CN108172290A - A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology - Google Patents
A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology Download PDFInfo
- Publication number
- CN108172290A CN108172290A CN201711321749.6A CN201711321749A CN108172290A CN 108172290 A CN108172290 A CN 108172290A CN 201711321749 A CN201711321749 A CN 201711321749A CN 108172290 A CN108172290 A CN 108172290A
- Authority
- CN
- China
- Prior art keywords
- nutrition
- postoperative
- patient user
- patient
- model
- 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.)
- Pending
Links
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses a kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology, step 1, the postoperative every body index for obtaining patient user；Step 2, the eating habit for obtaining patient user and current nutrition condition；Step 3 handles postoperative every body index of patient user, eating habit, current nutrition condition input nutrition in postoperative suggestion mode；The result of model treatment is shown by step 4 to patient user；Step 5, the nutrition intake situation for recording patient user's reality；Step 6 is analyzed and combines the nutrition intake situation of patient user's reality and every body index of patient user, and lasting dynamic adjusts and carries out subsequent nutrition in postoperative suggestion activity.The present invention combines the postoperative physical condition and nourishment of patient user, based on data monitoring and artificial intelligence analysis, rational nutritional support is provided for postoperative hepatopath, to maintain patient user in stable condition and liver function recovery being promoted to provide science reference.
Description
Technical field
It is particularly a kind of based on artificial intelligence technology the present invention relates to multiple fields such as medicine, machine learning, big datas
Hepatopath's nutrition in postoperative suggestion mode.
Background technology
Machine learning (Machine Learning, ML) is a multi-field cross discipline, be related to probability theory, statistics,
The multi-door subjects such as Approximation Theory, convextiry analysis, algorithm complexity theory.It is dedicated to studying the mankind's simulated or realized to computer how
Learning behavior to obtain new knowledge or skills, reorganizes the existing structure of knowledge and is allowed to constantly improve the performance of itself, leads to
It crosses and the combination of mass data, can show the real meaning of data behind.Artificial neural network is one kind in machine learning
Algorithm based on the basic principle of neural network in biology, is abstracted human brain neuroid from information processing angle, with
Network topology knowledge is theoretical foundation, and simulation human brain establishes the mathematical model of certain processing complex information.One neural network quilt
It is considered as the mathematical model for containing many parameters, is mutually substituted into and obtained by several functions.To input and desired output simultaneously
After being supplied to artificial intelligence technology, artificial neural network obtains all weights in model, repeatedly adjusts by learning and training
Weight cause the output of model be equal to or be infinitely close to it is desired as a result, namely having searched out and having output and input between
Relationship, so as to complete the foundation of model.
Liver is an organ based on metabolic function in body, and hepatopath is because obstacle occurs in liver function, then
It can lead to the metabolic disorder of body internal protein, fat etc., so as to cause a degree of liver source property malnutritive.Nutritional mode
Selection can be divided into parenteral nutrition and enteral nutrition, it is generally the case that for hepatopath, parenteral nutrition is mainly used in
It during early postoperation, cooperates with enteral nutrition, the patient can not normally to feed provides nutritional support, action time
It is shorter；It is gradually transitions complete enteral nutrition later.Such nutritional support mode can maintain the integrality of function of intestinal canal, subtract
The generation infected less promotes the recovery of patient's liver function.But all each not phase of the constitution situation and postoperative status of every patient
Together, and how according to the comprehensive condition of each patient, the suggestion of nutrition intake is targetedly made for it, be the present invention urgently
The science and technology with positive effect solved.
Invention content
More than present situation is based on, the present invention proposes a kind of hepatopath's nutrition in postoperative suggestion side based on artificial intelligence technology
With reference to machine learning techniques, using computer information processing, rational nutrition intake suggestion is provided for postoperative hepatopath for method
Feasible program is provided, ensures the supply of patient's energy, promotes the recovery of patient body function.
A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology of the present invention, this method include following
Step：
Step 1, the postoperative every body index for obtaining patient user, exist including at least body temperature, blood pressure, blood-sugar content
Interior body measurement value and patient perceivable's symptom；
Step 2, the eating habit for obtaining patient user and current nutrition condition；
Step 3, the nutrition in postoperative suggestion mode for building patient；These data that step 1- steps 3 are collected are divided into two
Point, a part, will be by postoperative every body index, the drink of a large amount of hepatopaths being collected by relevant channels as training set
The input of dietary habits, current nutrition condition as model using the nutritional contents that should be taken in breath as output, defines model
Activation primitive and the number of plies by constantly learning and training, obtain the weight of nutrition Suggestion model；Another part is as test
Collection, for verifying the accuracy of nutrition Suggestion model；Later, it by details adjusts and optimizes, so as to establish nutrition in postoperative branch
Hold model；Hereafter, postoperative every body index of patient user, eating habit, current nutrition condition input nutrition in postoperative are built
View model is handled；；
The result of model treatment is shown by step 4 to patient user；
Step 5, the nutrition intake situation for recording patient's reality；
Step 6 is analyzed and combines the nutrition intake situation of patient user's reality and every body index of patient user, holds
Continuous dynamic adjusts and carries out subsequent nutrition in postoperative suggestion activity.
Compared with prior art, the present invention can combine the postoperative physical condition and nourishment of patient user, based on number
According to monitoring and artificial intelligence analysis, rational nutritional support is provided for postoperative hepatopath, to maintain patient user's shape
State is stable and the recovery of liver function is promoted to provide science reference.
Description of the drawings
Fig. 1 is a kind of hepatopath's nutrition in postoperative suggesting method overall flow based on artificial intelligence technology of the present invention
Figure.
Specific embodiment
In the enteral nutrition mode in later stage, need to record the dietary data of user and convert it into specific nutrient
Careful analysis is carried out, in this respect, this method establishes the dietary nutrient database of oneself, includes 100 various nutrients；
In terms of the data of patient user, we establish cooperation with hospital and obtain data, it is ensured that data source it is true
Property and reliability；Artificial intelligence technology has very strong nonlinear fitting ability, can map arbitrarily complicated non-linear relation, and
Learning rules are simple, realize that this provides suitable realization method for this research convenient for computer；In addition, this research also with it is more
The medical practitioner of hospital of family establishes contact, and the hints and tips of profession can be provided for the research of the present invention.It is above some, all
Make it possible a kind of realization hepatopath's nutrition in postoperative proposed projects based on artificial intelligence technology proposed by the present invention.
Embodiments of the present invention are described in further detail below in conjunction with attached drawing.
Hepatopath's nutrition in postoperative proposed projects based on artificial intelligence technology of the present invention, this method specifically includes following
Step：
Step 1, the postoperative every body index for obtaining patient user specifically include the measured value of some body datas, example
Such as body temperature, blood pressure, blood-sugar content and some physical conditions, than such as whether there are the symptoms such as abdominal pain, diarrhea, nausea；
Step 2, the eating habit for obtaining patient user and current nutrition condition, so as to targetedly provide to the user
Nutrition Suggestion；
Step 3, the nutrition in postoperative suggestion mode for building patient；These data that step (1)-step (3) is collected are divided into
Two parts, a part will refer to the postoperative every body for a large amount of hepatopaths being collected by relevant channels as training set
The input of mark, eating habit, current nutrition condition as model using the nutritional contents that should be taken in breath as output, defines
The activation primitive and the number of plies of model by constantly learning and training, obtain the weight of nutrition Suggestion model；Another part is made
For test set, for verifying the accuracy of nutrition Suggestion model；Later, it by details adjusts and optimizes, it is postoperative so as to establish
Nutritional support model；；Hereafter, it is postoperative every body index of patient user, eating habit, the input of current nutrition condition is postoperative
Nutrition Suggestion model is handled；
The handling result of nutrition in postoperative suggestion mode is shown by step 4 to patient user, specific to show that content is 1,
Current physical condition is shown to user in the form of radar map and word；2, nutrition intake is provided a user in the form of word
Suggestion；
(Postoperative records it and is converted to specific nutrient progress for step 5, the nutrition intake situation of record patient's reality
Careful analysis), the content including enteral nutrition and parenteral nutrition；
Step 6 is analyzed and combines the nutrition intake situation of patient user's reality and every body index of patient user, holds
Continuous dynamic adjusts and carries out subsequent nutrition in postoperative suggestion activity.
The invention is not limited in aforementioned flow, any combination by presently disclosed feature or new step into
Row extension, fall within protection scope of the present invention.
Claims (1)
1. a kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology, which is characterized in that this method include with
Lower step：
Step (1), the postoperative every body index for obtaining patient user, including body temperature, blood pressure, blood-sugar content
Body measurement value and patient perceivable's symptom；
Step (2), the eating habit for obtaining patient user and current nutrition condition；
Step (3), the nutrition in postoperative suggestion mode for building patient；These data that step (1)-step (3) is collected are divided into two
Part, a part as training set, by by the postoperative every body index for a large amount of hepatopaths being collected by relevant channels,
The input of eating habit, current nutrition condition as model using the nutritional contents that should be taken in as output, defines model
Activation primitive and the number of plies by constantly learning and training, obtain the weight of nutrition Suggestion model；Another part is as test
Collection, for verifying the accuracy of nutrition Suggestion model；Later, it by details adjusts and optimizes, so as to establish nutrition in postoperative branch
Hold model；；Hereafter, postoperative every body index of patient user, eating habit, current nutrition condition input nutrition in postoperative are built
View model is handled；
The result of model treatment is shown by step (4) to patient user；
Step (5), the nutrition intake situation for recording patient user's reality；
Step (6) is analyzed and combines the nutrition intake situation of patient's reality and every body index of patient user, and lasting is dynamic
State adjusts and carries out subsequent nutrition in postoperative suggestion activity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711321749.6A CN108172290A (en) | 2017-12-12 | 2017-12-12 | A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711321749.6A CN108172290A (en) | 2017-12-12 | 2017-12-12 | A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108172290A true CN108172290A (en) | 2018-06-15 |
Family
ID=62525729
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711321749.6A Pending CN108172290A (en) | 2017-12-12 | 2017-12-12 | A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108172290A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112151182A (en) * | 2020-08-14 | 2020-12-29 | 北京大学 | Intelligent medical advice generation method and system based on greedy search |
| CN112842269A (en) * | 2021-01-06 | 2021-05-28 | 南通市第一人民医院 | Postoperative activity system and method for negative pressure ball wearer |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809164A (en) * | 2015-04-01 | 2015-07-29 | 惠州Tcl移动通信有限公司 | Healthy diet recommendation method based on mobile terminal and mobile terminal |
| CN105677852A (en) * | 2016-01-07 | 2016-06-15 | 陕西师范大学 | Personalized healthy diet recommendation service method |
| CN106250673A (en) * | 2016-07-20 | 2016-12-21 | 美的集团股份有限公司 | A kind of dietary recommendations continued and evaluation methodology, intelligent terminal, Cloud Server and system |
-
2017
- 2017-12-12 CN CN201711321749.6A patent/CN108172290A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809164A (en) * | 2015-04-01 | 2015-07-29 | 惠州Tcl移动通信有限公司 | Healthy diet recommendation method based on mobile terminal and mobile terminal |
| CN105677852A (en) * | 2016-01-07 | 2016-06-15 | 陕西师范大学 | Personalized healthy diet recommendation service method |
| CN106250673A (en) * | 2016-07-20 | 2016-12-21 | 美的集团股份有限公司 | A kind of dietary recommendations continued and evaluation methodology, intelligent terminal, Cloud Server and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112151182A (en) * | 2020-08-14 | 2020-12-29 | 北京大学 | Intelligent medical advice generation method and system based on greedy search |
| CN112842269A (en) * | 2021-01-06 | 2021-05-28 | 南通市第一人民医院 | Postoperative activity system and method for negative pressure ball wearer |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108836285A (en) | The autonomous diagnostic model of human body diseases and artificial intelligence examination method and system and health evaluating visually demonstrate method | |
| WO2016067093A1 (en) | System and method for providing biometric and context based messaging | |
| CN110584601B (en) | Old man cognitive function monitoring and evaluation system | |
| US20210162261A1 (en) | Methods and systems for generating physical activity sets for a human subject | |
| CN109480868B (en) | Intelligent infant monitoring system | |
| CN102110192A (en) | Auxiliary disease judgment method based on diagnostic element data association | |
| CN108198603A (en) | A kind of nutrition dietary based on personal physical examination information recommends method | |
| US10089391B2 (en) | Ontological information retrieval system | |
| CN107742533A (en) | A kind of method based on dietary nutrient prediction gout | |
| CN105260594A (en) | Individual nutrition evaluation system | |
| CN113168919A (en) | System for performing health analysis by using personalized indexes and using method thereof | |
| CN104462765A (en) | Three-dimensional visualization doctor-patient communication platform construction method and system | |
| Kristoffersson et al. | Wearable sensors for monitoring and preventing noncommunicable diseases: A systematic review | |
| CN108172290A (en) | A kind of hepatopath's nutrition in postoperative suggesting method based on artificial intelligence technology | |
| Zhang et al. | Auto-annotating sleep stages based on polysomnographic data | |
| CN111161842A (en) | Postoperative rehabilitation nutrition suggestion method based on artificial intelligence technology | |
| Palacios et al. | Machine learning-based meal detection using continuous glucose monitoring on healthy participants: an objective measure of participant compliance to protocol | |
| Cabarkapa et al. | Physical and performance characteristics of 3× 3 professional male basketball players | |
| US20230400971A1 (en) | Method for providing service that inputs and shares entity observation information, and computer-readable storage medium | |
| Fortier et al. | Development of a mobile cardiac wellness application and integrated wearable sensor suite | |
| Tams et al. | Creating clinical decision support systems for respiratory medicine | |
| Gordon | Research traditions available to family medicine | |
| CN109994169A (en) | Interactive intelligent is health management system arranged | |
| Henderson et al. | Sensor-based Data Fusion for Multimodal Affect Detection in Game-based Learning Environments. | |
| Shi et al. | The study of pulse condition of traditional Chinese medicine in information age |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180615 |