Human-computer interaction-based osteoporosis auxiliary rehabilitation treatment decision-making system

Abstract

The invention relates to an osteoporosis auxiliary rehabilitation treatment decision-making system based on man-machine interaction, and belongs to the technical field of osteoporosis treatment, and the method comprises the following steps: a patient information obtaining module is used for obtaining the data information of a current patient in response to the data information input operation of the current patient by a user; the disease determination module is used for determining a disease type based on the first disease information; the prescription recommendation module is used for responding to a prescription generation instruction of a user, generating a first to-be-used prescription based on the disease type, the basic information and the medication information, and displaying the first to-be-used prescription as a recommended prescription; the effect evaluation module is used for responding to a second illness state information input operation of the user and generating treatment effect information based on the first illness state information and the second illness state information; and when the treatment effect information does not reach the expectation, generating a second to-be-used prescription, taking the second to-be-used prescription as the first to-be-used prescription, and displaying the second to-be-used prescription. The application has the effect of improving the rehabilitation effect in osteoporosis treatment.

Description

Technical Field

[0001] The present application relates to the technical field of osteoporosis treatment, and in particular to an osteoporosis auxiliary rehabilitation treatment decision-making system based on human-computer interaction. Background Art

[0002] Osteoporosis has become a global health problem. According to statistics, one osteoporotic fracture occurs every 3 seconds in the world. In my country, with the aging of the population, the incidence of osteoporosis is also rising year by year.

[0003] According to relevant data, the prevalence of osteoporosis in the elderly over 60 years old in my country is as high as 36%, and osteoporosis-induced fractures and other complications have brought huge economic and mental burdens to patients and their families. However, the current diagnosis and treatment of osteoporosis has problems such as low diagnosis rate, low treatment rate, and poor treatment compliance; Western medicine treatment is mainly based on drug treatment, but long-term use may cause side effects and relatively high costs. As a treasure of traditional Chinese medicine, Chinese medicine has unique advantages in the diagnosis and treatment of osteoporosis.

[0004] In recent years, domestic scholars have conducted a lot of research on the treatment of osteoporosis with traditional Chinese medicine and achieved a series of results. Some traditional Chinese medicine prescriptions have been proven to promote bone formation and inhibit bone resorption with relatively few side effects. However, the current rehabilitation treatment of osteoporosis patients lacks scientific and standard clinical decision support programs. Many patients do not receive efficient and comprehensive guidance and monitoring during the rehabilitation process, resulting in rehabilitation effects that are lower than expected. Summary of the invention

[0005] In order to improve the rehabilitation effect in osteoporosis treatment, the present application provides an osteoporosis auxiliary rehabilitation treatment decision-making system based on human-computer interaction.

[0006] In the first aspect, the present application provides an osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction, which adopts the following technical solutions:

[0007] An osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction, comprising:

[0008] A patient information acquisition module, for acquiring the current patient's information in response to a user's input operation on the current patient's information, wherein the information includes basic information, first condition information, and medication information;

[0009] A disease determination module, used for determining a disease type based on the first disease information;

[0010] a prescription recommendation module, configured to generate a first prescription to be used based on the disease type, basic information and medication information in response to the prescription generation instruction of the user, and display the first prescription to be used as a recommended prescription;

[0011] The effect evaluation module is used to respond to the user's second condition information input operation, generate treatment effect information based on the first condition information and the second condition information; when the treatment effect information does not meet expectations, generate a second prescription to be used, and use the second prescription to be used as the first prescription to be used and display it, wherein the second condition information is the condition information of the current patient after using the first prescription to be used.

[0012] By adopting the above technical scheme, the patient's basic information, first condition information and medication information are collected through the patient information acquisition module, the condition determination module quickly and accurately determines the condition type according to the first condition information of the current patient, and the prescription recommendation module comprehensively considers the condition type, basic information and previous medication information of the current patient to generate a first prescription to be used, so as to more accurately target the patient's specific situation and improve the treatment effect, and can recommend a new second prescription to be used based on the current treatment effect, so as to evaluate the treatment effect in real time and make adjustments, improve the scientific nature of rehabilitation treatment, provide data support for clinical decision-making, and thereby improve the rehabilitation effect in the treatment of osteoporosis.

[0013] Optionally, the prescription recommendation module includes:

[0014] The first query module is used to query whether there is a disease type matching the current patient in the patient database; if there is a matching disease type, obtain the treatment prescription of the historical patient matching the disease type;

[0015] The second query module is used to query whether the current patient is taking other medications; if the patient is taking other medications, query whether there are conflicting medications that conflict with the other medications in the drug information database, wherein the conflicting medications include medications that increase or decrease the efficacy of other medications and produce toxic substances when used together with other medications;

[0016] A rejection module is used to reject the treatment prescription containing the conflicting medication when there is a conflicting medication, and obtain the prescription to be selected;

[0017] The selection module is used to select one of the prescriptions to be selected as the first prescription to be used.

[0018] By adopting the above technical solution, the first query module can quickly find the treatment prescription that matches the current patient's symptom type by querying the patient database. Through the combined action of the second query module and the elimination module, the risk of drug interaction is effectively avoided, thereby ensuring the safety of drug use.

[0019] Optionally, the rejection module is also specifically used to:

[0020] Before querying whether the current patient is taking other medications, obtaining allergy medications of the current patient;

[0021] Eliminate the treatment prescription containing the allergy medication.

[0022] By adopting the above technical solution, the elimination module automatically eliminates prescriptions containing allergic drugs when recommending treatment prescriptions, thereby avoiding serious consequences caused by drug allergies.

[0023] Optionally, the selection module is specifically used to:

[0024] Obtaining basic information of historical patients corresponding to the prescription to be selected;

[0025] Obtaining a first score corresponding to the basic information of the historical patient and a weight corresponding to the first score;

[0026] Calculate the screening score of each of the historical patients based on the first score and the weight corresponding to the first score;

[0027] Obtaining a second score corresponding to the basic information of the current patient and a weight corresponding to the second score;

[0028] Calculating a matching score for the current patient based on the second score and the weight corresponding to the second score;

[0029] Calculating a score difference between the screening score and the matching score;

[0030] The prescription to be selected corresponding to the historical patient with the smallest score difference is selected as the first prescription to be used.

[0031] By adopting the above technical solution, the first prescription to be used is determined by the screening scores and matching scores of the basic information of historical patients and current patients, and by the score difference between the screening scores and the matching scores, so that the selected first prescription to be used has a higher matching degree with the current patient, thereby improving the treatment effect and reducing treatment failure or adverse reactions caused by inappropriate medication.

[0032] Optionally, the effect evaluation module is specifically used to:

[0033] Obtaining a current treatment cycle of the current patient;

[0034] Determining a difference reference range based on the current treatment cycle;

[0035] Acquire first physiological indicator data corresponding to the first condition information;

[0036] Acquire second physiological indicator data corresponding to the second condition information;

[0037] Calculating a data difference between the first physiological indicator data and the second physiological indicator data;

[0038] When the data difference is not within the difference reference range, it is determined that the expectation is not met.

[0039] By adopting the above technical solution, the current treatment cycle of the current patient is obtained, thereby determining the difference reference range corresponding to the current treatment cycle. When the data difference is not within the set difference reference range, the module will determine that the treatment effect has not met expectations, thereby facilitating timely adjustment of the treatment plan to improve the treatment effect.

[0040] Optionally, when the treatment effect information does not meet expectations, the effect evaluation module is further specifically used to:

[0041] Acquire the physiological index data to be processed whose data difference is less than the difference reference range;

[0042] Querying whether the prescription to be selected has medicine for treating the physiological indicator data to be processed;

[0043] If so, the to-be-selected prescription corresponding to the medication for treating the physiological indicator data to be processed is selected as the to-be-used prescription.

[0044] By adopting the above technical solution, by obtaining the data of physiological indicators to be processed whose differences are less than the reference range of the differences, it is possible to determine which physiological indicators have not improved significantly or have not met expectations. When it is found that there are therapeutic drugs for these substandard physiological indicators in the prescriptions to be selected, the therapeutic prescriptions corresponding to these drugs are used as the second drug to be used, which helps to improve the overall treatment effect.

[0045] Optionally, the osteoporosis assisted rehabilitation treatment decision-making system based on human-computer interaction further includes a safety evaluation module, and the safety evaluation module is used to:

[0046] Acquiring treatment process information using the first prescription to be used;

[0047] Check whether there is any adverse reaction information in the treatment process information;

[0048] If yes, determining the adverse reaction grade based on the adverse reaction information;

[0049] When the adverse reaction level is greater than a preset level, one of the remaining prescriptions to be selected is reselected as the first prescription to be used.

[0050] By adopting the above technical solution, by obtaining the treatment process information of the first prescription to be used, the patient's treatment progress and drug reaction can be monitored in real time to ensure the safety of the patient's treatment process. When there is adverse reaction data, it can respond quickly to provide timely information support to the doctor, thereby formulating a more reasonable treatment plan, which improves the safety of treatment to a certain extent.

[0051] Optionally, the safety evaluation module is further specifically used for:

[0052] Obtaining the current patient's daily medication information;

[0053] When the medication information does not match the standard medication information, an alarm message is generated and sent to the mobile terminal of the current patient and the mobile terminal of the corresponding doctor.

[0054] By adopting the above technical solution, by obtaining the current patient's daily medication information and comparing it with the standard medication information, it is possible to promptly detect whether the patient is taking the medication as ordered by the doctor. When a mismatch is found, an alarm message is promptly generated and sent to the patient's and doctor's mobile terminals. The doctor can understand the patient's medication status in a timely manner and adjust the treatment plan or provide further guidance as needed. Patients can also use the doctor's feedback to clarify their medication requirements and enhance their confidence and cooperation in treatment.

[0055] Optionally, the osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction further includes a data quality verification module, and the data quality verification module is specifically used to:

[0056] Complete information verification of the user's filled-in information, including data information, treatment process information and medication information;

[0057] When the filled-in information is complete, the obtained filled-in information is stored;

[0058] When the filled-in information is incomplete, feedback prompt information is generated to enable the user to modify the filled-in information.

[0059] By adopting the above technical solutions, the complete information of the user's information and treatment process information is verified to ensure that all collected data is complete and meets the requirements. This helps to reduce information bias caused by missing or incorrect data and improve the overall data quality.

[0060] Optionally, the osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction further includes a correlation evaluation module, and the correlation evaluation module is specifically used to:

[0061] The age and gender of historical patients are obtained, and the age and gender are used as influencing factors;

[0062] Determining whether the impact factor is digital data;

[0063] If not, converting the impact factor into digital data according to a preset rule;

[0064] Performing linear regression analysis on the digital data and the data difference to obtain a linear regression result;

[0065] The correlation between each of the influencing factors and the treatment effect is determined based on the linear regression results.

[0066] By adopting the above technical solution, non-digital data can be converted into digital data according to preset rules, thereby achieving standardized data processing and ensuring the consistency and accuracy of subsequent analysis. By performing linear regression analysis on digital data and data differences, the potential relationship between each influencing factor and the treatment effect can be determined. This helps doctors to more accurately consider individual differences such as age and gender of patients when formulating treatment plans, thereby improving the accuracy and effectiveness of treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a structural block diagram of an osteoporosis auxiliary rehabilitation treatment decision-making system based on human-computer interaction in an embodiment of the present application. DETAILED DESCRIPTION

[0068] The present application is further described in detail below in conjunction with the accompanying drawings.

[0069] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0071] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0072] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0073] Figure 1 This is a structural block diagram of an osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction provided in this application. Figure 1 As shown in the figure, the osteoporosis auxiliary rehabilitation treatment decision system based on human-computer interaction mainly includes:

[0074] The patient information acquisition module 101 is used to obtain the current patient's information in response to the user's input operation on the current patient's information, and the information includes basic information, first condition information and medication information;

[0075] In this embodiment, the user mentioned above may be a medical staff or a patient. The medical staff is taken as an example for explanation. The medical staff triggers the data information input operation through the mouse, keyboard, touch screen, etc. of the electronic device, and inputs the patient's data information into the electronic device. The patient information acquisition module 101 obtains the patient's data information, wherein the patient corresponding to the acquired data information is taken as the current patient, the basic information may be but not limited to the name, age, gender, marital status, height and weight of the current patient, the first condition information may be but not limited to the patient's chief complaint, accompanying symptoms, bone density, bone metabolism, serum hormone level and the presence or absence of other diseases, and the medication information is but not limited to Chinese medicine, Western medicine, medication currently being taken and history of drug allergy.

[0076] A disease determination module 102, configured to determine a disease type based on the first disease information;

[0077] When the disease determination module 102 obtains the data information of the current patient, the first disease condition information is input into the neural network model to obtain the disease type of the current user, wherein the disease type is classified according to the cause, including but not limited to primary osteoporosis, secondary osteoporosis and drug-related osteoporosis, primary osteoporosis includes postmenopausal osteoporosis, senile osteoporosis and idiopathic osteoporosis, and is classified according to clinical manifestations, including but not limited to mild osteoporosis, moderate osteoporosis and severe osteoporosis.

[0078] The symptom determination module 102 may also determine the symptom type of the current patient through a symptom grading quantitative table, a VAS score, a SF-36 questionnaire, and the like.

[0079] The prescription recommendation module 103 is used to generate a first prescription to be used based on the disease type, basic information and medication information in response to the user's prescription generation instruction, and display the first prescription to be used as a recommended prescription;

[0080] Specifically, the prescription recommendation module includes a first query module, which is used to query whether there is a disease type matching the current patient in the patient database; if there is a matching disease type, then obtain the treatment prescription of the historical patient matching the disease type; a second query module, which is used to query whether the current patient is taking other medications; if other medications are being taken, then query the drug information database to see whether there are conflicting medications that conflict with other medications, and conflicting medications include medications that increase or decrease the efficacy and produce toxic substances when used with other medications; a removal module, which is used to remove the treatment prescription containing the conflicting medication when there is a conflicting medication, and obtain the prescription to be selected; and a selection module, which is used to select one of the prescriptions to be selected as the first prescription to be used.

[0081] In this embodiment, medical staff triggers prescription generation instructions through the mouse, keyboard, touch screen, etc. of the electronic device to generate a recommended prescription, wherein the prescription recommendation refers to the recommendation of a Chinese medicine prescription. When the current patient's condition is determined and the prescription generation instruction is obtained, the prescription recommendation module obtains the condition type of historical patients in the patient database, and filters out treatment prescriptions for the current patient's condition type. The screening method can be through keyword screening. For example, the first query module obtains the current patient's condition type, and uses the condition type as a keyword for screening, thereby obtaining the same condition type as the current patient, and extracting the treatment prescription corresponding to the same condition type.

[0082] Traditional Chinese medicine preparations may interact with other drugs, resulting in enhanced or weakened efficacy, or even the production of toxic substances. Therefore, in this embodiment, a drug information database is provided, which stores the conflicting medications corresponding to each drug.

[0083] When a treatment prescription corresponding to the same disease type as the current patient is obtained, the second query module obtains the medication information of the current patient. If the current patient is taking any medication, the second query module queries the conflicting medications corresponding to the medications being taken in the drug information database one by one, and extracts the names of the conflicting medications, and queries whether there are any conflicting medications in the treatment prescription. If so, the elimination module will include the treatment prescription containing the conflicting medications, thereby obtaining the prescription to be selected to reduce the possibility of affecting the drug efficacy.

[0084] The elimination module is also specifically used to: obtain the allergy drugs of the current patient before querying whether the current patient is taking other medications; and eliminate the treatment prescriptions containing allergy drugs.

[0085] In this embodiment, the second query module obtains the drug allergy history of the current patient, which includes allergic drugs, and queries whether the name of the allergic drug exists in the treatment prescription. If it exists, the elimination module will include the treatment prescription of the allergic drug, thereby obtaining the prescription to be selected to reduce the occurrence of adverse reactions.

[0086] The selection module is specifically used to: obtain the basic information of historical patients corresponding to the prescription to be selected; obtain the first score corresponding to the basic information of the historical patients and the weight corresponding to the first score; calculate the screening score of each historical patient based on the first score and the weight corresponding to the first score; obtain the second score corresponding to the basic information of the current patient and the weight corresponding to the second score; calculate the matching score of the current patient based on the second score and the weight corresponding to the second score; calculate the score difference between the screening score and the matching score; select the prescription to be selected corresponding to the historical patient with the smallest score difference as the first prescription to be used.

[0087] In this embodiment, historical patients matching the current patient are selected based on the age, gender, height and weight of the current patient, wherein the age, height and weight can be divided into multiple intervals, each interval corresponds to a first score, and each first score is preset, wherein the weight value corresponding to each first score is determined according to the correlation relationship determined by the correlation evaluation module 107, and the determination of the correlation relationship is specifically described in the correlation evaluation module 107 and will not be described in detail here.

[0088] In this embodiment, the selection module calculates the screening score corresponding to each patient information based on the first score and the weight value corresponding to the first score, calculates the matching score of the current patient based on the above method, calculates the score difference between the matching score and the screening score, takes the absolute value of the score difference, and selects the treatment formula of the historical patient corresponding to the screening score with the smallest absolute value as the first prescription to be used. The smaller the score difference, the higher the matching degree between the current patient and the historical patient.

[0089] When there are multiple screening scores with the same score difference, calculate the first hash value of the historical patient's age, height and weight, and the second hash value of the current patient's age, height and weight, wherein the first hash value and the second hash value are calculated in the same way, and select the treatment prescription of the historical patient corresponding to the first hash value that is the same as the second hash value as the first prescription to be used. When there is no first hash value that is the same as the second hash value, you can select any one of them as the first prescription to be used, and show the first prescription to be used to the doctor and the current patient to provide the doctor with auxiliary decision-making information and improve the scientific nature of the rehabilitation treatment of osteoporosis patients.

[0090] The effect evaluation module 104 is used to respond to the user's second condition information input operation and generate treatment effect information based on the first condition information and the second condition information; when the treatment effect information does not meet expectations, a second prescription to be used is generated, and the second prescription to be used is used as the first prescription to be used and displayed, wherein the second condition information is the condition information of the current patient after using the first prescription to be used.

[0091] The effect evaluation module is specifically used to: obtain the current treatment cycle of the current patient; determine the difference reference range based on the current treatment cycle; obtain the first physiological indicator data corresponding to the first condition information; obtain the second physiological indicator data corresponding to the second condition information; calculate the data difference between the first physiological indicator data and the second physiological indicator data; when the data difference is not within the difference reference range, determine that it has not met expectations.

[0092] In this embodiment, when the current patient is using the first prescription to be used, the second condition information is triggered in real time through the mouse, keyboard, touch screen, etc. of the electronic device to input the second condition information into the electronic device. The second condition information includes but is not limited to the chief complaint, bone density, accompanying symptoms, bone metabolism and serum hormone levels. Among them, bone density, bone metabolism and serum hormone levels can be checked regularly in the hospital, and the results can be entered into the electronic device after the examination.

[0093] In this embodiment, bone density, accompanying symptoms, bone metabolism and serum hormone levels are called treatment factors. The data difference between each treatment factor and the previous treatment factor is calculated, and the data difference is compared with the difference reference range of the corresponding interval to determine the treatment effect.

[0094] Among them, the treatment cycle of the current patient is determined according to the current patient's medication time. Each treatment cycle corresponds to a difference reference range. The difference reference range is obtained based on the treatment data analysis of historical patients. When the data difference is not within the difference reference range, it is determined that the treatment effect information does not meet expectations.

[0095] When the treatment effect information does not meet expectations, the effect evaluation module is also specifically used to: obtain the to-be-processed physiological indicator data whose data difference is less than the difference reference range; query whether the to-be-selected prescription contains medicines for treating the to-be-processed physiological indicator data; if so, select the to-be-selected prescription corresponding to the medicine for treating the to-be-processed physiological indicator data as a prescription to be used.

[0096] In this embodiment, when the treatment effect information does not meet expectations, physiological indicator data whose difference is less than the difference reference range are extracted, and the extracted physiological indicator data are used as the physiological indicator data to be processed. The indicators of the data to be processed are bone density, bone metabolism and serum hormone levels. Whether there is a medication for treating the physiological indicator data to be processed in the prescription to be selected, if there is, the prescription to be selected containing the medication corresponding to the medication is used as the first medication to be used, and treatment continues.

[0097] The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction also includes a safety evaluation module 105, which is used to: obtain treatment process information using the first prescription to be used; query whether there is adverse reaction information in the treatment process information; if so, determine the adverse reaction level based on the adverse reaction information; when the adverse reaction level is greater than the preset level, re-select one of the remaining prescriptions to be selected as the first prescription to be used.

[0098] In this embodiment, the current patient inputs the treatment process information of the first prescription to be used into the electronic device in real time through the mouse, keyboard, touch screen, etc. of the smart terminal, wherein the treatment process information includes but is not limited to adverse reaction information, medication dosage and medication time. The safety evaluation module 105 obtains the treatment process information in real time. When there is adverse reaction information, the corresponding adverse reaction information is extracted, and the adverse information is analyzed to determine the adverse reaction level.

[0099] Level 1: Mild adverse events: refers to adverse events that have little impact on the patient's health, usually do not require special treatment or intervention, and will not have a significant impact on the treatment plan, such as mild headache, nausea, etc.; Level 2: Moderate adverse events: refers to adverse events that have a certain impact on the patient's health, may require certain treatment or intervention, and sometimes affect the treatment process, but usually do not cause serious harm to the patient, such as moderate allergic reactions, moderate bleeding, etc.; Level 3: Severe adverse events: refers to adverse events that have a serious impact on the patient's health, require active treatment and intervention, and may cause serious harm to the patient or even endanger his life, such as severe allergic reactions, severe bleeding, etc.

[0100] When the adverse reaction level is greater than level one, one of the other medications to be selected is selected as the first prescription to be used, so that the current first prescription to be used can be updated in a timely manner to ensure the treatment effect while reducing the possibility of adverse effects on the current patient.

[0101] The safety evaluation module 105 is also specifically used to: obtain the current patient's daily medication information; when the medication information does not match the standard medication information, generate an alarm message, and send the alarm message to the current patient's mobile terminal and the corresponding doctor's mobile terminal.

[0102] In this embodiment, the medication information of the current patient is obtained in real time, wherein the medication information includes a first prescription to be used, a medication dosage and a medication time. Standard medication information is preset in the electronic device, and the safety evaluation module 105 compares the medication information with the standard medication information to determine whether the medication information is consistent with the standard medication information. When the medication information is inconsistent with the standard medication information, an alarm message is generated in time to be sent to the current patient's mobile terminal and the corresponding doctor's mobile terminal to monitor the current patient's medication information and ensure the treatment effect and patient safety.

[0103] The osteoporosis assisted rehabilitation treatment decision-making system based on human-computer interaction also includes a data quality verification module 106, which is specifically used to: verify the completeness of the user's filled-in information, the filled-in information includes data information, treatment process information and medication information; when the filled-in information is complete, the obtained filled-in information is stored; when the filled-in information is incomplete, feedback prompt information is generated to enable the user to modify the filled-in information.

[0104] In this embodiment, when obtaining the user's data information, treatment process information and medication information, the data information, treatment process information and medication information need to be verified to ensure the integrity of the data.

[0105] Parity check can be used for error detection. A check bit is added at the end of the data byte to ensure that the number of "1"s in the data meets the parity requirement. The cryptographic hash function MD5 verifies the integrity of the data and checks specific polynomial division to detect errors in the data.

[0106] The osteoporosis assisted rehabilitation treatment decision-making system based on human-computer interaction also includes a correlation evaluation module 107, which is specifically used to: obtain the age and gender of historical patients, and use age and gender as influencing factors; determine whether the influencing factor is digital data; if not, convert the influencing factor into digital data according to preset rules; perform linear regression analysis on the digital data and the data difference to obtain linear regression results; and determine the correlation between each influencing factor and the treatment effect based on the linear regression results.

[0107] In this embodiment, the correlation evaluation module 107 obtains the age, gender and data difference of all historical patients in the patient database, converts the gender into digital data, such as 0 for male and 1 for female, and establishes scatter plots of gender and treatment effect and scatter plots of age and treatment effect, respectively, wherein the treatment effect can be the data difference, and a linear regression model is established based on the scatter plot, and a linear regression analysis is performed through the linear regression model to determine the linear regression result, so as to determine the relationship between the influencing factor and the treatment effect.

[0108] It is particularly noted that a linear regression model can be established based on each treatment cycle, and a linear regression model can also be established based on the entire treatment process.

[0109] Among them, the corresponding weight value is determined according to the correlation between the influencing factor and the treatment effect. The stronger the correlation, the greater the weight. The first prescription to be used can also be updated in time according to the correlation between the influencing factor and the treatment effect in each treatment cycle to improve the current patient's rehabilitation effect in osteoporosis treatment.

[0110] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0111] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. An osteoporosis assisted rehabilitation treatment decision-making system based on human-computer interaction, characterized in that: include: A patient information acquisition module, for acquiring the current patient's information in response to a user's input operation on the current patient's information, wherein the information includes basic information, first condition information, and medication information; A disease determination module, used for determining a disease type based on the first disease information; a prescription recommendation module, configured to generate a first prescription to be used based on the disease type, basic information and medication information in response to the prescription generation instruction of the user, and display the first prescription to be used as a recommended prescription; The effect evaluation module is used to respond to the user's second condition information input operation, generate treatment effect information based on the first condition information and the second condition information; when the treatment effect information does not meet expectations, generate a second prescription to be used, and use the second prescription to be used as the first prescription to be used and display it, wherein the second condition information is the condition information of the current patient after using the first prescription to be used.

2. According to the human-computer interaction-based osteoporosis assisted rehabilitation treatment decision-making system of claim 1, it is characterized in that: The prescription recommendation module includes: The first query module is used to query whether there is a disease type matching the current patient in the patient database; if there is a matching disease type, obtain the treatment prescription of the historical patient matching the disease type; The second query module is used to query whether the current patient is taking other medications; if the patient is taking other medications, query whether there are conflicting medications that conflict with the other medications in the drug information database, wherein the conflicting medications include medications that increase or decrease the efficacy of other medications and produce toxic substances when used together with other medications; A rejection module is used to reject the treatment prescription containing the conflicting medication when there is a conflicting medication, and obtain the prescription to be selected; The selection module is used to select one of the prescriptions to be selected as the first prescription to be used.

3. The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction according to claim 2 is characterized in that: The culling module is also specifically used for: Before querying whether the current patient is taking other medications, obtaining allergy medications of the current patient; Eliminate the treatment prescription containing the allergy medication.

4. The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction according to claim 2 is characterized in that: The selection module is specifically used for: Obtaining basic information of historical patients corresponding to the prescription to be selected; Obtaining a first score corresponding to the basic information of the historical patient and a weight corresponding to the first score; Calculate the screening score of each of the historical patients based on the first score and the weight corresponding to the first score; Obtaining a second score corresponding to the basic information of the current patient and a weight corresponding to the second score; Calculating a matching score for the current patient based on the second score and the weight corresponding to the second score; Calculating a score difference between the screening score and the matching score; The prescription to be selected corresponding to the historical patient with the smallest score difference is selected as the first prescription to be used.

5. The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction according to claim 1, characterized in that: The effect evaluation module is specifically used for: Obtaining a current treatment cycle of the current patient; Determining a difference reference range based on the current treatment cycle; Acquire first physiological indicator data corresponding to the first condition information; Acquire second physiological indicator data corresponding to the second condition information; Calculating a data difference between the first physiological indicator data and the second physiological indicator data; When the data difference is not within the difference reference range, it is determined that the expectation is not met.

6. The osteoporosis assisted rehabilitation treatment decision-making system based on human-computer interaction according to claim 5, characterized in that: When the treatment effect information does not meet expectations, the effect evaluation module is further specifically used to: Acquire the physiological index data to be processed whose data difference is less than the difference reference range; Querying whether the prescription to be selected has medicine for treating the physiological indicator data to be processed; If so, the to-be-selected prescription corresponding to the medication for treating the physiological indicator data to be processed is selected as the to-be-used prescription.

7. The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction according to claim 1 is characterized in that: The osteoporosis auxiliary rehabilitation treatment decision-making system based on human-computer interaction also includes a safety evaluation module, which is used to: Acquiring treatment process information using the first prescription to be used; Check whether there is any adverse reaction information in the treatment process information; If yes, determining the adverse reaction grade based on the adverse reaction information; When the adverse reaction level is greater than a preset level, one of the remaining prescriptions to be selected is reselected as the first prescription to be used.

8. The osteoporosis assisted rehabilitation treatment decision-making system based on human-computer interaction according to claim 7, characterized in that: The safety evaluation module is also specifically used for: Obtaining the current patient's daily medication information; When the medication information does not match the standard medication information, an alarm message is generated and sent to the mobile terminal of the current patient and the mobile terminal of the corresponding doctor.

9. The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction according to claim 1, characterized in that: The osteoporosis auxiliary rehabilitation treatment decision-making system based on human-computer interaction also includes a data quality verification module, which is specifically used to: Complete information verification of the user's filled-in information, including data information, treatment process information and medication information; When the filled-in information is complete, the obtained filled-in information is stored; When the filled-in information is incomplete, feedback prompt information is generated to enable the user to modify the filled-in information.

10. The osteoporosis assisted rehabilitation treatment decision system based on human-computer interaction according to claim 5, characterized in that: The osteoporosis auxiliary rehabilitation treatment decision-making system based on human-computer interaction also includes a correlation evaluation module, which is specifically used to: The age and gender of historical patients are obtained, and the age and gender are used as influencing factors; Determining whether the impact factor is digital data; If not, converting the impact factor into digital data according to a preset rule; Performing linear regression analysis on the digital data and the data difference to obtain a linear regression result; The correlation between each of the influencing factors and the treatment effect is determined based on the linear regression results.

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