CN116434916B - Digital nutrition management method for tumor rehabilitation - Google Patents
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention provides a digital nutrition management method for tumor rehabilitation, which comprises the following steps: carrying out centralized management analysis on case information and body information of a patient, and determining an initial recipe by combining nutrition; based on the body information of the historical patient, acquiring a historical record corresponding to the historical patient with the patient matching degree higher than the preset matching degree from the digital nutrition record of the historical patient, and verifying and adjusting the initial recipe based on the historical record to obtain a target recipe; the method and the system realize a series of comprehensive management for recipe updating according to the feedback of the patient on the recipes and provide scientific and nutritional recipes for the patient.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a digital nutrition management method for tumor rehabilitation.
Background
Tumor diseases are one of the diseases which people are afraid of, especially malignant tumors are encountered, a patient often bears huge physical burden in the treatment process, and in order to prevent recurrence and control tumor cells, follow-up rehabilitation therapy is also important to pay attention, wherein diet of the patient is also an important ring for tumor rehabilitation, how to conduct centralized management and analysis on information of the patient, determine a scientific nutrition recipe, and realize a series of comprehensive management of recipe updating according to feedback of the patient to the recipe, so that providing efficient and high-quality recipe management service for the patient is an urgent problem to be solved.
Disclosure of Invention
The invention provides a digital nutrition management method for tumor rehabilitation, which realizes a series of comprehensive management of recipe updating according to the feedback of a patient on recipes and provides scientific nutrition recipes for the patient.
The invention provides a digital nutrition management method for tumor rehabilitation, which comprises the following steps:
s1: carrying out centralized management analysis on case information and body information of a patient, and determining an initial recipe by combining nutrition;
s2: based on the body information of the historical patient, acquiring a historical record corresponding to the historical patient with the patient matching degree higher than the preset matching degree from the digital nutrition record of the historical patient, and verifying and adjusting the initial recipe based on the historical record to obtain a target recipe;
s3: and acquiring feedback information and body response information of the patient on the target recipe, and updating the target recipe.
Preferably, in S1, determining an initial recipe based on the centralized management analysis of case information and body information of the patient in combination with nutrition includes:
establishing a human body characteristic map according to the body information of the patient, marking the case information on the human body characteristic map, and obtaining the current body state information of the patient according to the marking result;
Comparing the current physical state information of the patient with the standard state information to determine an abnormal state;
according to nutrition, supplementary foods for abnormal states are determined, and based on the supplementary foods, an initial recipe is designed.
Preferably, the method for determining a supplementary food in an abnormal state according to nutrition and designing an initial recipe based on the supplementary food comprises:
establishing a human body characteristic map according to the body information of the patient, marking the case information on the human body characteristic map, and obtaining the current body state information of the patient according to the marking result;
comparing the current physical state information of the patient with standard state information to determine abnormal state information;
according to nutrition, supplementary foods for abnormal state information are determined, and an initial recipe is designed based on the supplementary foods.
Preferably, the supplementary food for determining abnormal state information according to nutrition, and the initial recipe is designed based on the supplementary food, comprising:
acquiring tabu information matched with abnormal state information from a tumor treatment database, and acquiring tabu food from the tabu information;
determining supplementary nutrition for the abnormal state information based on nutrition, and determining supplementary food corresponding to the supplementary nutrition;
And removing the tabu food from the supplementary food to obtain a target food, and obtaining an initial recipe according to the target food.
Preferably, in S2, from the historical patient digital nutrition record, a historical record similar to the patient is obtained, including:
s2, based on the body information of the historical patient, acquiring the historical record corresponding to the historical patient with the matching degree of the patient higher than the preset matching degree from the digital nutritional record of the historical patient, wherein the method comprises the following steps:
according to the historical rehabilitation record, body characteristic attributes with influence on tumor rehabilitation larger than preset influence are obtained to serve as main body attributes, and other body information serves as secondary body attributes;
dividing body information of a patient based on the main body attribute and the secondary body attribute to obtain main information and secondary information, and acquiring main body characteristics corresponding to the main information and secondary body characteristics corresponding to the secondary information;
matching the main body characteristics and the secondary body characteristics with the historical patients according to the main weight and the secondary weight respectively, and obtaining a target historical patient with the body matching degree larger than the preset matching degree;
the digital nutrition records of the target historical patient are obtained from the historical patient digital nutrition records and serve as the historical records corresponding to the historical patients with the matching degree of the patients higher than the preset matching degree.
Preferably, in S2, the verification and adjustment are performed on the initial recipe based on the history record, so as to obtain the target recipe, including:
acquiring a reference recipe and recovery information of a historical patient from a historical record;
comparing all the reference recipes, determining common recipe characteristics and individual recipe characteristics of each reference recipe, and dividing recovery information to obtain quick recovery characteristics and abnormal recovery characteristics;
acquiring characteristic corresponding relations between the common recipe characteristics and the individual recipe characteristics and the quick recovery characteristics and the abnormal recovery characteristics;
determining a first recovery effect of the common recipe features of each reference recipe from the feature correspondence, determining a second recovery effect of the individual recipe features of each reference recipe from the feature correspondence, and determining a comprehensive recovery effect of each reference recipe according to the first recovery effect and the second recovery effect;
according to the comprehensive recovery effect, setting verification information of each recipe characteristic of the reference recipes;
determining common target characteristics and individual target characteristics of the target recipes, verifying the common target characteristics and the individual target characteristics based on verification information, and judging whether the common target characteristics and the individual target characteristics pass verification;
if yes, taking the initial recipe as a target recipe;
Otherwise, extracting common characteristics to be adjusted, which are not verified, from the common target characteristics, adjusting the common characteristics to be adjusted by utilizing the common recipe characteristics, extracting individual characteristics to be adjusted, which are not verified, from the individual target characteristics, and adjusting the individual characteristics to be adjusted by utilizing the individual recipe characteristics to obtain the latest target characteristics after adjustment;
based on the latest target features, a target recipe is obtained.
Preferably, the verification information is a feature passing value set for each recipe feature according to the comprehensive recovery effect, and when the feature passing value is lower than the feature passing value, verification is not passed, otherwise, verification is passed.
Preferably, in S3, the step of acquiring feedback information and body response information of the patient on the target recipe, and updating the target recipe includes:
s31: periodically sending a questionnaire survey of the target recipe to the patient, acquiring feedback information of the patient, periodically performing physical examination on the patient, acquiring physical response information of the patient, and determining an evaluation index of the target recipe according to tumor information of the patient;
s32: acquiring a first keyword related to an evaluation index from feedback information, and acquiring a second keyword related to the evaluation from body reaction information;
S33: determining a first evaluation value under an evaluation index based on a first keyword, determining a second evaluation value under the evaluation index based on a second keyword, forming an evaluation feature set according to the first evaluation value and the second evaluation value, determining an update strategy of the evaluation feature set under the evaluation index, and forming an update judgment rule set according to the update strategy of all the evaluation indexes;
s34: generating an updating decision model based on the evaluation index, wherein the updating decision model is generated by the evaluation feature set and the updating decision rule set, the updating decision rule set is dynamically updated by utilizing a real-time tumor restoration guidance rule, and the evaluation feature set is dynamically updated according to a first keyword and a second keyword which are regularly acquired; acquiring an updating decision result of the updating decision model in real time by utilizing the dynamic updating result;
s35: when the updating decision result is that the target recipe needs to be updated, the target recipe is updated by utilizing the latest evaluation feature set and combining nutrition; and when the updating decision result is that the updating of the target recipe is not needed, keeping the target recipe unchanged.
Preferably, updating the target recipe with the latest set of evaluation features in combination with the nutrition comprises:
when the updating strategy results are updated, selecting a first evaluation value and a second evaluation value which are lower than a preset evaluation value range from the latest evaluation feature set, and acquiring abnormal evaluation indexes corresponding to the first evaluation value and the second evaluation value which are lower than the preset evaluation value range;
Acquiring a real-time abnormal state under an abnormal evaluation index;
according to the nutrition, determining the lack of elements in the real-time abnormal state, and updating the target recipe by combining the nutrition.
Preferably, the method further comprises:
recording the feeding time point and the defecation time point of a patient according to the target recipe and the feeding information of the patient on the food of the target recipe, and determining the matching degree of the target recipe and the patient according to the following formula based on the feeding time point, the defecation time point and the feeding information;
；
wherein ,indicating the matching degree of the target recipe and the patient, n indicating the eating type number of the target recipe food by the patient, m indicating the non-eating type number of the target recipe food by the patient, and +.>Nutritional value representing the patient's ith food type of target recipe food, +.>The j-th of the patient's target recipe food is the nutritional value of the eating type, +.>Time difference representing feeding time point and defecation time point, +.>Represents standard time difference, +.>Indicating the length of time the patient has taken the target recipe food,/->Indicating a standard food intake duration;
if the matching degree of the target recipe and the patient is larger than the preset matching degree, archiving and recording the target recipe after the target recipe is updated, otherwise, not recording the target recipe after the target recipe is updated;
Acquiring the number of the target recipes recorded in the archive and the matching degree of each target recipe and a patient, and evaluating the accuracy of the target recipe acquisition method according to the following formula;
；
wherein ,representing the accuracy of the target recipe acquisition method, +.>Indicating the number of times of judgment of whether to update the target recipe,/->Representing the number of updates to the target recipe, b representing the number of target recipes to archive the record,indicate->Matching degree of the target recipes of the archive records and the patient;
if the accuracy of the target recipe acquisition method is greater than the preset accuracy, continuing to use the target recipe acquisition method; otherwise, the target recipe acquisition method is adjusted.
Preferably, the adjusting the target recipe obtaining method includes: taking the eating time point, the defecation time point and the eating information as personalized features, supplementing the evaluation indexes in the step S31, using the supplemented evaluation indexes, expanding the evaluation feature set correspondingly, and updating the target recipe by using the expanded evaluation feature set in the same way as the step S35.
Compared with the prior art, the invention has the following beneficial effects:
1) The method comprises the steps of carrying out centralized management analysis on case information and body information of a patient, combining nutrition, determining an initial recipe, obtaining a history record similar to the patient from a digital nutrition record of the history patient, verifying and adjusting the initial recipe based on the history record to obtain a target recipe, obtaining feedback information and body response information of the patient on the target recipe, updating the target recipe, realizing a series of comprehensive management of recipe updating according to feedback of the patient on the recipe, and providing scientific nutrition recipe for the patient.
2) The characteristic corresponding relation between the recipe and the recovery is determined according to the recipe information and the recovery condition of the historical patient, and the initial recipe is analyzed, verified and adjusted by utilizing the characteristic corresponding relation, so that the effective effect of the obtained target recipe on the recovery effect of the patient is ensured.
3) The method comprises the steps of acquiring real-time data information of a patient and rule information for tumor recovery, judging whether the target recipe needs to be updated according to the combined action of the real-time data information and the rule information for tumor recovery, analyzing and updating the target recipe from two parts of internal factors of the physical condition of the patient and the change of rules for tumor recovery, namely external factors, and generating an update decision model through centralized and combined management of the information of the patient and the external information to automatically and intelligently analyze whether the update of the target recipe is needed or not, so that the progress of the target recipe and the physical condition requirement of the patient are ensured.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a digital nutrition management method for tumor rehabilitation according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for obtaining a history record similar to a patient in an embodiment of the invention;
FIG. 3 is a flowchart of updating a target recipe according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
an embodiment of the present invention provides a digital nutrition management method for tumor rehabilitation, as shown in fig. 1, including:
s1: carrying out centralized management analysis on case information and body information of a patient, and determining an initial recipe by combining nutrition;
s2: based on the body information of the historical patient, acquiring a historical record corresponding to the historical patient with the patient matching degree higher than the preset matching degree from the digital nutrition record of the historical patient, and verifying and adjusting the initial recipe based on the historical record to obtain a target recipe;
S3: and acquiring feedback information and body response information of the patient on the target recipe, and updating the target recipe.
In this embodiment, the nutrition may determine the patient's missing nutrition based on physical conditions, and the supplementary food may be determined as an initial recipe based on the missing nutrition.
In this embodiment, the feedback information of the patient to the target recipe is oral feeling information of the patient, and the body reaction information is physical examination parameter information.
In this embodiment, the case information and the body information of the patient are subjected to centralized management analysis to concentrate the information acquired by both together for analysis.
In this embodiment, the verification and adjustment of the initial recipe based on the history is specifically the adjustment of the initial recipe based on experience with the history.
In this embodiment, a history having a degree of matching with the patient higher than a preset degree of matching is obtained, specifically, a history determined from the degree of matching of historical patient information in a digital nutrition record of the historical patient with the physical condition of the patient, in which the physical information of the historical patient, recipe information, and tumor recovery information are recorded.
In this embodiment, updating the target recipe is specifically to adjust the target recipe according to the latest condition of the patient according to feedback information and body response information of the patient to the target recipe, so as to satisfy the real-time physical state of the patient.
The beneficial effects of above-mentioned design scheme are: the method comprises the steps of carrying out centralized management analysis on case information and body information of a patient, combining nutrition, determining an initial recipe, obtaining a history record similar to the patient from a digital nutrition record of the history patient, verifying and adjusting the initial recipe based on the history record to obtain a target recipe, obtaining feedback information and body response information of the patient on the target recipe, updating the target recipe, realizing a series of comprehensive management of recipe updating according to feedback of the patient on the recipe, and providing scientific nutrition recipe for the patient.
Example 2:
based on embodiment 1, the embodiment of the invention provides a digital nutrition management method for tumor rehabilitation, in S1, according to the centralized management analysis of case information and body information of a patient, and in combination with nutrition, an initial recipe is determined, which comprises:
establishing a human body characteristic map according to the body information of the patient, marking the case information on the human body characteristic map, and obtaining the current body state information of the patient according to the marking result;
comparing the current physical state information of the patient with standard state information to determine abnormal state information;
According to nutrition, supplementary foods for abnormal state information are determined, and an initial recipe is designed based on the supplementary foods.
In this embodiment, the human body characteristic map is a map obtained from characteristics of respective parts of the patient determined based on body information of the patient.
In this embodiment, the case information is marked on a human body feature map, current physical state information of the patient is obtained according to the marking result, specifically, for example, postoperative wound information of the patient is obtained in the case information, a general tumor patient can leave a wound or a scar after an operation, the postoperative wound is marked on the human body feature map, the state corresponding to the wound is obtained by combining the characteristics of the postoperative wound on the human body feature map, such as the position, the shape and the like, the state corresponding to the wound is obtained as one factor of the physical state information, if the fact that the postoperative wound is inconsistent with the wound information under the standard state information is detected, the wound state is used as abnormal state information, and corresponding supplementary food is food beneficial to wound healing. Wherein, the wound under the standard state information refers to a recovered state that the wound can reach according to the passage of time under the condition of meeting the normal recovery. The inconsistency of wound information between a post-operative wound and standard state information can be generally manifested as a pus formation, a darkening of the wound, a ulceration, an outspread of the wound site, a lack of shrinkage of the wound within an expected recovery time, a slow recovery, etc.
In this embodiment, visual inspection of the physical state of the patient is facilitated by establishing a body profile.
The beneficial effects of above-mentioned design scheme are: by determining the abnormal state according to the specific illness and physical condition of the patient, and determining the supplementary food of the abnormal state according to nutrition, the nutrition generally provides specific nutrition collocation recipe suggestions for the patient with the specific illness, such as a wound recovery period, can not eat greasy food, can not eat spicy and stimulating and developing food, such as seafood and the like, and the food rich in protein and the fruit and vegetable rich in cellulose are beneficial to wound healing, can be used as the supplementary food, and based on the supplementary food, an initial recipe is designed to ensure the effectiveness of the obtained initial recipe on the rehabilitation of the patient.
Example 3:
based on embodiment 2, an embodiment of the present invention provides a digital nutrition management method for tumor rehabilitation, according to nutrition, determining supplementary food of abnormal state information, and designing an initial recipe based on the supplementary food, including:
acquiring tabu information matched with abnormal state information from a tumor treatment database, and acquiring tabu food from the tabu information; the contraindicated food needs to be removed from the recipe, so that the influence on tumor treatment is avoided;
Determining supplementary nutrition for the abnormal state information based on nutrition, and determining supplementary food corresponding to the supplementary nutrition; for foods which are favorable for tumor recovery, if the foods are not contained in the original recipe, the foods can be added as supplementary foods to improve the tumor recovery efficiency.
And removing the tabu food from the supplementary food to obtain a target food, and obtaining an initial recipe according to the target food.
The beneficial effects of above-mentioned design scheme are: the safety of the initial recipe is ensured, the effectiveness of the obtained initial recipe on the rehabilitation of the patient is ensured, and the initial recipe can be flexibly adjusted by combining different tumor types and abnormal state information. The nutrition is well known in the art, and the core of the protection of the invention is to flexibly combine the nutrition content as a reference basis for adjusting recipes.
Example 4:
based on embodiment 1, an embodiment of the present invention provides a digital nutrition management method for tumor rehabilitation, as shown in fig. 2, in S2, based on body information of a historical patient, a historical record corresponding to a historical patient with a patient matching degree higher than a preset matching degree is obtained from the historical patient digital nutrition record, including:
S21: according to the historical rehabilitation record, body characteristic attributes with influence on tumor rehabilitation larger than preset influence are obtained to serve as main body attributes, and other body information serves as secondary body attributes;
s22: dividing body information of a patient based on the main body attribute and the secondary body attribute to obtain main information and secondary information, and acquiring main body characteristics corresponding to the main information and secondary body characteristics corresponding to the secondary information;
s23: matching the main body characteristics and the secondary body characteristics with the historical patients according to the main weight and the secondary weight respectively, and obtaining a target historical patient with the body matching degree larger than the preset matching degree;
s24: the digital nutrition records of the target historical patient are obtained from the historical patient digital nutrition records and serve as the historical records corresponding to the historical patients with the matching degree of the patients higher than the preset matching degree.
In this embodiment, for example, the primary physical attribute is obesity, body weight, hypertension or other accompanying diseases tend to be more likely due to the characteristics of obesity, and the influence of other secondary physical attributes, such as gender, may be smaller for the recovery process of a tumor patient.
The beneficial effects of above-mentioned design scheme are: the body information of the patient is divided into the main body characteristics and the sub body characteristics, the main body characteristics are provided with larger weights, the sub body characteristics are provided with smaller weights, and the main body characteristics and the sub body characteristics are compared and matched with the historical patient, so that the obtained target historical patient is more matched with the condition of the patient, the accuracy of a historical record similar to the patient is ensured, and a basis is provided for determining a scientific nutrition target recipe.
Example 5:
based on embodiment 1, the embodiment of the invention provides a digital nutrition management method for tumor rehabilitation, in S2, the initial recipe is verified and adjusted based on the history record to obtain a target recipe, which comprises the following steps:
acquiring a reference recipe and recovery information of a historical patient from a historical record;
comparing all the reference recipes, determining common recipe characteristics and individual recipe characteristics of each reference recipe, and dividing recovery information to obtain quick recovery characteristics and abnormal recovery characteristics;
acquiring characteristic corresponding relations between the common recipe characteristics and the individual recipe characteristics and the quick recovery characteristics and the abnormal recovery characteristics;
determining a first recovery effect of the common recipe features of each reference recipe from the feature correspondence, determining a second recovery effect of the individual recipe features of each reference recipe from the feature correspondence, and determining a comprehensive recovery effect of each reference recipe according to the first recovery effect and the second recovery effect;
according to the comprehensive recovery effect, setting verification information of each recipe characteristic of the reference recipes;
determining common target characteristics and individual target characteristics of the target recipes, verifying the common target characteristics and the individual target characteristics based on verification information, and judging whether the common target characteristics and the individual target characteristics pass verification;
If yes, taking the initial recipe as a target recipe;
otherwise, extracting common characteristics to be adjusted, which are not verified, from the common target characteristics, adjusting the common characteristics to be adjusted by utilizing the common recipe characteristics, extracting individual characteristics to be adjusted, which are not verified, from the individual target characteristics, and adjusting the individual characteristics to be adjusted by utilizing the individual recipe characteristics to obtain the latest target characteristics after adjustment;
based on the latest target features, a target recipe is obtained.
In this embodiment, the common target feature and the personalized target feature of the target recipe may be matched from the common recipe feature and the personalized recipe feature of the total reference recipe.
In this example, it was verified that there was a problem not passing the amount or composition characterizing the recipe.
In this embodiment, the comprehensive recovery effect is obtained by superimposing the first recovery effect and the second recovery effect, and under the same type of effect, the better one of the first recovery effect and the second recovery effect is taken, and the comprehensive recovery effect is obtained.
In this embodiment, the feature correspondence is, for example, a fast recovery feature of the first recovery type for which the first common recipe feature has a good effect on the first recovery type, and also, for example, an abnormal recovery feature of the first recovery type for which the second common recipe feature has a bad effect on the first recovery type.
The beneficial effects of above-mentioned design scheme are: the characteristic corresponding relation between the recipe and the recovery is determined according to the recipe information and the recovery condition of the historical patient, and the initial recipe is analyzed, verified and adjusted by utilizing the characteristic corresponding relation, so that the effective effect of the obtained target recipe on the recovery effect of the patient is ensured.
Example 6:
based on embodiment 5, the embodiment of the invention provides a digital nutrition management method for tumor rehabilitation, verification information is that a characteristic passing value is set for each recipe characteristic according to the comprehensive recovery effect, when the characteristic passing value is lower than the characteristic passing value, verification is not passed, and otherwise, verification is passed.
In this embodiment, the feature passing value is used to determine according to the comprehensive recovery effect, the better the effect, the higher the feature passing value, for example, when the recipe feature is 100 g of carrot, the corresponding effect is good, the feature passing value of the recipe feature is 0.9, and when the recipe feature is 50 g of corn, the corresponding effect is general, the feature passing value of the recipe feature is 0.6.
The beneficial effects of above-mentioned design scheme are: by setting a feature passing value for each recipe feature, a verification result is determined, so that the simple and feasible verification and the accuracy of the verification result are ensured.
Example 7:
based on embodiment 1, an embodiment of the present invention provides a digital nutrition management method for tumor rehabilitation, as shown in fig. 3, in S3, feedback information and body response information of a patient on a target recipe are obtained, and the target recipe is updated, including:
s31: periodically sending a questionnaire survey of the target recipe to the patient, acquiring feedback information of the patient, periodically performing physical examination on the patient, acquiring physical response information of the patient, and determining an evaluation index of the target recipe according to tumor information of the patient;
s32: acquiring a first keyword related to an evaluation index from feedback information, and acquiring a second keyword related to the evaluation from body reaction information;
s33: determining a first evaluation value under an evaluation index based on a first keyword, determining a second evaluation value under the evaluation index based on a second keyword, forming an evaluation feature set according to the first evaluation value and the second evaluation value, determining an update strategy of the evaluation feature set under the evaluation index, and forming an update judgment rule set according to the update strategy of all the evaluation indexes;
s34: generating an updating decision model based on the evaluation index, wherein the updating decision model is generated by the evaluation feature set and the updating decision rule set, the updating decision rule set is dynamically updated by utilizing a real-time tumor restoration guidance rule, and the evaluation feature set is dynamically updated according to a first keyword and a second keyword which are regularly acquired; acquiring an updating decision result of the updating decision model in real time by utilizing the dynamic updating result;
S35: when the updating decision result is that the target recipe needs to be updated, the target recipe is updated by utilizing the latest evaluation feature set and combining nutrition; and when the updating decision result is that the updating of the target recipe is not needed, keeping the target recipe unchanged.
In this embodiment, the evaluation index, for example, the wound recovery index, for example, the wound area change condition, the wound color change condition, the wound depth change condition, and the like, the physical parameter index, for example, the physical detection parameter of the complications possibly accompanying the operation such as fever, venous thrombosis, cardiovascular accident, and the like, determines the feeling of the patient according to the feedback information of the patient under the wound recovery index, the first keyword corresponding to the evaluation feature set is normal or good, the first evaluation value corresponding to the evaluation feature set is 0.7 or 0.9, the second keyword is the detection parameter value and the corresponding evaluation (including normal or abnormal) thereof according to the physical reaction information of the patient under the wound recovery index, for example, the wound detection parameter is normal or abnormal, and the corresponding second evaluation value is 0.8 or 0.4.
In this embodiment, the first keyword is determined based on the oral feeling of the user, for example, the keyword of "feeling good, feeling bad" or the like is the query result of the wound pain, the better the feeling is, the higher the first evaluation value of the first keyword is, the second keyword is the detection data of the body of the user such as the body temperature, the wound area, the cardiovascular and the like, the corresponding wound recovery index and the detection data under the body parameter index.
In this embodiment, the update policy is whether an update to the target recipe is required.
In this embodiment, the update judgment rule set is dynamically updated by using a real-time tumor restoration guidance rule, for example, the feeling of the patient on the wound is better under the wound restoration index before, the corresponding first evaluation value is 0.9, the actual detection parameter of the wound is within the standard range, everything is normal, the corresponding second evaluation value is 0.9, which indicates that the current recipe meets the restoration requirement, the corresponding update policy is not updated, but the requirement on the wound restoration index is higher because the real-time change of the tumor restoration guidance rule, for example, the restoration standard of the area change of the wound is improved, the previous guidance rule is that the area change is reduced by 2% per week, the requirement can be met only by being improved to 5% per week, if the current detection is reduced by 3% per week, the oral feeling of the patient is not changed, the first evaluation value is 0.9, the second keyword is changed from normal to abnormal, the second evaluation value is lowered, and the second evaluation value is 0.5, and the update is needed.
In this embodiment, based on the evaluation index, the evaluation feature set and the update judgment rule set generate an update decision model, and the change of data is periodically acquired according to an external real-time data change, such as a change of a tumor restoration guidance rule, and both the evaluation feature set and the update judgment rule set are updated so that a decision result of generating the update decision model changes.
In this embodiment, the latest evaluation feature set is an evaluation feature set obtained by dynamically updating the evaluation feature set for the latest time, and the updated evaluation feature set includes the latest first evaluation value and the latest second evaluation value generated for the latest first keyword and the latest second keyword.
The beneficial effects of above-mentioned design scheme are: the method comprises the steps of acquiring real-time data information of a patient and rule information for tumor recovery, judging whether the target recipe needs to be updated according to the combined action of the real-time data information and the rule information for tumor recovery, analyzing and updating the target recipe from two parts of internal factors of the physical condition of the patient and the change of rules for tumor recovery, namely external factors, and generating an update decision model through centralized and combined management of the information of the patient and the external information to automatically and intelligently analyze whether the update decision model needs to be performed, so that the progress of the target recipe and the physical condition of the patient are ensured, scientific nutrition of the target recipe is ensured, and scientific nutrition management for tumor recovery is realized.
Example 8
Based on embodiment 7, the embodiment of the invention provides a digital nutrition management method for tumor rehabilitation, which uses the latest evaluation feature set and combines nutrition to update a target recipe, comprising:
When the updating strategy results are updated, selecting a first evaluation value and a second evaluation value which are lower than a preset evaluation value range from the latest evaluation feature set, and acquiring abnormal evaluation indexes corresponding to the first evaluation value and the second evaluation value which are lower than the preset evaluation value range;
acquiring a real-time abnormal state under an abnormal evaluation index;
according to the nutrition, determining the lack of elements in the real-time abnormal state, and updating the target recipe by combining the nutrition.
In this embodiment, the latest evaluation feature set is the one obtained by dynamically updating the evaluation feature set for the latest time.
The beneficial effects of above-mentioned design scheme are: the target recipe is updated according to the lack of elements in the real-time abnormal state and by combining nutrition, so that the real-time performance of the target recipe is ensured.
Example 9:
based on embodiment 7, the embodiment of the invention provides a digital nutrition management method for tumor rehabilitation, which further comprises the following steps:
recording the feeding time point and the defecation time point of a patient according to the target recipe and the feeding information of the patient on the food of the target recipe, and determining the matching degree of the target recipe and the patient according to the following formula based on the feeding time point, the defecation time point and the feeding information;
；
wherein ,indicating the matching degree of the target recipe and the patient, n indicates the eating type number of the patient to the target recipe food, and m tableIndicating the number of non-fed types of target recipe foods for the patient,/->Nutritional value representing the patient's ith food type of target recipe food, +.>The j-th of the patient's target recipe food is the nutritional value of the eating type, +.>Time difference representing feeding time point and defecation time point, +.>Represents standard time difference, +.>Indicating the length of time the patient has consumed the target recipe food,indicating a standard food intake duration;
if the matching degree of the target recipe and the patient is larger than the preset matching degree, archiving and recording the target recipe after the target recipe is updated, otherwise, not recording the target recipe after the target recipe is updated;
acquiring the number of the target recipes recorded in the archive and the matching degree of each target recipe and a patient, and evaluating the accuracy of the target recipe acquisition method according to the following formula;
；
wherein ,representing the accuracy of the target recipe acquisition method, +.>Indicating the number of times of judgment of whether to update the target recipe,/->Representing the number of updates to the target recipe, b representing the number of target recipes to archive the record, Indicate->Matching degree of the target recipes of the archive records and the patient;
if the accuracy of the target recipe acquisition method is greater than the preset accuracy, continuing to use the target recipe acquisition method; otherwise, the target recipe acquisition method is adjusted.
In this embodiment, the matching degree of the target recipe to the patient is used to indicate the suitability of the target recipe to the patient.
In this embodiment, the eating information of the target recipe food includes the eating time period, and the case of the food that is eaten and the remaining food that is not eaten.
In this embodiment, the target recipe acquisition method is S1-S3.
The beneficial effects of above-mentioned design scheme are: according to the method, the matching degree of a target recipe and a patient is determined according to the food taking time point, the defecation time point and the food taking information of the patient, the archive record is carried out, the matching degree of the target recipe and the patient is larger than the preset matching degree, the archive record is used as a target recipe set of the patient, a foundation is provided for subsequent recipe design, meanwhile, unsatisfied target recipes are removed, improper recipes are removed, management of the target recipes of the patient is achieved, the number of the recorded target recipes and the matching degree of each target recipe and the patient are also archived, the accuracy of the target recipe acquisition method is evaluated, real-time adjustment can be carried out on the target recipe acquisition method according to the evaluation result, real-time monitoring adjustment management of the target recipe acquisition method is achieved, a series of comprehensive management of recipe updating according to the feedback of the patient to the recipes is achieved, and scientific nutrition recipes are provided for the patient.
Example 10:
based on embodiment 9, the embodiment of the invention provides a digital nutrition management method for tumor rehabilitation, which adjusts the target recipe acquisition method, comprising the following steps: taking the eating time point, the defecation time point and the eating information as personalized features, supplementing the evaluation indexes in the step S31, using the supplemented evaluation indexes, expanding the evaluation feature set correspondingly, and updating the target recipe by using the expanded evaluation feature set in the same way as the step S35.
In this embodiment, in order to enhance the personalized features of the patient in the determination of the target recipe acquisition method, specifically, when the feedback information and the body response information of the patient to the target recipe are acquired in S3 of the target recipe acquisition method, the target recipe is updated by adding the monitored personalized information belonging to the patient, which is obtained by monitoring the eating time point, the defecation time point and the eating information under the target recipe, which are obtained by the previous update of the patient.
In this embodiment, the personalized information of the patient, which is obtained by monitoring the feeding time point, the defecation time point and the feeding information obtained by updating the last time under the target recipe, is added, specifically, the personalized information of the patient, which is obtained by monitoring the feeding time point, the defecation time point and the feeding information, is supplemented to the evaluation index in step S31, for example, the defecation time index, the feeding information index, etc., the corresponding extended evaluation feature set is added, and the target recipe is updated by using the extended evaluation feature set in combination with nutrition according to the same manner as in step S35.
The beneficial effects of above-mentioned design scheme are: the personalized characteristics of the patient are enhanced in the method for acquiring the determined target recipe, specifically, the evaluation indexes are expanded, and the target recipe is updated by combining the expanded evaluation characteristic set with nutrition, so that the determined target recipe is more matched with the patient, and a scientific and nutritional recipe is provided for the patient.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A digital nutrition management method for tumor rehabilitation, comprising:
s1: carrying out centralized management analysis on case information and body information of a patient, and determining an initial recipe by combining nutrition;
s2: based on the body information of the historical patient, acquiring a historical record corresponding to the historical patient with the patient matching degree higher than the preset matching degree from the digital nutrition record of the historical patient, and verifying and adjusting the initial recipe based on the historical record to obtain a target recipe;
S3: acquiring feedback information and body response information of a patient on a target recipe, and updating the target recipe;
in S2, verifying and adjusting the initial recipe based on the history record to obtain a target recipe, including:
acquiring a reference recipe and recovery information of a historical patient from a historical record;
comparing all the reference recipes, determining common recipe characteristics and individual recipe characteristics of each reference recipe, and dividing recovery information to obtain quick recovery characteristics and abnormal recovery characteristics;
acquiring characteristic corresponding relations between the common recipe characteristics and the individual recipe characteristics and the quick recovery characteristics and the abnormal recovery characteristics;
determining a first recovery effect of the common recipe features of each reference recipe from the feature correspondence, determining a second recovery effect of the individual recipe features of each reference recipe from the feature correspondence, and determining a comprehensive recovery effect of each reference recipe according to the first recovery effect and the second recovery effect;
according to the comprehensive recovery effect, setting verification information of each recipe characteristic of the reference recipes;
determining common target characteristics and individual target characteristics of the target recipes, verifying the common target characteristics and the individual target characteristics based on verification information, and judging whether the common target characteristics and the individual target characteristics pass verification;
If yes, taking the initial recipe as a target recipe;
otherwise, extracting common characteristics to be adjusted, which are not verified, from the common target characteristics, adjusting the common characteristics to be adjusted by utilizing the common recipe characteristics, extracting individual characteristics to be adjusted, which are not verified, from the individual target characteristics, and adjusting the individual characteristics to be adjusted by utilizing the individual recipe characteristics to obtain the latest target characteristics after adjustment;
based on the latest target features, a target recipe is obtained.
2. The digital nutrition management method for tumor rehabilitation according to claim 1, wherein in S1, determining an initial recipe based on the centralized management analysis of case information and body information of a patient in combination with nutrition comprises:
establishing a human body characteristic map according to the body information of the patient, marking the case information on the human body characteristic map, and obtaining the current body state information of the patient according to the marking result;
comparing the current physical state information of the patient with standard state information to determine abnormal state information;
according to nutrition, supplementary foods for abnormal state information are determined, and an initial recipe is designed based on the supplementary foods.
3. A digital nutrition management method for tumour rehabilitation according to claim 2, wherein the determination of supplementary food for abnormal state information according to nutrition and the design of an initial recipe based on the supplementary food comprises:
acquiring tabu information matched with abnormal state information from a tumor treatment database, and acquiring tabu food from the tabu information;
determining supplementary nutrition for the abnormal state information based on nutrition, and determining supplementary food corresponding to the supplementary nutrition;
and removing the tabu food from the supplementary food to obtain a target food, and obtaining an initial recipe according to the target food.
4. The digital nutrition management method for tumor rehabilitation according to claim 1, wherein in S2, based on the body information of the historical patient, obtaining the historical record corresponding to the historical patient with the patient matching degree higher than the preset matching degree from the historical patient digital nutrition record, comprises:
according to the historical rehabilitation record, body characteristic attributes with influence on tumor rehabilitation larger than preset influence are obtained to serve as main body attributes, and other body information serves as secondary body attributes;
dividing body information of a patient based on the main body attribute and the secondary body attribute to obtain main information and secondary information, and acquiring main body characteristics corresponding to the main information and secondary body characteristics corresponding to the secondary information;
Matching the main body characteristics and the secondary body characteristics with the historical patients according to the main weight and the secondary weight respectively, and obtaining a target historical patient with the body matching degree larger than the preset matching degree;
and acquiring the digital nutrition records of the target historical patient from the digital nutrition records of the historical patient as the historical record corresponding to the historical patient with the patient matching degree higher than the preset matching degree.
5. The method according to claim 1, wherein the verification information is a feature passing value set for each recipe feature according to the comprehensive recovery effect, and when the feature passing value is lower than the feature passing value, it indicates that the verification is not passed, otherwise, it indicates that the verification is passed.
6. The method for digital nutrition management for tumor rehabilitation according to claim 1, wherein in S3, feedback information and body response information of the patient to the target recipe are obtained, and updating the target recipe includes:
s31: periodically sending a questionnaire survey of the target recipe to the patient, acquiring feedback information of the patient, periodically performing physical examination on the patient, acquiring physical response information of the patient, and determining an evaluation index of the target recipe according to tumor information of the patient;
S32: acquiring a first keyword related to an evaluation index from feedback information, and acquiring a second keyword related to the evaluation from body reaction information;
s33: determining a first evaluation value under an evaluation index based on a first keyword, determining a second evaluation value under the evaluation index based on a second keyword, forming an evaluation feature set according to the first evaluation value and the second evaluation value, determining an update strategy of the evaluation feature set under the evaluation index, and forming an update judgment rule set according to the update strategy of all the evaluation indexes;
s34: generating an updating decision model based on the evaluation index, wherein the updating decision model is generated by the evaluation feature set and the updating decision rule set, the updating decision rule set is dynamically updated by utilizing a real-time tumor restoration guidance rule, and the evaluation feature set is dynamically updated according to a first keyword and a second keyword which are regularly acquired; acquiring an updating decision result of the updating decision model in real time by utilizing the dynamic updating result;
s35: when the updating decision result is that the target recipe needs to be updated, the target recipe is updated by utilizing the latest evaluation feature set and combining nutrition; and when the updating decision result is that the updating of the target recipe is not needed, keeping the target recipe unchanged.
7. The method of claim 6, wherein updating the target recipe with the latest set of evaluation features in combination with nutrition comprises:
when the updating strategy results are updated, selecting a first evaluation value and a second evaluation value which are lower than a preset evaluation value range from the latest evaluation feature set, and acquiring abnormal evaluation indexes corresponding to the first evaluation value and the second evaluation value which are lower than the preset evaluation value range;
acquiring a real-time abnormal state under an abnormal evaluation index;
according to the nutrition, determining the lack of elements in the real-time abnormal state, and updating the target recipe by combining the nutrition.
8. The digital nutrition management method for tumor rehabilitation of claim 6, further comprising:
recording the feeding time point and the defecation time point of a patient according to the target recipe and the feeding information of the patient on the food of the target recipe, and determining the matching degree of the target recipe and the patient according to the following formula based on the feeding time point, the defecation time point and the feeding information;
;
wherein ,indicating the matching degree of the target recipe and the patient, n indicating the eating type number of the target recipe food by the patient, m indicating the non-eating type number of the target recipe food by the patient,/o- >Nutritional value representing the patient's ith food type of target recipe food, +.>The j-th of the patient's target recipe food is the nutritional value of the eating type, +.>Time difference representing feeding time point and defecation time point, +.>Represents standard time difference, +.>Indicating the length of time the patient has consumed the target recipe food,indicating a standard food intake duration;
if the matching degree of the target recipe and the patient is larger than the preset matching degree, archiving and recording the target recipe after the target recipe is updated, otherwise, not recording the target recipe after the target recipe is updated;
acquiring the number of the target recipes recorded in the archive and the matching degree of each target recipe and a patient, and evaluating the accuracy of the target recipe acquisition method according to the following formula;
;
wherein ,representing the accuracy of the target recipe acquisition method, +.>Indicating the number of times of judgment of whether to update the target recipe,/->Representing the number of updates to the target recipe, b representing the number of target recipes recorded on file,/->Indicate->Matching degree of the target recipes of the archive records and the patient;
if the accuracy of the target recipe acquisition method is greater than the preset accuracy, continuing to use the target recipe acquisition method; otherwise, the target recipe acquisition method is adjusted.
9. The digital nutrition management method for tumor rehabilitation according to claim 8, wherein adjusting the target recipe acquisition method comprises:
taking the eating time point, the defecation time point and the eating information as personalized features, supplementing the evaluation indexes in the step S31, using the supplemented evaluation indexes, expanding the evaluation feature set correspondingly, and updating the target recipe by using the expanded evaluation feature set in the same way as the step S35.
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