CN115732058A - Artificial intelligence-based automatic intervention and adjustment method and system for surgical rehabilitation - Google Patents
Artificial intelligence-based automatic intervention and adjustment method and system for surgical rehabilitation Download PDFInfo
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Abstract
The application provides an automatic intervention adjusting method and system for operation rehabilitation based on artificial intelligence, which belongs to the technical field of data processing, and comprises the following steps of S1, collecting basic information, health conditions, exercise habits and dietary habits of perioperative patients related to diseases, and matching out an initial rehabilitation intervention scheme for pushing the patients daily; s2, determining the adaptation degree of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by a data acquisition module; and S3, adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree, and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is within a threshold interval. This application can carry out the automatic intervention of health process to perioperative period patient to can constantly adjust intervention intensity, improved perioperative period patient's health education management effect.
Description
Technical Field
The application relates to the technical field of data, in particular to an automatic intervention and adjustment method and system for operation rehabilitation based on artificial intelligence.
Background
Perioperative refers to a period of time before, during, and after a patient's hand. The period from 5-7 days before operation to 7-28 days after operation is a whole process around the operation, and the operation is started from the patient to receive the operation treatment until the patient basically recovers after the operation. Usually, the specific time after operation is determined according to the specific condition of the patient, but exercise test is usually needed to verify the effect within 28 days after operation.
At present, perioperative patients, particularly perioperative patients before and after lung surgery, doctors and nurses can perform necessary rehabilitation guidance such as preoperative artificial respiration training and thrombus prevention on patients before and after lung surgery, but medical staff cannot be required to perform traditional rehabilitation training and symptom management in detail under the conditions of high labor cost and lack of standardization degree of rehabilitation guidance, so that the feasibility of implementing standardized artificial training preoperatively is low, and after patients are discharged, even in hospitals with good medical quality, the medical staff cannot timely master the training states and abnormal index conditions of the patients, and therefore, the medical staff cannot effectively remind and feed back the symptoms and the rehabilitation progress of the patients.
For the patient, under the traditional rehabilitation and symptom management mode, the amount of information which can be directly obtained by the patient from the medical care side is limited, the patient is obscure and has many professional terms, the patient lacks one-to-one personalized guidance, and the patient can only carry out face-to-face operation in the presence of medical care personnel, so that the traditional method has poor accessibility, poor individual pertinence to the patient and low patient compliance.
Because different medical institutions interfere with and manage the health of patients in good and uneven levels, the patients teach one by one in the hospital, the efficiency is lower, the standardization is difficult, the medical care and the repeated labor are realized, the scarce human resources are wasted, simultaneously, only the management of the patients during the hospital period can be completed in space, the doctors and nurses can teach the patients on the spot, the conditions of the patients are inquired on the spot, a management blank is formed before and after the patients are admitted, the patients lack the rehabilitation support, the learning and training conditions of the patients are difficult to monitor, the health education management effect is difficult to guarantee, the problems of incapability of mastering the symptoms of diet, movement, psychology, sleep, cough, pain and the like before and after the patients are admitted are solved, and the comprehensive multidimensional intelligent health management system is necessary to provide.
Disclosure of Invention
In order to solve at least one of the above technical problems, a first aspect of the present application provides an automatic intervention and adjustment method for surgical rehabilitation based on artificial intelligence, which mainly includes:
s1, collecting basic information, health conditions, exercise habits and dietary habits of perioperative patients related to diseases, and matching an initial rehabilitation intervention scheme for pushing the patients every day, wherein the initial rehabilitation intervention scheme comprises but is not limited to a rehabilitation knowledge development scheme and a rehabilitation training scheme;
s2, determining the adaptation degree of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by a data acquisition module;
and S3, adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree, and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is within a threshold interval.
Preferably, in step S1, matching the initial rehabilitation intervention plan for daily pushing to the patient specifically includes:
s11, determining a perioperative period stage and a duration capable of learning and training of the perioperative period patient based on collected basic information, health conditions, exercise habits and dietary habits of the perioperative period patient related to diseases, wherein the perioperative period stage comprises a preoperative stage, a postsurgical hospital stage and a postoperative discharge stage;
and S12, matching corresponding rehabilitation intervention schemes from a preoperative phase database, a postoperative hospital internal phase database or a postoperative discharge phase database based on the duration, wherein rehabilitation training scheme items of perioperative patients related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients and rehabilitation knowledge education scheme item sequences related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients at different duration stages are recorded in the preoperative phase database, the postoperative hospital internal phase database or the postoperative discharge phase database.
Preferably, the step S12 further includes updating the ranking result of each rehabilitation knowledge development plan in each database, which specifically includes:
step S121, calculating keyword hit rates related to various rehabilitation knowledge development and education scheme projects based on question-answer conversations of perioperative patients, and taking the keyword hit rates as first adjustment parameters of corresponding rehabilitation knowledge development and education schemes;
step S122, calculating a second adjusting parameter corresponding to the rehabilitation knowledge suffering and education scheme based on the retention time of the reading page of each rehabilitation knowledge suffering and education scheme of the perioperative patient;
step S123, taking the recommendation degree of the medical staff for each rehabilitation knowledge suffering and teaching scheme as a third adjustment parameter of the corresponding rehabilitation knowledge suffering and teaching scheme;
step S124, carrying out weighted calculation on the first adjusting parameter, the second adjusting parameter and the third adjusting parameter to obtain the score of each rehabilitation knowledge suffering from education scheme, and carrying out item sequencing on each rehabilitation knowledge suffering from education scheme based on the score.
Preferably, in step S2, determining the degree of fitting of the perioperative patient during execution of the current day rehabilitation intervention program based on the feedback data of the questionnaire survey comprises:
step S21, matching a periodic symptom questionnaire according to the operation date of perioperative patients, wherein each question of the periodic symptom questionnaire is a measure item question;
s22, acquiring the expression of the perioperative patient in answering the periodic symptom questionnaire based on a facial recognition technology;
step S23, determining a first scale value of each question based on the responded stage symptom questionnaire, and determining a second scale value of the corresponding question based on the relation between the expression and the scale comparison table;
and S24, determining a comprehensive scale value of the corresponding problem based on the first scale value and the second scale value.
And S25, determining the adaptation degree of the rehabilitation intervention scheme on the day based on the comprehensive scale values of all the problems.
Preferably, in step S2, determining the suitability of the perioperative patient in the course of executing the current-day rehabilitation intervention scheme through the rehabilitation information acquired by the data acquisition module includes:
s26, collecting the current-day operation-related sign data of the perioperative patient based on the sign sensor;
s27, determining an energy structure of diet fed back by the perioperative patient based on an image recognition technology to determine nutritional data of the perioperative patient;
and S28, determining the adaptation degree of the rehabilitation intervention scheme on the day according to the given physical sign data interval and the given nutrition data interval.
The second aspect of the present application provides an automatic intervention adjustment system for operation rehabilitation based on artificial intelligence, which mainly comprises:
the rehabilitation intervention scheme matching module is used for collecting basic information, health conditions, exercise habits and dietary habits of the perioperative patients related to diseases and matching an initial rehabilitation intervention scheme for daily pushing to the patients, wherein the initial rehabilitation intervention scheme comprises but is not limited to a rehabilitation knowledge development scheme and a rehabilitation training scheme;
the adaptation degree calculation module is used for determining the adaptation degree of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by the data acquisition module;
and the rehabilitation intervention scheme adjusting module is used for adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is positioned in a threshold interval.
Preferably, the rehabilitation intervention scheme matching module comprises:
the system comprises an intervention time calculation unit, a data acquisition unit and a data processing unit, wherein the intervention time calculation unit is used for determining a perioperative period stage and a time length capable of learning and training of perioperative patients based on collected basic information, health conditions, exercise habits and dietary habits of the perioperative patients related to diseases, and the perioperative period stage comprises a preoperative stage, a postsurgical hospital stage and a postoperative discharge stage;
and the intervention scheme generating unit is used for matching out a corresponding rehabilitation intervention scheme from the preoperative stage database, the postoperative hospital inner stage database or the postoperative discharge stage database based on the duration, recording rehabilitation training scheme items of perioperative patients associated with the basic information, the health condition, the motion habits and the dietary habits of the perioperative patients and sequencing the rehabilitation knowledge suffering teaching scheme items associated with the basic information, the health condition, the motion habits and the dietary habits of the perioperative patients in different duration stages in the preoperative stage database, the postoperative hospital inner stage database or the postoperative discharge stage database.
Preferably, the system further comprises a database updating module, configured to order items of the multiple rehabilitation knowledge development plans in each database, where the database updating module includes:
the first adjustment parameter determining unit is used for calculating keyword hit rates relevant to various rehabilitation knowledge education scheme projects based on question-answer conversations of perioperative patients, and taking the keyword hit rates as first adjustment parameters corresponding to the rehabilitation knowledge education schemes;
the second adjustment parameter determining unit is used for calculating a second adjustment parameter corresponding to the rehabilitation knowledge suffering and education scheme based on the retention time of the reading page of the perioperative patient for each rehabilitation knowledge suffering and education scheme;
the third adjustment parameter determining unit is used for taking the recommendation degree of the medical staff to each rehabilitation knowledge suffering teaching scheme as a third adjustment parameter corresponding to the rehabilitation knowledge suffering teaching scheme;
and the score calculation unit is used for performing weighted calculation on the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain scores of the rehabilitation knowledge suffering education schemes, and performing item sequencing on the rehabilitation knowledge suffering education schemes based on the scores.
Preferably, the suitability calculation module includes:
the questionnaire generating unit is used for matching a periodic symptom questionnaire according to the operation date of a perioperative patient, wherein each question of the periodic symptom questionnaire is a measure item question;
the questionnaire reply expression recognition unit is used for acquiring expressions of perioperative patients in replying the stage symptom questionnaire based on a facial recognition technology;
a scale value generation unit for determining a first scale value of each question based on the stage symptom questionnaire of the responses, and determining a second scale value of the corresponding question based on the relationship between the expression and the scale comparison table;
and the comprehensive scale value calculating unit is used for determining the comprehensive scale value of the corresponding problem based on the first scale value and the second scale value.
And the first fitness calculating unit is used for determining the fitness of the rehabilitation intervention scheme on the current day based on the comprehensive scale values of all the problems.
Preferably, the suitability calculation module includes:
the physical sign data collection unit is used for collecting the physical sign data of the perioperative patient on the current day and relevant to the operation based on the physical sign sensor;
a nutritional data collection unit for determining an energy structure of a diet fed back by the perioperative patient based on image recognition technology to determine nutritional data of the perioperative patient;
and the second fitness calculating unit is used for determining the fitness of the rehabilitation intervention scheme on the day according to the given physical sign data interval and the given nutrition data interval.
A third aspect of the present application provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program for implementing an artificial intelligence based automatic intervention and adjustment method for surgical rehabilitation.
A fourth aspect of the present application provides a readable storage medium storing a computer program, which when executed by a processor, is used to implement the artificial intelligence based automatic intervention and adjustment method for surgical rehabilitation.
This application carries out the automatic intervention adjustment of health process to perioperative patient based on artificial intelligence, is the intelligent intervention scheme of considering the multidimension degree, and systematic grasp perioperative patient's the recovered condition to can be efficient carry out healthy supervision to perioperative patient, improve the intervention effect.
Drawings
Fig. 1 is a flow chart of a preferred embodiment of the automatic intervention adjusting method for surgical rehabilitation based on artificial intelligence.
FIG. 2 is a schematic diagram of a computer device according to a preferred embodiment of the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all embodiments of the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application, and should not be construed as limiting the present application. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application. Embodiments of the present application will be described in detail below with reference to the drawings.
The first aspect of the present application provides an automatic intervention and adjustment method for surgical rehabilitation based on artificial intelligence, as shown in fig. 1, mainly including:
s1, collecting basic information, health conditions, exercise habits, eating habits and other living habits of perioperative patients related to diseases, and matching an initial rehabilitation intervention scheme for daily pushing to the patients, wherein the initial rehabilitation intervention scheme comprises but is not limited to a rehabilitation knowledge education scheme and a rehabilitation training scheme.
In this embodiment, the basic information, health condition, exercise habits, eating habits and other living habits of the perioperative patients related to the diseases are collected mainly in the form of questionnaires, and may also be extracted after being associated with the medical system, and the basic information, health condition, exercise habits, eating habits and other living habits mainly include but are not limited to the following:
a) Basic information: age, sex, height, weight, city.
b) Lung nodules detected baseline: date of surgery, date of hospitalization, etc.
c) Symptoms are: chest distress and tachypnea, cough and expectoration, difficulty in sleeping, tension and anxiety, fatigue and weakness, inappetence without appetite, nausea and emesis, diarrhea, constipation, and pain.
d) History of disease: asthma, chronic bronchitis, chronic Obstructive Pulmonary Disease (COPD), digestive dysfunction or irritable bowel syndrome, rheumatoid arthritis or osteoporosis, diabetes, hypertension, hyperlipidemia, heart disease, liver and gall stones or liver cirrhosis, chronic kidney disease, other tumors.
e) Family history: (relatives including parents, brother sisters, daughter, tert Jiujiu, aunt and grandfather) breast cancer, oral cancer, nasopharyngeal cancer, laryngeal cancer, lung cancer, liver cancer, gastric cancer, intestinal cancer, ovarian cancer, endometrial cancer, cervical cancer and other cancers.
f) The life style is as follows: 1) Exercise habits: exercise frequency, exercise intensity, exercise duration, 2) smoking history: whether smoking is performed, smoking cessation time, smoking age, smoking amount, pollution exposure; 3) History of alcohol consumption: frequency of drinking; 4) The nutrition condition is as follows: the food intake; 5) Sleep state: sleep time, sleep problems.
g) History of allergy: eggs or eggs, gluten, milk/lactose, peanuts, soybeans, nuts, etc.
The method comprises the steps of acquiring a plurality of training samples and checking samples in a labeling mode, wherein each type of data in the 7 types of data in each training sample is used as an input, so that an input layer can be set to be 7 layers and 5-10 outputs are set, namely, the output layer is 5-10 layers, the hidden number of layers of the BP neural network is about 10 layers, each output corresponds to one rehabilitation intervention scheme, a doctor determines standard output according to the input, training is carried out through the BP neural network after labeling, weights and threshold values of each layer in the trained BP neural network are determined, and in the trained BP neural network, the 7 input data are respectively calculated according to parameter values in an questionnaire, and then the corresponding rehabilitation intervention scheme is determined through the BP neural network.
Besides using the neural network to determine the rehabilitation intervention program, a specific rehabilitation intervention program can be determined from the questionnaire according to the set 3-5 values in a database table with a corresponding number, for example, in some optional embodiments, the matching of the initial rehabilitation intervention program for daily pushing to the patient in step S1 specifically includes:
s11, determining a perioperative period stage and a duration capable of learning and training of the perioperative period patient based on collected basic information, health conditions, exercise habits and dietary habits of the perioperative period patient related to diseases, wherein the perioperative period stage comprises a preoperative stage, a postsurgical hospital stage and a postoperative discharge stage;
and S12, matching corresponding rehabilitation intervention schemes from a preoperative phase database, a postoperative hospital internal phase database or a postoperative discharge phase database based on the duration, wherein rehabilitation training scheme items of perioperative patients related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients and rehabilitation knowledge education scheme item sequences related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients at different duration stages are recorded in the preoperative phase database, the postoperative hospital internal phase database or the postoperative discharge phase database.
In this embodiment, basic information of perioperative patients is collected, including disease status, time from surgery, physical conditions (including basic information, pulmonary nodule detection, symptoms, disease history, family history, life style, allergy history, etc.), exercise habits, and the results of the comprehensive exercise capacity test are referenced to determine the stage (pre-operation/post-operation in/out of hospital) and the duration of learning and training, and then the corresponding perioperative rehabilitation intervention scheme is matched from a preset database, and necessary contents (including but not limited to various forms such as pictures and texts and videos) are pushed to the patients daily in the most simplified amount, including necessary rehabilitation knowledge, patient education scheme, rehabilitation training scheme, symptom questionnaire, feedback suggestion, etc.
For example, in step S11, the stage where the patient is located is determined to be a preoperative stage, a postsurgical in-hospital stage, and a postsurgical discharge stage according to the time from the operation, in the preoperative stage, the rehabilitation knowledge development scheme and the rehabilitation training scheme are extracted from the preoperative stage database, in the postsurgical in-hospital stage, the rehabilitation knowledge development scheme and the rehabilitation training scheme are extracted from the postsurgical in-hospital stage database, and in the postsurgical discharge stage, the rehabilitation knowledge development scheme and the rehabilitation training scheme are extracted from the postsurgical discharge stage database. Each training plan or rehabilitation knowledge and education plan in each database has a time parameter, and then in step S12, a corresponding rehabilitation intervention plan is matched from the pre-operation stage database, the post-operation intra-hospital stage database, or the post-operation discharge stage database based on the duration.
The steps can provide an accurate initial rehabilitation intervention scheme for perioperative patients in a machine learning mode.
In the above embodiment, each database provides a specific rehabilitation training scheme item and a rehabilitation knowledge education scheme. The rehabilitation knowledge education program mainly refers to courses, for example, in a preoperative stage database, a first course item sequencing list with first basic information, health condition, exercise habit and diet habit, a second course item sequencing list with second basic information, health condition, exercise habit and diet habit, a third course item sequencing list with third basic information, health condition, exercise habit and diet habit and the like, and each course item sequencing list has a plurality of courses which are set in advance and sequenced according to importance degrees.
In some optional embodiments, the above-mentioned set plurality of courses sorted by importance may be adjusted according to the collected information of the perioperative patient, that is, the sorting result for updating each rehabilitation knowledge and education program in each database specifically includes:
step S121, calculating keyword hit rates related to various rehabilitation knowledge development and education scheme projects based on question-answer conversations of perioperative patients, and taking the keyword hit rates as first adjustment parameters of corresponding rehabilitation knowledge development and education schemes;
step S122, calculating a second adjusting parameter corresponding to the rehabilitation knowledge suffering and education scheme based on the retention time of the reading page of each rehabilitation knowledge suffering and education scheme of the perioperative patient;
step S123, taking the recommendation degree of the medical staff to each rehabilitation knowledge suffering and education scheme as a third adjustment parameter corresponding to the rehabilitation knowledge suffering and education scheme;
step S124, carrying out weighted calculation on the first adjusting parameter, the second adjusting parameter and the third adjusting parameter to obtain the score of each rehabilitation knowledge suffering from education scheme, and carrying out item sequencing on each rehabilitation knowledge suffering from education scheme based on the score.
In step S121, expected values of courses (a certain rehabilitation knowledge and education program) reflected in the communication session with the perioperative patient are mainly identified in an artificial intelligence manner, for example, if a certain keyword is hit continuously during the communication with the perioperative patient, the course corresponding to the keyword is regarded as a useful course for the perioperative patient, in this step, a first adjustment parameter is assigned to each course in a normalized processing manner according to the hit rate, for example, 4 keywords are hit, the hit times of the corresponding 4 courses are respectively 4 times, 3 times, 2 times and 1 time, and the first adjustment parameters of the four courses are respectively 0.4,0.3,0.2 and 0.1. The first adjustment parameter for the other lessons is 0.
In step S122, the reading page staying time is divided by the expected time to obtain a second adjustment parameter, for example, the expected staying time of the reading page corresponding to a certain course is 10S, and the actual staying time is 5S, then the second adjustment parameter is 0.5, or other manners may be adopted, so that the second adjustment parameter is between 0 and 1.
Specifically, in step S124, the score of each course (rehabilitation knowledge development plan) can be calculated by the following model:
wherein, c i The number of course hits, t, corresponding to the ith keyword of the n keywords i Actual stay for reading ith course, T i Actual expected stay for reading ith lesson, m i Recommendation for the ith course for the caregiver (between 0 and 1), a 1 ，a 2 ，a 3 The weights of the first adjusting parameter, the second adjusting parameter and the third adjusting parameter are respectively, and the sum of the weights is 1.
And S2, determining the adaptation degree of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by the data acquisition module.
This step mainly determines the role played by the rehabilitation intervention program executed on the day, and thus determines whether the rehabilitation intervention program executed on the day is valid, where the degree of adaptation is understood to be the degree of difficulty of the rehabilitation intervention program, the effect of the rehabilitation intervention program, and so on.
In some alternative embodiments, determining the degree of fit of the perioperative patient during execution of the current day rehabilitation intervention program based on the feedback data of the questionnaire in step S2 comprises:
step S21, matching a periodic symptom questionnaire according to the operation date of perioperative patients, wherein each question of the periodic symptom questionnaire is a measure item question;
s22, acquiring the expression of the perioperative patient in answering the periodic symptom questionnaire based on a facial recognition technology;
step S23, determining a first scale value of each question based on the responded stage symptom questionnaire, and determining a second scale value of the corresponding question based on the relation between the expression and the scale comparison table;
and S24, determining a comprehensive scale value of the corresponding problem based on the first scale value and the second scale value.
And S25, determining the adaptation degree of the rehabilitation intervention scheme on the day based on the comprehensive scale values of all the problems.
In step S21 and step S23, it is usually determined whether the rehabilitation intervention program pushed on the day has a good effect by means of a questionnaire, each question of the periodic symptom questionnaire is set as a measure question, that is, the patient' S suitability for a specific rehabilitation intervention program can be described by a first scale value of the measure question, for example, if a certain question in the questionnaire is satisfied with the breathing exercise course, 1 is very unsatisfactory, and 10 is very satisfactory, the score fed back by the patient can be indirectly used as the first scale value of the breathing exercise course, for example, the score fed back by the patient is 5, and the first scale value is 0.5, it can be understood that the feedback value of the measure question varies within a set interval, and the corresponding first scale value is the value fed back to the interval scaled to 0-1.
In step S22 and step S23, the scale comparison table mainly records the relationship between the expression and the scale, different expressions corresponding to different scales, the step mainly brings the patient' S expression into scale evaluation, and the step obtains the emotion information by identifying the facial expression of the patient and analyzing, and compares the analysis result with the questionnaire table result to judge and correct the emotion analysis result of the patient. For example, the postoperative pain change condition of the patient is inquired, and the artificial intelligence facial emotion analysis is combined to double judge the pain condition of the patient, so that data support is provided for reminding the patient to take medicine or other countermeasures. In the training process of the patient, the emotion of the patient before and after training is recorded and analyzed in an artificial intelligence emotion recognition mode. Still taking the above embodiment as an example, when the patient score is 5, the expression thereof is recognized and corresponds to 0.4 in the scale chart, i.e. the second scale value is 0.4.
Step S24 actually uses the second scale value 0.4 to correct the first scale value 0.5, and its model is, for example:
F＝0.8p 1 +0.2p 2 . Wherein p is 1 Is a first scale value, p 2 And F is the second scale value, and F is the comprehensive scale value of the corresponding problem.
In step S25, the integrated scale values corresponding to all the problems are averaged, or weighted and averaged, and the calculated final value is used as the fitness of the rehabilitation intervention plan on the current day.
Above-mentioned adaptation degree of rehabilitation intervention scheme on the day can regard as first adaptation degree, and is corresponding, and this application still can confirm the second adaptation degree of perioperative period patient in carrying out rehabilitation intervention scheme on the day in-process through the recovered information that data acquisition module gathered, specifically includes:
s26, collecting the current-day operation-related sign data of the perioperative patient based on the sign sensor;
step S27, determining an energy structure of diet fed back by the patient in the perioperative period based on an image recognition technology so as to determine nutritional data of the patient in the perioperative period;
and S28, determining the adaptation degree of the rehabilitation intervention scheme on the day according to the given physical sign data interval and the given nutrition data interval.
In step S26, the physical sign data, including the respiration frequency, blood pressure, and other data related to the operation of the patient, is collected by the sensor, and the corresponding intervention advice can be given according to the determination of the normal and abnormal intervals, so that the physical data can be obtained by the physical sign sensor.
In step S27, the patient may take a picture of the uploaded diet, identify the meal of the patient, and analyze the completion of the recommended nutritional regimen. The types of food, total energy, energy structures and the like ingested by the patient are analyzed, basic information and the stage (before and after operation) of the patient are combined, whether nutrition and calorie ingestion of the patient is reasonable or not is judged through artificial intelligence analysis, and a further nutrition intervention scheme is matched, so that nutrition data can be determined through an image recognition technology.
In step S28, if the collected vital sign data is not in the vital sign data interval, the fitness of one current-day rehabilitation intervention program is determined according to the set mapping function, and similarly, if the collected nutritional data is not in the nutritional data interval, the fitness of another current-day rehabilitation intervention program is determined according to the set mapping function, and the fitness of the two current-day rehabilitation intervention programs may jointly form a second fitness. The mapping function is not exemplified, and can be generally implemented by a piecewise function
And finally, the first adaptation degree and the second adaptation degree jointly form a total adaptation degree. For example by taking an average or a weighted average.
And S3, adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree, and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is within a threshold interval.
In this step, the rehabilitation intervention scheme pushed on the same day can be quantified through verification in step S2, if the adaptation degree is within the threshold interval, the rehabilitation intervention scheme of the next day can be continuously recommended according to the pushing scheme with the same strength, if the adaptation degree is not within the threshold interval, the strength of the rehabilitation intervention scheme of the predetermined next day needs to be adjusted, for example, when the adaptation degree is less than the lower limit of the threshold interval, the strength of the rehabilitation intervention scheme of the next day is appropriately increased, otherwise, when the adaptation degree is greater than the upper limit of the threshold interval, the strength of the rehabilitation intervention scheme of the next day is appropriately decreased, and the increasing or decreasing proportion is adjusted according to the difference between the specific adaptation degree and the upper limit and the lower limit of the threshold interval according to the step length.
This application can carry out the automatic intervention of health process to perioperative period patient to can constantly adjust intervention intensity, improved perioperative period patient's health education management effect.
The second aspect of the present application provides an artificial intelligence-based automatic intervention and adjustment system for surgical rehabilitation corresponding to the above method, which mainly includes:
the rehabilitation intervention scheme matching module is used for collecting basic information, health conditions, exercise habits and dietary habits of the perioperative patients related to diseases and matching an initial rehabilitation intervention scheme for daily pushing to the patients, wherein the initial rehabilitation intervention scheme comprises but is not limited to a rehabilitation knowledge development scheme and a rehabilitation training scheme;
the fitness calculation module is used for determining the fitness of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by the data acquisition module;
and the rehabilitation intervention scheme adjusting module is used for adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is within a threshold interval.
In some optional embodiments, the rehabilitation intervention program matching module comprises:
the system comprises an intervention time calculation unit, a data acquisition unit and a data processing unit, wherein the intervention time calculation unit is used for determining a perioperative period stage and a time length for learning and training of a perioperative period patient based on collected basic information, health conditions, motion habits and dietary habits of the perioperative period patient related to diseases, and the perioperative period stage comprises a preoperative period, an intra-operative period and a post-operative discharge period;
and the intervention scheme generating unit is used for matching out a corresponding rehabilitation intervention scheme from the preoperative phase database, the postoperative internal phase database or the postoperative discharge phase database based on the duration, recording rehabilitation training scheme items of perioperative patients related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients and sequencing the rehabilitation knowledge patient education scheme items related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients in different duration stages in the preoperative phase database, the postoperative internal phase database or the postoperative discharge phase database.
In some optional embodiments, the apparatus further comprises a database updating module for sorting the items of the plurality of rehabilitation knowledge development schemes in each database, and the database updating module comprises:
the first adjustment parameter determining unit is used for calculating keyword hit rates relevant to various rehabilitation knowledge education scheme projects based on question-answer conversations of perioperative patients, and taking the keyword hit rates as first adjustment parameters corresponding to the rehabilitation knowledge education schemes;
the second adjustment parameter determining unit is used for calculating a second adjustment parameter corresponding to the rehabilitation knowledge suffering and education scheme based on the retention time of the reading page of the perioperative patient for each rehabilitation knowledge suffering and education scheme;
the third adjustment parameter determining unit is used for taking the recommendation degree of the medical staff to each rehabilitation knowledge suffering teaching scheme as a third adjustment parameter corresponding to the rehabilitation knowledge suffering teaching scheme;
and the score calculating unit is used for carrying out weighted calculation on the first adjusting parameter, the second adjusting parameter and the third adjusting parameter to obtain the score of each rehabilitation knowledge suffering from education scheme, and carrying out item sequencing on each rehabilitation knowledge suffering from education scheme based on the score.
In some optional embodiments, the fitness calculation module comprises:
the questionnaire generating unit is used for matching a periodic symptom questionnaire according to the operation date of a perioperative patient, wherein each question of the periodic symptom questionnaire is a measure item question;
the questionnaire reply expression recognition unit is used for acquiring the expression of perioperative patients in replying to the stage symptom questionnaire based on a facial recognition technology;
a scale value generation unit for determining a first scale value of each question based on the stage symptom questionnaire of the responses, and determining a second scale value of the corresponding question based on the relationship between the expression and the scale comparison table;
and the comprehensive scale value calculation unit is used for determining a comprehensive scale value of the corresponding problem based on the first scale value and the second scale value.
And the first fitness calculating unit is used for determining the fitness of the rehabilitation intervention scheme on the current day based on the comprehensive scale values of all the problems.
In some optional embodiments, the fitness calculation module comprises:
the physical sign data collection unit is used for collecting the physical sign data of the perioperative patient on the current day and relevant to the operation based on the physical sign sensor;
the nutrition data collection unit is used for determining the energy structure of diet fed back by the patient in the perioperative period based on an image recognition technology so as to determine the nutrition data of the patient in the perioperative period;
and the second fitness calculating unit is used for determining the fitness of the rehabilitation intervention scheme on the day according to the given physical sign data interval and the given nutrition data interval.
In a third aspect of the present application, a computer device includes a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program for implementing an artificial intelligence based automatic intervention and adjustment method for surgical rehabilitation.
In a fourth aspect of the present application, a readable storage medium stores a computer program, which when executed by a processor, is used for implementing the artificial intelligence based automatic intervention and adjustment method for surgical rehabilitation. The computer-readable storage medium may be included in the apparatus described in the above embodiment; or may be present separately and not assembled into the device. The computer readable storage medium carries one or more programs which, when executed by the apparatus, process data in the manner described above.
Referring now to FIG. 2, there is shown a schematic block diagram of a computer device 400 suitable for use in implementing embodiments of the present application. The computer device shown in fig. 2 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.
As shown in fig. 2, the computer apparatus 400 includes a Central Processing Unit (CPU) 401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the device 400 are also stored. The CPU401, ROM402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the present application, the processes described above with reference to the flow charts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409 and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 401. It should be noted that the computer storage media of the present application can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or units described in the embodiments of the present application may be implemented by software or hardware. The modules or units described may also be provided in a processor, the names of which in some cases do not constitute a limitation of the module or unit itself.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An automatic intervention and adjustment method for surgical rehabilitation based on artificial intelligence is characterized by comprising the following steps:
s1, collecting basic information, health conditions, exercise habits and dietary habits of perioperative patients related to diseases, and matching an initial rehabilitation intervention scheme for pushing the patients every day, wherein the initial rehabilitation intervention scheme comprises but is not limited to a rehabilitation knowledge development scheme and a rehabilitation training scheme;
s2, determining the adaptation degree of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by a data acquisition module;
and S3, adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree, and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is within a threshold interval.
2. The method for adjusting automatic intervention for surgical rehabilitation based on artificial intelligence as claimed in claim 1, wherein the step S1 of matching the initial rehabilitation intervention plan for daily pushing to the patient specifically comprises:
s11, determining a perioperative period stage and a duration capable of learning and training of the perioperative period patient based on collected basic information, health conditions, exercise habits and dietary habits of the perioperative period patient related to diseases, wherein the perioperative period stage comprises a preoperative stage, a postsurgical hospital stage and a postoperative discharge stage;
and S12, matching corresponding rehabilitation intervention schemes from a preoperative stage database, a postoperative hospital inner stage database or a postoperative discharge stage database based on the duration, wherein rehabilitation training scheme items of perioperative patients related to the basic information, health condition, motion habits and dietary habits of the perioperative patients and rehabilitation knowledge suffering and education scheme item sequences related to the basic information, health condition, motion habits and dietary habits of the perioperative patients at different duration stages are recorded in the preoperative stage database, the postoperative hospital inner stage database or the postoperative discharge stage database.
3. The method for adjusting automatic intervention for surgical rehabilitation based on artificial intelligence as claimed in claim 2, wherein the step S12 further comprises updating the ranking result of each rehabilitation knowledge and education program in each database, which specifically comprises:
step S121, calculating keyword hit rates related to various rehabilitation knowledge development and education scheme projects based on question-answer conversations of perioperative patients, and taking the keyword hit rates as first adjustment parameters of corresponding rehabilitation knowledge development and education schemes;
step S122, calculating a second adjustment parameter corresponding to the rehabilitation knowledge suffering and teaching scheme based on the retention time of the reading page of each rehabilitation knowledge suffering and teaching scheme of the perioperative patient;
step S123, taking the recommendation degree of the medical staff to each rehabilitation knowledge suffering and education scheme as a third adjustment parameter corresponding to the rehabilitation knowledge suffering and education scheme;
step S124, carrying out weighted calculation on the first adjusting parameter, the second adjusting parameter and the third adjusting parameter to obtain the score of each rehabilitation knowledge suffering from education scheme, and carrying out item sequencing on each rehabilitation knowledge suffering from education scheme based on the score.
4. The artificial intelligence based automatic intervention and adjustment method for surgical rehabilitation as claimed in claim 1, wherein in step S2, determining the degree of adaptation of the perioperative patient during execution of the current day rehabilitation intervention program based on the feedback data of the questionnaire survey comprises:
step S21, matching a periodic symptom questionnaire according to the operation date of perioperative patients, wherein each question of the periodic symptom questionnaire is a measure item question;
step S22, obtaining the expression of the perioperative patient when answering the stage symptom questionnaire based on a facial recognition technology;
step S23, determining a first scale value of each question based on the responded stage symptom questionnaire, and determining a second scale value of the corresponding question based on the relation between the expression and the scale comparison table;
and S24, determining a comprehensive scale value of the corresponding problem based on the first scale value and the second scale value.
And S25, determining the adaptation degree of the rehabilitation intervention scheme on the day based on the comprehensive scale values of all the problems.
5. The artificial intelligence based automatic intervention and adjustment method for surgical rehabilitation, according to claim 1, wherein in the step S2, the determining the adaptation degree of the perioperative patient during the execution of the current day rehabilitation intervention scheme through the rehabilitation information collected by the data collection module comprises:
s26, collecting the current-day operation-related sign data of the perioperative patient based on the sign sensor;
s27, determining an energy structure of diet fed back by the perioperative patient based on an image recognition technology to determine nutritional data of the perioperative patient;
and S28, determining the adaptation degree of the rehabilitation intervention scheme on the day according to the given physical sign data interval and the given nutrition data interval.
6. An artificial intelligence based automatic intervention and adjustment system for surgical rehabilitation, which is characterized by comprising:
the rehabilitation intervention scheme matching module is used for collecting basic information, health conditions, exercise habits and dietary habits of the perioperative patients related to diseases and matching an initial rehabilitation intervention scheme for daily pushing to the patients, wherein the initial rehabilitation intervention scheme comprises but is not limited to a rehabilitation knowledge development scheme and a rehabilitation training scheme;
the adaptation degree calculation module is used for determining the adaptation degree of the perioperative patient in the process of executing the current-day rehabilitation intervention scheme through feedback data based on questionnaire survey and rehabilitation information acquired by the data acquisition module;
and the rehabilitation intervention scheme adjusting module is used for adjusting the rehabilitation intervention scheme pushed on the current day based on the adaptation degree and generating the rehabilitation intervention scheme pushed on the next day so that the adaptation degree is positioned in a threshold interval.
7. The artificial intelligence based surgical rehabilitation automated intervention adjusting system of claim 6, wherein the rehabilitation intervention program matching module comprises:
the system comprises an intervention time calculation unit, a data acquisition unit and a data processing unit, wherein the intervention time calculation unit is used for determining a perioperative period stage and a time length capable of learning and training of perioperative patients based on collected basic information, health conditions, exercise habits and dietary habits of the perioperative patients related to diseases, and the perioperative period stage comprises a preoperative stage, a postsurgical hospital stage and a postoperative discharge stage;
and the intervention scheme generating unit is used for matching out a corresponding rehabilitation intervention scheme from the preoperative phase database, the postoperative internal phase database or the postoperative discharge phase database based on the duration, recording rehabilitation training scheme items of perioperative patients related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients and sequencing the rehabilitation knowledge patient education scheme items related to the basic information, the health condition, the exercise habits and the dietary habits of the perioperative patients in different duration stages in the preoperative phase database, the postoperative internal phase database or the postoperative discharge phase database.
8. The artificial intelligence based automatic intervention and adjustment system for surgical rehabilitation according to claim 7, further comprising a database updating module for item ordering of a plurality of rehabilitation knowledge development plans in each database, the database updating module comprising:
the first adjustment parameter determining unit is used for calculating keyword hit rates relevant to various rehabilitation knowledge suffering and education scheme items based on question-answer conversations of perioperative patients, and taking the keyword hit rates as first adjustment parameters corresponding to the rehabilitation knowledge suffering and education schemes;
the second adjustment parameter determining unit is used for calculating second adjustment parameters corresponding to the rehabilitation knowledge suffering and teaching scheme based on the retention time of the reading page of the patient in the perioperative period for each rehabilitation knowledge suffering and teaching scheme;
the third adjustment parameter determining unit is used for taking the recommendation degree of the medical staff to each rehabilitation knowledge suffering teaching scheme as a third adjustment parameter corresponding to the rehabilitation knowledge suffering teaching scheme;
and the score calculating unit is used for carrying out weighted calculation on the first adjusting parameter, the second adjusting parameter and the third adjusting parameter to obtain the score of each rehabilitation knowledge suffering from education scheme, and carrying out item sequencing on each rehabilitation knowledge suffering from education scheme based on the score.
9. The artificial intelligence based automatic intervention and adjustment system for surgical rehabilitation according to claim 6, wherein the fitness calculating module comprises:
the questionnaire generating unit is used for matching a stage symptom questionnaire according to the operation date of a perioperative patient, wherein each question of the stage symptom questionnaire is a measure item question;
the questionnaire reply expression recognition unit is used for acquiring expressions of perioperative patients in replying the stage symptom questionnaire based on a facial recognition technology;
a scale value generation unit for determining a first scale value of each question based on the stage symptom questionnaire of the responses, and determining a second scale value of the corresponding question based on the relationship between the expression and the scale comparison table;
and the comprehensive scale value calculation unit is used for determining a comprehensive scale value of the corresponding problem based on the first scale value and the second scale value.
And the first fitness calculating unit is used for determining the fitness of the rehabilitation intervention scheme on the day based on the comprehensive scale values of all the problems.
10. The artificial intelligence based automatic intervention and adjustment system for surgical rehabilitation of claim 6, wherein the fitness calculation module comprises:
the physical sign data collection unit is used for collecting the physical sign data of the perioperative patient on the current day and relevant to the operation based on the physical sign sensor;
a nutritional data collection unit for determining an energy structure of a diet fed back by the perioperative patient based on image recognition technology to determine nutritional data of the perioperative patient;
and the second fitness calculating unit is used for determining the fitness of the rehabilitation intervention scheme on the day according to the given physical sign data interval and the given nutrition data interval.
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| CN117133402A (en) * | 2023-08-30 | 2023-11-28 | 广东省中医院（广州中医药大学第二附属医院、广州中医药大学第二临床医学院、广东省中医药科学院） | Method, device, equipment and readable storage medium for dynamically supervising patient rehabilitation |
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| CN116343989A (en) * | 2023-03-09 | 2023-06-27 | 北京体育大学 | Digital training regulation and control method and system based on remote monitoring |
| CN116153531A (en) * | 2023-04-17 | 2023-05-23 | 北京康爱医疗科技股份有限公司 | Rehabilitation monitoring method and system for tumor patient |
| CN116665871A (en) * | 2023-08-02 | 2023-08-29 | 上海迎智正能文化发展有限公司 | Monitoring scheme optimization method and system based on big data |
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