Method and system for determining disease intervention scheme based on group treatment template

Abstract

The invention provides a method and system for determining a disease intervention scheme based on group treatment templates, and belongs to the technical field of digital therapies, the method comprises the following steps: obtaining a plurality of group treatment templates; based on the plurality of group treatment templates, matching a proper group treatment template as a disease intervention scheme of the patient according to indexes of the patient, the plurality of acquired group treatment templates comprising basic information of the group treatment templates and recommended matching condition setting, wherein the recommended matching condition is used for matching with an index of the patient, and when the index of the patient accords with the recommended matching condition of a current group treatment template, the current group treatment template is used as a disease intervention scheme of the patient; the system comprises a group treatment template generation subsystem and a disease intervention scheme matching subsystem. According to the method, a more targeted disease intervention scheme can be determined according to the indexes of the patient, the treatment effect can be effectively improved, and the treatment quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of digital therapeutics, and particularly to a method and a system for determining a disease intervention plan based on a group treatment template. Background Art

[0002] Digital Therapeutics (DTx) is a subset of digital health and is an evidence-based therapeutic intervention driven by high-quality software programs for preventing, managing, or treating medical disorders or diseases. It can be broadly defined as a treatment method that uses digital technologies and Internet-based health technologies to stimulate patients' behavior changes. Digital therapeutics can break through the limitations of traditional drug treatments and present treatment and management plans in the form of application software through digital means to achieve intervention and treatment. The application software can be used remotely, is simple, convenient, and has good interactivity, and can effectively improve the treatment rate of related diseases. Whether it is for Alzheimer's disease, insomnia, anxiety, depression, autism, or attention deficit hyperactivity disorder, digital therapeutics can enable patients to perform a large amount of active intervention and training at home. Compared with traditional therapies, digital therapeutics can significantly reduce the economic burden on patients and reduce the time required for patients to visit doctors, thus providing a more cost-effective treatment.

[0003] Currently, when treating patients with digital therapeutics, a general treatment plan is usually adopted. However, due to the differences in the conditions and physiological characteristics of different patients, the treatment lacks pertinence and the treatment effect is not good. Summary of the Invention

[0004] In view of the above problems, in order to enable patients to obtain a more targeted and accurate digital therapeutics disease intervention plan, the present invention provides a method and a system for determining a disease intervention plan based on a group treatment template. By matching the indicators of patients with multiple group treatment templates, a disease intervention plan that more conforms to the treatment characteristics of patients can be obtained, which helps to improve the treatment effect of patients.

[0005] In a first aspect, the present invention provides a method for determining a disease intervention plan based on a group treatment template, which is implemented through the following technical solutions.

[0006] A method for determining a disease intervention plan based on a group treatment template includes:

[0007] Obtaining a plurality of group treatment templates;

[0008] Based on the plurality of group treatment templates, matching a suitable group treatment template according to the indicators of the patient as the disease intervention plan of the patient.

[0009] As a further improvement of the present invention, the obtaining of multiple group treatment templates includes the basic information of the group treatment templates and the setting of recommended matching conditions, where the recommended matching conditions are used to match with the indicators of the patient, and when the indicators of the patient meet the recommended matching conditions of the current group treatment template, the current group treatment template is used as the disease intervention plan for the patient.

[0010] As a further improvement of the present invention, the setting of the basic information of the group treatment templates in the obtaining of multiple group treatment templates includes the group treatment template name, target name, intervention means, and label setting, where the target is the part of the human body or the substance in the human body affected by the intervention means, and the label is the severity of the disease.

[0011] As a further improvement of the present invention, the setting of the recommended matching conditions of the group treatment templates includes obtaining indicators and defining the conditional range of the indicators. When the indicator data of the patient is within the conditional range of the indicators, the group treatment template including the recommended matching conditions that meet the indicator data of the patient is used as the disease intervention plan for the patient.

[0012] As a further improvement of the present invention, the obtaining of multiple group treatment templates includes determining disease intervention means according to the causes of different populations, and the intervention means includes at least one sub-intervention means.

[0013] As a further improvement of the present invention, the sub-intervention means includes drug intervention, device intervention, information intervention, physical factor intervention, and software intervention.

[0014] As a further improvement of the present invention, the configuration of the group treatment template adopts JSON structured data.

[0015] As a further improvement of the present invention, before performing the matching of the appropriate group treatment template according to the indicators of the patient based on multiple group treatment templates as the disease intervention plan for the patient, the JSON structured data is converted into Groovy script data.

[0016] In a second aspect, the present invention provides a system for determining a disease intervention plan based on group treatment templates, which is implemented through the following technical solutions.

[0017] A system for determining a disease intervention plan based on group treatment templates includes:

[0018] A group treatment template generation subsystem for generating multiple standard group treatment templates for different populations;

[0019] A disease intervention plan matching subsystem, which is used to match the patient's indicators with multiple standard group treatment templates generated by the group treatment template generation subsystem, and select a suitable group treatment template as the patient's disease intervention plan.

[0020] As a further improvement of the present invention,

[0021] The group treatment template generation subsystem includes:

[0022] An etiology analysis and identification module, which is used to identify the etiologies of different populations and determine the targets affected by digital therapy;

[0023] A disease intervention means determination module, which is used to determine the treatment intervention means of the disease according to the etiologies identified by the etiology analysis and identification module and the targets of digital therapy determined;

[0024] A recommended matching condition setting module, which is used to obtain indicators and set the recommended matching conditions of the group treatment template according to the indicators, defined operators and target values;

[0025] The disease intervention plan matching subsystem includes:

[0026] A patient data collection and acquisition module, which is used to collect the patient's real indicator data and save it;

[0027] A group treatment template matching module, which is used to match the patient indicator data collected by the patient data collection and acquisition module with multiple group treatment templates generated by the group treatment template generation subsystem, and obtain a group treatment template that meets the conditions from multiple group treatment templates.

[0028] The method and system for determining the basic intervention plan based on the group treatment template of the present invention first obtain multiple standard group treatment templates for different populations based on epidemiological research data and clinical data, and each group treatment template can be used for the disease intervention treatment of the corresponding population; when treating a patient, according to the real indicator data of the patient himself collected and obtained, it is matched with multiple standard group treatment templates, and a group treatment template suitable for the patient is matched from multiple standard group treatment templates, and the successfully matched group treatment template is recommended as the patient's disease intervention plan for the patient's intervention treatment, so that the finally obtained disease intervention treatment plan is more in line with the patient's own condition, and can more specifically carry out intervention treatment on the patient, which can significantly improve the treatment effect; at the same time, since the method of automatically matching through the patient's indicators to obtain a suitable disease intervention treatment plan is adopted, the time for formulating the intervention plan is also shortened. Description of the Drawings

[0029] Figure 1It is a schematic flowchart of the method for determining a disease intervention plan based on a group treatment template according to Embodiment 1 of the present invention.

[0030] Figure 2 It is a schematic flowchart of the method for determining a disease intervention plan based on a group treatment template according to Embodiment 2 of the present invention.

[0031] Figure 3 It is an example diagram of the method for determining a disease intervention plan based on a group treatment template according to Embodiment 2 of the present invention.

[0032] Figure 4 It is another example diagram of the method for determining a disease intervention plan based on a group treatment template according to Embodiment 2 of the present invention.

[0033] Figure 5 It is a schematic diagram of the system for determining a disease intervention plan based on a group treatment template according to Embodiment 3 of the present invention.

[0034] Explanation of reference numerals: 1. Group treatment template generation subsystem; 11. Etiology analysis and identification module; 12. Disease intervention means determination module; 13. Recommended matching condition setting module; 14. Data conversion module; 2. Disease intervention plan matching subsystem; 21. Patient data collection and acquisition module; 22. Group treatment template matching module; 23. Treatment preference setting module; 24. Matching degree calculation module. Detailed implementation manners

[0035] The following combines specific embodiments and attached Figures 1-5 to make a detailed description of the invention, so that those skilled in the art can more fully understand the purpose, features and effects of the present invention.

[0036] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present invention belongs. When the definition of a term in this specification conflicts with the meaning commonly understood by those skilled in the art to which the present invention belongs, the definition in this document shall prevail.

[0037] The present invention provides a method and a system for determining a disease intervention plan based on a group treatment template, which improve the existing digital therapy technology. By setting a group treatment template, when intervening in the treatment of patients, a suitable group treatment template for the patients can be quickly selected according to the indicators of the patients and recommended to doctors, helping doctors provide more accurate digital disease intervention treatment plans for patients, improving the quality and efficiency of treatment plan formulation, and enabling patients to receive effective treatment.

[0038] Embodiment 1

[0039] As a specific embodiment of the present invention, this embodiment provides a method for determining a disease intervention plan based on a group treatment template, referring to Figure 1 , the specific steps are as follows:

[0040] S1. Obtain multiple group treatment templates

[0041] Based on epidemiological research data and clinical data, configure treatment plans and obtain multiple standard group treatment templates, where each group treatment template corresponds to a standard disease intervention plan for digital therapy.

[0042] In this embodiment, S1 obtains multiple group treatment templates for different populations by giving the best intervention treatment means or combinations of intervention treatment means for different populations.

[0043] S2. Based on the multiple group treatment templates, match a suitable group treatment template according to the patient's indicators as the patient's disease intervention plan

[0044] Before treating the patient, collect and obtain the patient's indicator data, match the patient's indicator data with the obtained multiple group treatment templates. When the patient's indicator data meets the conditions of the group treatment template, it means a successful match, and the matched group treatment template is used as the disease intervention plan for the patient to be treated, and the patient is intervened and treated using this group treatment template.

[0045] When formulating a disease intervention plan using the method for determining a disease intervention plan based on a group treatment template of the present invention, multiple group treatment templates for different populations are pre-configured. When treating a patient, a group treatment template suitable for the patient is automatically matched as the patient's disease intervention plan, so that a more targeted digital therapy treatment plan can be quickly determined. Since the determined treatment plan has high accuracy, it helps to improve the treatment effect.

[0046] Embodiment 2

[0047] As a specific embodiment of the present invention, this embodiment provides a method for determining a disease intervention plan based on a group treatment template, referring to Figure 2 , the specific steps are as follows:

[0048] S1. Obtain multiple group treatment templates

[0049] Based on epidemiological research data and clinical data, configure treatment plans and obtain multiple standard group treatment templates, where each group treatment template corresponds to a standard disease intervention plan for digital therapy.

[0050] The configuration of the group treatment template includes basic information configuration and recommended matching condition configuration. The basic information includes the group treatment template name, target name, intervention means, label, such asFigure 3 as shown

[0051] Specifically, obtaining multiple group treatment templates includes:

[0052] S11. Etiology analysis and identification

[0053] Identify the etiology of the patient. Based on the identified etiology of the patient, determine the target of the digital therapy. The target in digital therapy represents a key substance in the human body pathway, or can also be a certain part or organ of the human body. For example, the target of the S-curve digital therapy is the spine, the target of the diabetes digital therapy is glycated hemoglobin (HbA1c), and the target of the attention deficit and hyperactivity disorder (ADHD) digital therapy is dopamine. When using digital therapy to intervene in a patient, the target will change, thus affecting the disease treatment process. The purpose of using the intervention means to intervene in the patient is to prevent, relieve or eliminate the abnormal target through the intervention means, so that the human body maintains a healthy balance state.

[0054] S12. Determine the disease intervention means

[0055] According to the etiology identified in S11 and the determined target of the digital therapy, determine the treatment intervention means for the disease. The intervention of digital therapy can be used alone with drugs and devices, or can be combined with other means. The intervention means is a combination of sub-intervention means. Specifically, the intervention means includes at least one sub-intervention means, where the intervention means includes drug intervention, device intervention, information intervention, physical factor intervention, software intervention. The information intervention can be one or several sub-intervention means among text intervention, picture intervention, and video intervention. The physical factor intervention can be one or several sub-intervention means among sound intervention, light intervention, current intervention, and magnetic field intervention. When using digital therapy, use one sub-intervention means or a combination of sub-intervention means to exert an influence on the patient, and then treat the disease corresponding to the corresponding target by promoting the change of the target.

[0056] Through the above methods, obtain the best sub-intervention means or a combination of sub-intervention means of the group treatment template for diseases with different targets and different severities. For example, referring to Figure 3 , in a group treatment template for spinal S-curve treatment, the name of the group treatment template is S-curve treatment, the target name is the spine, the intervention means is a combination of 4 sub-intervention means: Complet melatonin tablets, S-curve training, additional training video, and popular science introduction of spinal monitoring data, and the label is mild.

[0057] It should be noted that in each group treatment template, multiple label groups can be configured, and multiple labels are configured for each label group, that is, each group treatment template can include several labels, and only one label can be selected as the label of the group treatment template in each label group.

[0058] By allowing each group treatment template to configure multiple tag groups, and each tag group to configure multiple tags, flexible configuration of the treatment template is achieved, which can meet the treatment needs of different groups, allows different tag groups and the tags under the tag groups to be selected under different circumstances, and improves flexibility and applicability.

[0059] Each group treatment template includes multiple tag groups, and only one tag can be selected in each tag group, enabling flexible combination between different tags and tag groups. For a specific group treatment template, one tag of the corresponding tag group can be selected in each tag group, or only in some tag groups, meeting complex treatment needs.

[0060] By restricting that only one tag can be selected in each tag group, it can be ensured that the tag selection of each tag group of each group treatment template is unique, avoiding data redundancy and contradictions.

[0061] When determining disease intervention means, combinations that can be combined together and can promote the treatment effect with each other according to medical research and clinical data are determined as combinations of sub-intervention means, and sub-intervention means that cannot be used simultaneously or inhibit the treatment effect with each other are not used as combinations of sub-intervention means in the group treatment template.

[0062] Furthermore, this embodiment further includes:

[0063] S13. Setting recommended matching conditions

[0064] The purpose of setting recommended conditions is that only when the patient's index data meets the recommended matching conditions, the current group treatment template that meets the conditions is recommended to the patient as the patient's disease intervention plan. Otherwise, when the patient's index data does not meet the recommended matching conditions, the current group treatment is not recommended to the patient.

[0065] For example, in a group treatment template for the treatment of spinal S curvature, the name of the group treatment template is S curvature treatment, the target name is the spine, the intervention means is a combination of 4 sub-intervention means including Complet melatonin tablets, S curvature training, additional training videos, and popular science introduction of spinal monitoring data, and the tag is mild;

[0066] The recommended matching condition is that when the scoliosis type contains S, the matching is successful, and the group treatment template is recommended as the patient's disease intervention plan, and the patient is intervened and treated with a combination of 4 sub-intervention means including Complet melatonin tablets, S curvature training, additional training videos, and popular science introduction of spinal monitoring data.

[0067] In this embodiment, different population characteristics are distinguished by using recommendation matching conditions and tags. Treatment plans are determined for different groups according to the digital therapy targets and the recommendation matching conditions, and multiple group treatment templates are obtained.

[0068] Specifically, in this embodiment, the configuration method of the recommendation conditions is as follows:

[0069] Obtain the indicators, the operators and target values that define the indicators. The indicator refers to the digital expression of the target. After determining the target of the digital therapy, the target is digitally described by using the indicator. Its function is to describe the disease that the digital therapy wants to express, and the human conditions concerned by the disease are digitally expressed through the indicator.

[0070] The operator represents a mathematical operator. For example, LT means less than, LE means less than or equal to, EQ means equal to, NE means not equal to, GE means greater than or equal to, and BETWEEN means between two values. Its function is to compare the target value with the real physical index data of each patient, match the patient to a specific group treatment template, and thus recommend a suitable treatment plan for the patient.

[0071] The target value represents the standard value corresponding to the indicator in different group treatment templates and serves as the comparison object for the real index data of the patient. By setting the target value, the quantitative evaluation of the indicator can be realized.

[0072] In a specific example, a fasting blood glucose of 110 - 126 mg / dl indicates impaired fasting glucose. At this time, the target value of the indicator "impaired fasting glucose" is 110 - 126. When the patient's own indicator data is within the range of 110 - 126, the patient will be matched to this group treatment template and this treatment template will be used as the disease intervention plan for the patient.

[0073] After completing the configuration of the group treatment template, the digital therapy is released, and at the same time, the configuration data is saved in the PostgreSQL database to realize the digitalization of the group treatment template configuration data.

[0074] In a specific example, the JSON structure data is used for the configuration of the group treatment template, as shown below:

[0075] {"dtxId":825,"id":7,"name":"S-shaped curve treatment","targetPointids":

[12] ,"meansPids":[32,29,31,30],"tags":"Mild","triggerCondition":"{\"combinator\":\"and\",\"conditions\":[{\"combinator\":\"and\",\"conditions\":[{\"fieldName\":\"scoliosis type.value\",\"operator\":\"CONTAINS\",\"field\":

[0076] \"indicator.side_bending_type.value\",\"value\":[[\"S\"]],\"type\":\"STRING\"}]}]}"}

[0077] Among them, "dtxId" represents the digital therapy id, "targetPointids" represents the set of selected target point ids, "meansPids" represents the list of selected sub-intervention means, and "tags" represents the label.

[0078] triggerCondition represents the matching condition. In the above example, it means that when the value of the indicator "scoliosis type" contains "S", the condition is met. The data type of the indicator is "STRING". When this matching condition is met, the group treatment template is triggered.

[0079] Use JSON format data to describe the matching conditions of the group treatment template, which has the characteristics of easy maintenance and extensibility, facilitating page rendering and constructing a visual configuration page.

[0080] When the digital therapy is released, the system converts the JSON format data into Groovy script data for storage, which is convenient for the script execution engine provided by JDK to execute the Groovy script data.

[0081] When performing data format conversion, use the indicator parameter as the formal parameter and the target value in the JSON format matching condition as the value to be compared. Through string concatenation and replacement, the replaced part is indicator.side_bending_type.value, generating a script that can be recognized by the computer.

[0082] By taking the index parameter as a formal parameter and the target value in the JSON - formatted matching condition as the value to be compared, and using the method of string concatenation and replacement, a computer - recognizable script is generated, realizing the automatic conversion of data format from JSON format to groovy script, which improves the degree of automation and efficiency of data processing.

[0083] The data instance of the converted groovy script is as follows:

[0084] import com.cwdata.dtx.engine.Conditions boolean func(Map<String,Object>obj){return

[0085] ((Conditions.textCondition(obj.get('indicator.side_bending_type.value',null)as String,'CONTAINS',["S"])))}func(obj)

[0086] In another embodiment, the JSON - formatted data can also be converted into a JavaScript script.

[0087] S2. Based on multiple group treatment templates, match a suitable group treatment template according to the patient's indicators as the disease intervention plan for the patient

[0088] Before treating the patient, collect and obtain the patient's own real indicator data, then save it to the database. Match the patient's indicator data with the obtained multiple group treatment templates, traverse the group treatment templates. When the patient's indicator data meets the conditions of the current group treatment template, it means a successful match, and the matched current group treatment template is used as the disease intervention plan for the patient to be treated, and the traversal ends.

[0089] Furthermore, after collecting and obtaining the patient's own real indicator data, determine at least one treatment preference based on the patient's indicators, where the treatment preference is obtained according to the labels of the group treatment templates. Since the treatment preferences correspond to the labels of the group treatment templates, multiple treatment preferences form a treatment preference group. When determining, one treatment preference from one treatment preference group can be selected, or one treatment preference from each treatment preference group in multiple treatment preference groups can be selected.

[0090] By collecting and obtaining the patient's own real indicator data, combining the labels of the group treatment template, and selecting the treatment preference that suits the patient's actual situation based on the doctor's experience, the doctor can determine the personalized treatment preference for the patient.

[0091] When determining the treatment preference, one treatment preference in a treatment preference group can be selected, or one treatment preference in each of multiple treatment preference groups can be selected respectively to form different treatment preference combinations, realizing the diversification and flexibility of treatment preference determination.

[0092] By obtaining the treatment preference according to the label of the group treatment template, the consistency between the treatment preference and the label is ensured.

[0093] Specifically, based on the doctor's observation of the patient's indicators, at least one treatment preference is selected and determined. Then, according to the patient's indicator data and treatment preference, a suitable group treatment template is matched, including:

[0094] S21. Match the group treatment template of the patient

[0095] Query and obtain the patient's own real indicator data value from the database, convert the indicator data into Map format data, use the indicator code as the key and the specific value of the patient's indicator as the value, and put it into the Map container. In S1, since the indicator of the groovy script data is in Map format, it is necessary to convert the obtained patient's indicator data into Map format data.

[0096] Converting the patient's own real indicator data value obtained from the database query into Map format data realizes the conversion of data format, enables the data to be processed and operated more flexibly, and improves the availability and operability of the data.

[0097] Pass the patient's indicator data converted into Map format and the groovy script of the digital therapy as actual parameters to the script execution engine. After traversing, the return result is obtained: true or false. Since the Groovy script returns a bool type, the output result is of bool type. If the execution engine returns true, it means that the patient's indicator data conforms to the current group treatment template and the matching is successful; if the execution engine returns false, it means that the patient's indicator data does not conform to the current group treatment template, and the matching of the next group treatment template is carried out.

[0098] The patient index data converted into the Map format and the Groovy script of the digital therapy are passed as arguments to the script execution engine, realizing the transfer of data and script, enabling the effective association and processing of data and script, and improving the operability and flexibility. Through the return result of the script execution engine, it can be judged whether the patient index data conforms to the current group treatment template, realizing the intelligent matching and judgment of the relationship between patient data and the group treatment template, and improving the matching efficiency and accuracy.

[0099] S22. Calculate the matching degree between the determined treatment preference and the label of the obtained group treatment template

[0100] In this embodiment, when calculating the matching degree between the treatment preference and the label of the matching group treatment template, the number of labels in the determined treatment preference that are the same as those in the obtained group treatment is a, and the number of labels configured in all group treatment templates is b. The matching degree k is calculated in the following way:

[0101]

[0102] Since the selected treatment preferences are different, there may be multiple matching labels. The more the number of matching labels, the higher the matching degree score, indicating that the group treatment template is more suitable for the patient.

[0103] Refer to Figure 4 , in an example, the treatment preferences include mild, moderate, and severe. When the selected treatment preference is mild, the matching degree k between the treatment preference and the label of the group treatment template obtained in S21 is 100%, indicating that the determined treatment preference corresponds exactly to the label of the obtained group treatment template.

[0104] Determine the final disease intervention plan according to the group treatment template that meets the conditions matched from multiple group treatment templates in S21 and the matching degree between the S22 treatment preference and the label of the obtained group treatment template.

[0105] Specifically, there may be multiple group treatment templates that meet the conditions matched in S21. For each group treatment template, there are differences in its matching degree k. Therefore, it can be determined which group treatment template's plan to finally select for disease intervention treatment of the patient according to the level of the matching degree k.

[0106] The method for determining the disease intervention means based on the group treatment template of the present invention only needs to collect and obtain the real index data of the patient to match the most suitable group treatment template for the patient among the configured multiple standard group treatment templates, and give specific intervention treatment means or combinations of means, providing comprehensive and scientific decision-making reference support for doctors to formulate more targeted treatment plans, and enabling doctors to determine the final treatment plan faster and more accurately.

[0107] Example 3

[0108] As a specific embodiment of the present invention, this embodiment provides a system for determining a disease intervention plan based on a group treatment template. Referring to Figure 5 , it includes a group treatment template generation subsystem 1 and a disease intervention plan matching subsystem 2.

[0109] Among them,

[0110] The group treatment template generation subsystem 1 is used to generate multiple standard group treatment templates for different populations. Each group treatment template includes at least basic information and recommended matching conditions. The basic information includes the group treatment template name, target name, intervention means, and label;

[0111] The disease intervention plan matching subsystem 2 is used to match the patient's indicators to a suitable group treatment template among the multiple standard group treatment templates generated by the group treatment template generation subsystem 1 as the patient's disease intervention plan.

[0112] Specifically, the group treatment template generation subsystem 1 includes:

[0113] The etiology analysis and identification module 11 is used to identify the etiology of different populations and then determine the targets affected by digital therapy. For example, the target of the S-curve digital therapy is corresponding to the spine, the target of the diabetes digital therapy is corresponding to glycated hemoglobin, and the target of the attention deficit and hyperactivity disorder digital therapy is corresponding to dopamine;

[0114] The disease intervention means determination module 12 is used to determine the treatment intervention means of the disease according to the etiology identified by the etiology analysis and identification module 11 and the determined targets of digital therapy. The intervention means includes at least one sub-intervention means, such as including drugs, devices, information, physical factors, and software intervention;

[0115] The recommended matching condition setting module 13 is used to obtain indicators and set the recommended matching conditions of the group treatment template according to the indicators, defined operators, and target values.

[0116] The group treatment template generation subsystem 1 of this embodiment gives the best intervention treatment means or a combination of intervention treatment means for different populations, and obtains multiple group treatment templates for different populations.

[0117] The disease intervention plan matching subsystem 2 includes:

[0118] The patient data collection and acquisition module 21 is used to collect the real indicator data of the patient and save it;

[0119] The population treatment template matching module 22 is used to match the patient index data collected by the patient data acquisition module 21 with the population treatment templates generated by the population treatment template generation subsystem 1, and match the population treatment template that meets the conditions from multiple population treatment templates.

[0120] In the disease intervention plan matching subsystem 2 of this embodiment, after collecting and obtaining the patient index data, the index data is matched with multiple standard population treatment templates generated in the population treatment template generation subsystem 1. When the patient index data meets the conditions of one of the population treatment templates, the population treatment template is returned and used as the recommended plan for the patient's disease intervention plan.

[0121] In another specific embodiment, the population treatment template generation subsystem 1 further includes a data conversion module 14, which is used to convert the recommended matching conditions set in JSON format into groovy script data.

[0122] In another specific embodiment, the disease intervention plan matching subsystem 2 further includes a treatment preference setting module 23 and a matching degree calculation module 24, where:

[0123] The treatment preference setting module 23 is used to set treatment preferences according to the population treatment templates obtained in the population treatment template generation subsystem 1. Specifically, the corresponding treatment preferences are set using the labels of the population treatment templates. For example, according to the degree of illness, the treatment preferences are set to mild, moderate, and severe.

[0124] The matching degree calculation module 24 is used to calculate the matching degree between the treatment preferences selected by the treatment preference setting module 23 and the labels of the population treatment templates matched by the population treatment template matching module 22.

[0125] Specifically, there may be multiple population treatment templates that meet the conditions matched by the population treatment template matching module 22. The matching degree calculation module 24 calculates the matching degree of each population treatment template, and pushes the population treatment template with the highest matching degree to the doctor as the disease intervention treatment plan for the patient.

[0126] The system for determining a disease intervention plan based on a population treatment template of the present invention uses the population treatment template generation subsystem 1 to generate multiple standard population treatment templates for different populations. After obtaining the patient index data, the disease intervention plan matching subsystem 2 matches the patient's index data with multiple population treatment templates, so as to obtain a population treatment template that meets the patient index data, and uses this population treatment template as the disease intervention plan for the patient for intervention treatment, making the formulation of the intervention treatment plan more targeted and helping to improve the treatment effect.

[0127] The above are only the preferred embodiments of the present invention, and are not any other form of limitation to the present invention. Any modification or equivalent change made according to the technical essence of the present invention still belongs to the scope protected by the present invention.

Claims

1. A method for determining a disease intervention plan based on a group treatment template, characterized in that, The method includes: Obtaining a plurality of group treatment templates; Based on the plurality of group treatment templates, matching a suitable group treatment template according to the patient's indicators as the patient's disease intervention plan.

2. The method for determining a disease intervention plan based on a group treatment template according to claim 1, wherein The obtaining of the plurality of group treatment templates includes the setting of the basic information and recommended matching conditions of the group treatment templates, wherein the recommended matching conditions are used to match with the patient's indicators, and when the patient's indicators meet the recommended matching conditions of the current group treatment template, the current group treatment template is used as the patient's disease intervention plan.

3. The method for determining a disease intervention plan based on a group treatment template according to claim 2, wherein The setting of the basic information of the group treatment templates in the obtaining of the plurality of group treatment templates includes the group treatment template name, target name, intervention means, and label setting, wherein the target is the part of the human body or the substance in the human body affected by the intervention means, and the label is the severity of the disease.

4. The method for determining a disease intervention plan based on a group treatment template according to claim 2, wherein, The setting of the recommended matching conditions of the group treatment templates includes obtaining indicators and defining the condition range of the indicators. When the patient's indicator data is within the condition range of the indicators, the group treatment template including the recommended matching conditions that meet the patient's indicator data is used as the patient's disease intervention plan.

5. The method for determining a disease intervention plan based on a group treatment template according to claims 1-4, characterized in that, The obtaining of the plurality of group treatment templates includes determining disease intervention means according to the causes of different populations, and the intervention means includes at least one sub-intervention means.

6. The method for determining a disease intervention plan based on a group treatment template according to claim 5, wherein The sub-intervention means includes drug intervention, device intervention, information intervention, physical factor intervention, and software intervention.

7. The method for determining a disease intervention plan based on a group treatment template according to claim 1, wherein The configuration of the group treatment templates adopts JSON structured data.

8. The method for determining a disease intervention plan based on a group treatment template according to claim 7, wherein, Before performing the step of matching a suitable group treatment template according to the patient's indicators as the patient's disease intervention plan based on the plurality of group treatment templates, the JSON structured data is converted into Groovy script data.

9. A determination system for a disease intervention plan based on a group treatment template, characterized in that, The system includes: A group treatment template generation subsystem for generating a plurality of standard group treatment templates for different populations; A disease intervention plan matching subsystem for matching a suitable group treatment template from the plurality of standard group treatment templates generated by the group treatment template generation subsystem according to the patient's indicators as the patient's disease intervention plan.

10. The system for determining a disease intervention plan based on group treatment templates according to claim 9, wherein The group treatment template generation subsystem includes: A cause analysis and identification module for identifying the causes of different populations and then determining the targets affected by digital therapy; A disease intervention means determination module for determining the treatment intervention means of the disease according to the causes identified by the cause analysis and identification module and the determined targets of digital therapy; A recommended matching condition setting module for obtaining indicators and setting the recommended matching conditions of the group treatment templates according to the indicators and the defined operators and target values; The disease intervention plan matching subsystem includes: A patient data collection and acquisition module for collecting and saving the real indicator data of the patient; A group treatment template matching module for matching the patient indicator data collected by the patient data collection and acquisition module with the plurality of group treatment templates generated by the group treatment template generation subsystem, and obtaining the group treatment templates that meet the conditions from the plurality of group treatment templates.

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