CN114121219B - Nutrition management system and management method - Google Patents
Nutrition management system and management method Download PDFInfo
- Publication number
- CN114121219B CN114121219B CN202210080531.0A CN202210080531A CN114121219B CN 114121219 B CN114121219 B CN 114121219B CN 202210080531 A CN202210080531 A CN 202210080531A CN 114121219 B CN114121219 B CN 114121219B
- Authority
- CN
- China
- Prior art keywords
- data
- nutrition
- value
- user
- detection result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—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
Landscapes
- Health & Medical Sciences (AREA)
- Nutrition Science (AREA)
- Engineering & Computer Science (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention provides a nutrition management system and a management method, which relate to the field of information processing and comprise a data acquisition unit, a data storage unit, a data calculation unit and a data judgment unit, wherein the nutrition management system comprises the following steps: acquiring initial detection data by a data acquisition unit; preprocessing initial detection data; outputting a nutrition management recommended value and a detection result predicted value; acquiring actual detection result data; calculating a first compensation value A of a user according to the detection result prediction value and actually obtained detection result data, calculating a nutrition management recommendation value, and storing the nutrition management recommendation value in a data storage unit; and judging whether the initial detection result meets the index requirement of the user or not according to the data judgment unit, and if not, performing the operation of the steps again until the index requirement of the user is met. The invention can comprehensively manage the nutrition state of the user and obtain the nutrition detection result more fitting the real demand of the user.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a nutrition management system and a management method.
Background
Tumor and treatment can generate toxic and side effects, which not only affects the life quality, but also can cause treatment interruption and cause the death rate of complication infection and the like of serious patients. In the face of the condition that the number of tumor patients is continuously increased every year, provincial and above hospitals are busy with acute-period treatment, the patients return to a residential place for rehabilitation treatment after conventional treatment, but local hospitals lack systematic rehabilitation treatment training and tools and cannot meet the requirements of widely existing symptom treatment, and the patients are in a health condition of providing a rehabilitation and symptom treatment scheme for doctors by means of an internet intelligent big data technology, providing full-course rehabilitation management service for the patients, and enabling the patients to complete screening evaluation of symptoms and online treatment of common symptoms on line. In view of the need for staged fragmentation of tumor patient symptoms, scale is required to support the operation of medical services; and scanning the code by a nutrition detector to focus on and accumulate the online patients. The traditional nutrition management aiming at the user does not carry out nutrition support according to the requirement of the user on nutrition treatment, the analysis means is not standard, and meanwhile, the problem of low efficiency exists, the evaluation on the nutrition state of the user is single, the user cannot be comprehensively evaluated, and the nutrition detection result cannot meet the real requirement of the user.
Chinese patent CN104951645A relates to a nutrition management system, providing a nutrition management system for pregnant women which can be matched with the duration of pregnancy and the nutritional status of the pregnant women. The nutrition management system includes: a user information arithmetic processing unit for calculating the nutrient requirement of the user according to user information, calculating the processing of the nutrient excess/deficiency information of the user according to the nutrient requirement and the nutrition analysis result information of the user, a cooking information search processing unit for searching the cooking information according to an instruction from the user, calculating the nutrient quantity available from the cooking of the cooking information, and a cooking information arithmetic processing unit for providing the cooking information according to the nutrient quantity and the nutrient excess/deficiency information of the user. However, the nutrition state of the user can be evaluated only by the method, and the application range is limited, so that the nutrition detection result cannot meet the real requirement of the user.
Chinese patent CN103401901A belongs to the field of health management, and in particular relates to a food nutrition management system and a food nutrition management method. The invention adopts the food nutrition management system comprising the weighing device, the mobile terminal and the remote server to be combined with the food nutrition management method, the weighing device calculates and displays the quality of each nutrient component of the food according to the food quality and a preset food nutrient component ratio table, meanwhile, the nutrition information including the quality of each nutrient component of the food is sent to the mobile terminal, the mobile terminal sets the ratio of the nutrient components of the food of the weighing device according to the instruction of the user, receives and stores the nutrition information, and simultaneously sends the nutrition information to the remote server, or receiving and displaying the medical information sent by the remote server, receiving and storing the nutrition information by the remote server, sending the medical information to the mobile terminal according to the nutrition information, the nutrition state of the user is evaluated only singly, and the user cannot be evaluated comprehensively, so that the nutrition detection result cannot meet the real requirement of the user.
Chinese patent CN113573417A discloses an intelligent nutrition management system, comprising units for: generating patient nutrition management information by a kidney disease management server and transmitting the patient nutrition management information to a base station; transmitting, by the base station, a first downlink control message to the mobile terminal on the first frequency band and the first set of symbols in response to receiving the patient nutrition management information; in response to receiving a first downlink control message transmitted by the base station, starting, by the mobile terminal, reception of downlink data transmitted by the base station on a downlink resource indicated by the first downlink control message; allocating, by the base station to the second mobile terminal, a first set of PRACH resources for transmitting the random access preamble in response to starting to transmit downlink data to the mobile terminal; transmitting, by the base station, a second downlink control message to the mobile terminal on the second frequency band and before the fourth set of time slots in response to allocating the first PRACH resource for transmitting the random access preamble to the second mobile terminal. However, the patent is single in evaluation of the nutritional state of the user and complex in operation, and cannot perform comprehensive evaluation on the user.
Chinese patent CN111653340A discloses a detection device for nutrition analysis, comprising: the data acquisition module is used for acquiring information of a user; the data evaluation module is used for evaluating the nutritional status of the current user according to the screening and evaluation of each scale, identifying the malnutrition risk of the user, judging the malnutrition degree of the user with the malnutrition risk, determining the malnutrition type and finding out the cause of the malnutrition; the data processing module is used for selecting and processing the nutrition scheme according to the content of each scale, and analyzing the nutrition support way, the nutrition support type and the nutrition support degree of the user according to the content of each scale and the information of the evaluation result; and the data nutrition management module is used for analyzing the user according to the content and the evaluation result of each scale to provide nutrition personalized guidance. By implementing and modifying the invention, the device carries out nutrition support according to the actual requirements of the user by comprehensively evaluating and analyzing the nutrition condition of the user. However, the invention does not disclose the implementation degree of the hospital and the solution that the situation changed with seasons affects the detection results of the nutrition analysis, and further affects the final detection results.
Disclosure of Invention
The invention provides an operation method of a nutrition management system, aiming at the problem of detection deviation caused by incomplete rehabilitation management in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a nutrition management system comprises a data acquisition unit, a data storage unit, a data calculation unit and a data judgment unit, wherein the data acquisition unit processes acquired data and transmits the processed data to the data storage unit, and the data storage unit processes data information after responding and inputs the processed data information to the data judgment unit for judgment;
the data acquisition unit, the data storage unit, the data calculation unit and the data judgment unit are all in communication connection;
the data storage unit is used for storing the detection result information, the nutrition recommended value information, the nutrition compensation value information and the user basic information of the user.
Further, the data acquisition unit comprises a basic information module, a nutrition risk screening module, a PG-SGA evaluation module, a body composition analysis module, a diet investigation module, a laboratory examination module and an exercise investigation module;
further, the basic information module is used for representing the basic data of the user;
further, the nutrition risk screening module rapidly identifies nutrition risk users according to the NRS-2002 scale;
furthermore, the PG-SGA evaluation module adopts a nutrition evaluation tool to judge the reason and the degree of influencing the nutrition condition;
further, the body composition analysis module adopts a body composition algorithm to dynamically detect changes of muscle mass, skeletal muscle indexes of limbs and phase angles;
further, the meal survey module is used for surveying the daily meal intake condition of the user to represent that the user has a problem of meal structure;
furthermore, the laboratory examination module directly captures nutrition-related laboratory examination results including protein, inflammation, electrolyte and liver and kidney function indexes by butting the hospital LIS system;
further, the athletic survey module is used to assess the physical activity level of the user.
A method of managing a nutrition management system, the steps comprising:
step S1: acquiring initial detection data by a data acquisition unit;
step S2: preprocessing initial detection data, calculating by a data calculation unit to obtain a nutrition management recommended value and a detection result predicted value, and storing the initial recommended value and the predicted value in a data storage unit;
step S3: outputting a nutrition management recommended value and a detection result predicted value;
step S4: acquiring actual detection result data;
step S5: calculating a first compensation value A of a user according to the detection result prediction value and actually obtained detection result data, calculating a nutrition management recommendation value, and storing the nutrition management recommendation value in a data storage unit;
step S6: and judging whether the initial detection result meets the index requirement of the user or not according to the data judgment unit, if the initial detection result does not meet the index requirement of the user, jointly calculating to obtain a new recommended value according to the obtained first compensation value A and the nutrition management recommended value, and performing the operations of the steps S3-S6 again until the index requirement of the user is met.
Furthermore, a second compensation value B is also set, the first compensation values A of a plurality of users are gathered and averaged to be used as the second compensation value B, the execution condition of the hospital is evaluated according to the second compensation value B, and the result of the second compensation value B is involved in the calculation of the step S5;
further, if the second compensation value B is greater than or equal to 3, the execution condition of the hospital is good, and if the second compensation value B <3, the execution condition of the hospital needs to be improved, and in such a case, the hospital needs to check whether the missed detection occurs or not.
Further, a third compensation value C is set, the third compensation value C of the user is calculated according to the change trend of the human body in the season changing process by adopting the correlation analysis, and the third compensation value C is involved in the calculation of the step S5.
Further, the third compensation value C =
Wherein M is an age influence factor, N is a season influence factor, P is a temperature influence factor, and alpha is an age compensation factor,
is a compensation factor for the season and gamma is a compensation factor for the temperature.
Further, the step of acquiring actual detection result data in step S4 includes:
step 1: inputting a password and logging in a nutrition management system;
step 2: starting system detection, and storing measured data into a data storage unit of the system;
and step 3: and automatically recommending the prescription by the system according to the detection data of the stored record.
Furthermore, the system recommends a prescription, establishes a multi-dimensional correlation analysis model by adopting a correlation analysis method, performs correlation analysis on detection data in a set time period to obtain correlation results of each day in an analysis time period, performs statistical analysis on the mean value and the maximum value attribution phase, and further performs visual presentation.
Further, the first compensation value a in step S5 is a difference between the predicted detection result value and the actually acquired detection result data.
Further, the nutritional management recommendation value is equal to
Wherein x is the compensation coefficient of the first compensation value A, y is the compensation coefficient of the second compensation value, and z is the second compensation valueAnd (5) compensation coefficients of three compensation values C.
Further, the new recommended value in step S6 is the sum of the initially obtained nutrition management recommended value and the first offset value a.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention provides a plurality of compensation values for compensation, comprehensively considers the execution condition of a hospital and the detection data of a user, calculates under different time periods and automatically recommends a prescription, improves the accuracy of the detection data and meets the requirement of treatment of symptoms which widely exist.
(2) The invention provides a combined structure of a data detection unit, a data judgment unit and the like to comprehensively manage the nutrition state of a user, detect a plurality of physiological parameters and the like of the user in real time, obtain a nutrition detection result more fitting the real requirements of the user and provide a prescription more conforming to the user.
Detailed Description
It should be noted that the raw materials used in the present invention are all common commercial products, and the sources thereof are not particularly limited.
The following sources of raw materials are exemplary:
example 1
A nutrition management system comprises a data acquisition unit, a data storage unit, a data calculation unit and a data judgment unit, wherein the data acquisition unit processes acquired data and transmits the processed data to the data storage unit, and the data storage unit processes data information after responding and inputs the processed data information to the data judgment unit for judgment. The data acquisition unit, the data storage unit, the data calculation unit and the data judgment unit are all in communication connection. A data storage unit for storing various detected data, such as various information data for storing user detection result information, nutrition recommendation value information, nutrition compensation value information, prediction value information, user basic information and the like; the data calculation unit is used for correspondingly calculating the received data; and the data judgment unit is used for judging whether the detection calculation result meets the index requirement of the user.
A method of managing a nutrition management system, the steps comprising: step S1: acquiring initial detection data by a data acquisition unit; step S2: preprocessing the initial detection data, calculating to obtain a nutrition management recommended value and a detection result predicted value, and storing the initial recommended value and the predicted value into a data storage unit; step S3: outputting a nutrition management recommended value and a detection result predicted value; step S4: detecting in the operation process to obtain actual detection result data; step S5: calculating a first compensation value A of a user according to the detection result prediction value and actually obtained detection result data, calculating a nutrition management recommendation value, and storing the nutrition management recommendation value in a data storage unit; step S6: and if the initial detection result does not meet the index requirement of the user, jointly calculating to obtain a new recommended value according to the obtained first compensation value A and the nutrition management recommended value, and performing the operations of the steps S3 to S6 again. Specifically, the index requirements of the user are determined according to the actual user conditions. The first compensation value a in step S5 is a difference between the predicted detection result value and the actually obtained detection result data. The nutritional management recommendation value equal to
Wherein x is the compensation coefficient of the first compensation value A, y is the compensation coefficient of the second compensation value, and z is the compensation coefficient of the third compensation value C, and the specific compensation coefficient is set according to the actual situation. The new recommended value in step S6 is the sum of the initially obtained nutrition management recommended value and the first offset value a.
A second compensation value B is also set, the first compensation values A of a plurality of users are gathered and averaged to be used as the second compensation value B, the second compensation value B is calculated to evaluate the execution condition of the hospital, the result of the second compensation value B is involved in the calculation of the step S5, the new compensation value is the value obtained by adding the first compensation value A, the initial nutrition management recommendation value and the second compensation value B, if the second compensation value B is more than or equal to 3, the execution condition of the hospital is good, and if the second compensation value B is more than or equal to 3, the second compensation value B<3, the execution condition of the hospital needs to be improved, and in this case, the hospital needs to check whether the missed detection occurs or not. And is also provided withAnd a third compensation value C, calculating the third compensation value C of the user according to the change tendency of the human body in the seasonal change process by adopting correlation analysis, and participating the third compensation value C in the calculation of the step S5, wherein the new compensation value is a value obtained by adding the first compensation value A, the initial nutrition management recommendation value, the second compensation value B and the third compensation value C. For example, when the third compensation value C is calculated, the calculation may be divided into four stages, namely, spring, summer, autumn and winter, and the average value may be obtained according to the variation tendency of each stage, and the average value may be used as the third compensation value C to participate in the step S5 for corresponding calculation. The third compensation value C =
Wherein M is an age influence factor, N is a season influence factor, wherein the seasons refer to spring, summer, autumn and winter, P is a temperature influence factor, alpha is an age compensation factor,
is a compensation factor for the season and gamma is a compensation factor for the temperature. Specifically, the data acquisition unit in step S1 includes a basic information module, a nutritional risk screening module, a PG-SGA evaluation module, a body composition analysis module, a meal survey module, a laboratory examination module, and a sport survey module; the basic information module is used for representing user basic data; the nutrition risk screening module rapidly identifies nutrition risk users according to the NRS-2002 scale; the PG-SGA evaluation module adopts a nutrition evaluation tool to judge the reason and the degree of influencing the nutrition condition; the body composition analysis module dynamically detects muscle mass, the indexes of skeletal muscles of limbs and phase angle changes by adopting a body composition algorithm, calculates body indexes such as fat, lean body mass, skeletal muscles and the like by utilizing a bioelectrical impedance principle according to electrical resistivity measured by different components of a human body, and measures the content of intracellular and extracellular fluids by utilizing low-frequency and high-frequency currents, so that the body composition analysis module is a key means for evaluating and detecting the nutrition level; the meal investigation module is used for investigating the daily meal intake condition of the user to represent the problem of the meal structure of the user; the laboratory examination module directly captures nutrition-related laboratory examination results including eggs by docking the hospital LIS systemWhite, inflammatory, electrolyte and liver and kidney function indexes; the athletic survey module is used to assess a physical activity level of the user. And the initial detection data is preprocessed by predicting and recommending the basic information of the single patient, nutrition risk screening data, PG-SGA evaluation data, body composition analysis data, diet survey data, laboratory examination data and exercise survey data.
If the detected human body composition measurement value is abnormally high or low, the skin condition of the user can be considered, for example, when the skin of the user is dry or oily, the skin can be wiped clean by using skin moistening oil or an alcohol cotton sheet and then tested, so as to avoid influencing the detection result. The nutrition management system has the functions of stepped nutrition support, diet selection guidance, nursing suggestion giving and the like, and the specific stepped nutrition support means that personalized nutrition treatment prescriptions are recommended according to stepped nutrition treatment principles and specific nutrition support ways, preparations, dosages and use time; the diet selection guidance refers to the recommended nutritional diet selection according to the diet guidance of malignant tumor patients aiming at different cancers, complications and digestive tract functions; care recommendations are generally based on treatment regimens and complications recommendations. The preferable environmental temperature range is-20-50 degrees, the relative humidity range can be 20-80 percent, and the atmospheric pressure range can be 700-1000 hpa.
The step of acquiring actual detection result data in step S4 includes: step 1: inputting a password and logging in a nutrition management system; step 2: starting system detection, and storing measured data into a data storage unit of the system, wherein the specific data storage unit can select an adaptive storage; and step 3: and automatically recommending the prescription by the system according to the detection data of the stored record. And the system recommends the prescription to adopt the correlation analysis method, set up the correlation analysis model of the multidimension degree, carry on the correlation analysis to the measured data in the settlement time quantum, obtain the correlation result of every day in the analysis period, and carry on the average value and maximum value and belong to the phase place statistical analysis, carry on the predictive analysis of the stated phase place of every user according to the statistical result, and use the maximum and minimum normalization method to carry on the visualization presentation.
Finally, it should be noted that the above-mentioned contents are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, and that the simple modifications or equivalent substitutions of the technical solutions of the present invention by those of ordinary skill in the art can be made without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. A management method of a nutrition management system is characterized by comprising the nutrition management system, wherein the nutrition management system comprises a data acquisition unit, a data storage unit, a data calculation unit and a data judgment unit, the data acquisition unit processes acquired data and transmits the processed data to the data storage unit, and the data storage unit processes data information after responding and then inputs the processed data information to the data judgment unit for judgment;
the data acquisition unit, the data storage unit, the data calculation unit and the data judgment unit are all in communication connection;
the data storage unit is used for storing the detection result information, the nutrition recommendation value information, the nutrition compensation value information and the user basic information of the user;
the data acquisition unit comprises a basic information module, a nutrition risk screening module, a PG-SGA evaluation module, a body composition analysis module, a diet investigation module, a laboratory examination module and a sport investigation module;
the basic information module is used for representing the basic data of the user;
the nutrition risk screening module rapidly identifies nutrition risk users according to the NRS-2002 scale;
the PG-SGA evaluation module adopts a nutrition evaluation tool to judge the reason and the degree of influencing the nutrition condition;
the body composition analysis module adopts a body composition algorithm to dynamically detect changes of muscle mass, skeletal muscle indexes of limbs and phase angles;
the meal investigation module is used for investigating the daily meal intake condition of the user to represent the problem of the meal structure of the user;
the laboratory examination module is used for directly grabbing nutrition-related laboratory examination results comprising protein, inflammation, electrolyte and liver and kidney function indexes by butting the hospital LIS system;
the motion investigation module is used for evaluating the physical activity level of the user;
the management method comprises the following steps:
step S1: acquiring initial detection data by a data acquisition unit;
step S2: preprocessing initial detection data, calculating by a data calculation unit to obtain a nutrition management recommended value and a detection result predicted value, and storing the initial recommended value and the predicted value in a data storage unit;
the initial detection data is preprocessed through prediction and recommendation of single patient basic information, nutrition risk screening data, PG-SGA evaluation data, body composition analysis data, diet survey data, laboratory examination data and exercise survey data;
step S3: outputting a nutrition management recommended value and a detection result predicted value;
step S4: acquiring actual detection result data;
step S5: calculating a first compensation value A of the user according to the detection result predicted value and actually obtained detection result data, wherein the first compensation value A is a difference value between the detection result predicted value and the actually obtained detection result data; the hospital management system is also provided with a second compensation value B, the first compensation values A of a plurality of users are gathered and averaged to be used as the second compensation value B, and the execution condition of the hospital is evaluated according to the second compensation value B; a third compensation value C is also set, and the third compensation value C of the user is calculated for the change trend of the human body in the seasonal change process by adopting the correlation analysis; calculating a nutritional management recommendation value equal to
Wherein x is the compensation coefficient of the first compensation value A, y is the compensation coefficient of the second compensation value, and z is the compensation coefficient of the third compensation value C; storing the data into a data storage unit;
step S6: and judging whether the initial detection result meets the index requirement of the user or not according to the data judgment unit, if the initial detection result does not meet the index requirement of the user, jointly calculating to obtain a new recommended value according to the obtained first compensation value A and the nutrition management recommended value, wherein the new recommended value is the sum of the nutrition management recommended value obtained in the step S5 and the first compensation value A, and performing the operation of the step S3-the step S6 again until the index requirement of the user is met.
2. A method of managing a nutrition management system according to claim 1, wherein the third compensation value C =
Wherein M is an age influence factor, N is a season influence factor, P is a temperature influence factor, and alpha is an age compensation factor,
is a compensation factor for the season and gamma is a compensation factor for the temperature.
3. The method for managing a nutrition management system according to claim 1, wherein the step of acquiring actual test result data in step S4 includes:
step 1: inputting a password and logging in a nutrition management system;
step 2: starting system detection, and storing measured data into a data storage unit of the system;
and step 3: and automatically recommending the prescription by the system according to the detection data of the stored record.
4. The management method of the nutrition management system according to claim 3, wherein the system recommends that the prescription adopts a correlation analysis method, establishes a multidimensional correlation analysis model, performs correlation analysis on the detection data in a set time period to obtain correlation results of each day in an analysis time period, performs statistical analysis on the mean value and the maximum value attribution phase, and further performs visual presentation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210080531.0A CN114121219B (en) | 2022-01-24 | 2022-01-24 | Nutrition management system and management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210080531.0A CN114121219B (en) | 2022-01-24 | 2022-01-24 | Nutrition management system and management method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114121219A CN114121219A (en) | 2022-03-01 |
| CN114121219B true CN114121219B (en) | 2022-05-17 |
Family
ID=80361216
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210080531.0A Active CN114121219B (en) | 2022-01-24 | 2022-01-24 | Nutrition management system and management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114121219B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5692501A (en) * | 1993-09-20 | 1997-12-02 | Minturn; Paul | Scientific wellness personal/clinical/laboratory assessments, profile and health risk managment system with insurability rankings on cross-correlated 10-point optical health/fitness/wellness scales |
| CN105631211A (en) * | 2015-12-28 | 2016-06-01 | 王萍 | Multi-objective nutrition optimization and decision support system |
| CN111445981A (en) * | 2020-03-28 | 2020-07-24 | 华中科技大学同济医学院附属协和医院 | Cancer patient nutrition management system |
| CN111653340A (en) * | 2020-04-13 | 2020-09-11 | 江苏康爱营养科技有限责任公司 | Detection apparatus for nutritional analysis |
-
2022
- 2022-01-24 CN CN202210080531.0A patent/CN114121219B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5692501A (en) * | 1993-09-20 | 1997-12-02 | Minturn; Paul | Scientific wellness personal/clinical/laboratory assessments, profile and health risk managment system with insurability rankings on cross-correlated 10-point optical health/fitness/wellness scales |
| CN105631211A (en) * | 2015-12-28 | 2016-06-01 | 王萍 | Multi-objective nutrition optimization and decision support system |
| CN111445981A (en) * | 2020-03-28 | 2020-07-24 | 华中科技大学同济医学院附属协和医院 | Cancer patient nutrition management system |
| CN111653340A (en) * | 2020-04-13 | 2020-09-11 | 江苏康爱营养科技有限责任公司 | Detection apparatus for nutritional analysis |
Non-Patent Citations (2)
| Title |
|---|
| Nutritional assessment and risk factors associated to malnutrition in patients with esophageal cancer;Jingjing Cao 等;《Current Problems in Cancer》;20210208;第45卷(第1期);全文 * |
| 肿瘤营养诊疗系统的实施与应用;于恺英 等;《肿瘤代谢与营养电子杂志》;20200909;第7卷(第3期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114121219A (en) | 2022-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20150279235A1 (en) | Nutrition management system and nutrition management program | |
| JP7319930B2 (en) | Systems and methods for calculating, displaying, modifying, and using a single food intake score that reflects optimal quantity and quality of ingestibles | |
| Wirt et al. | Diet quality–what is it and does it matter? | |
| CN107658001B (en) | Household oil health management method and system | |
| US20140052722A1 (en) | Optimization-based regimen method and system for personalized diabetes and diet management | |
| US20140149143A1 (en) | Method for providing personalized dietary compliance rate based on health information | |
| KR20120109264A (en) | System for providing personalized recommended food based on mobile and providing method using the same | |
| CN113488168A (en) | Personalized physical examination item recommendation method based on user health information characteristic set | |
| CN111627523A (en) | Clinical nutrition diagnosis and treatment simulation system and simulation method | |
| CN110993043A (en) | Medical health management system | |
| CN114121219B (en) | Nutrition management system and management method | |
| CN111048180B (en) | Dietary intake investigation analysis system, method and terminal | |
| CN111653340A (en) | Detection apparatus for nutritional analysis | |
| CN105433901B (en) | A kind of method and its application measuring human body body fat | |
| JP4989240B2 (en) | Meal management support system | |
| CN115985470A (en) | Intelligent nutrition management method and intelligent management system | |
| WO2020202738A1 (en) | Intestinal flora analysis system, information processor, and information processing method and program | |
| CN114023419A (en) | Recipe recommendation method and device and nonvolatile storage medium | |
| CN113569140A (en) | Information recommendation method and device, electronic equipment and computer-readable storage medium | |
| CN111354456A (en) | Traditional Chinese medicine constitution type detection method and device | |
| JP2020166323A (en) | Information provision system, information provision program | |
| KR20200110112A (en) | Health management system and Health management apparatus using bio-impedance measuring apparatus | |
| WO2010064323A1 (en) | Biological information judgment device and biological information judgment method | |
| CN112349388B (en) | Data processing method and device for blood sugar, storage medium and electronic equipment | |
| Edefonti et al. | Dietary patterns and cancer risk: an overview with focus on methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |