en) | Method and apparatus of providing wavefront color therapy | |
| Nautiyal et al. | Role of digital healthcare in the well-being of elderly people: a systematic review | |
| Babins | A humanistic approach to old-old people: A general model | |
| Nguyen et al. | Quantifying patient experiences with therapeutic neurorehabilitation technologies: a scoping review | |
| CN109616222A (en) | Information interacting method and system | |
| Hofmann et al. | Treating avoidant personality disorder: The case of Paul | |
| Adachi | Study of a cognitive effects model in Dohsa‐hou for the elderly | |
| TRIEF | Childhood abuse and chronic pain: Observations from clinical practice | |
| Rezk et al. | Tele-rehabilitation for upper limb functions of children with hemiplegic cerebral palsy during Covid pandemic: Randomized controlled trial | |
| Newgent et al. | Single session treatment of nontraumatic fear of flying with eye movement desensitization reprocessing: Pre and post-September 11 | |
| CN115376677A (en) | Old age care mode recommendation system | |
| Stummer | An update on the use of contracting |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211102 |