CN107349489B - Neural network-based infusion dripping speed determination method and system - Google Patents
Neural network-based infusion dripping speed determination method and system Download PDFInfo
- Publication number
- CN107349489B CN107349489B CN201710658657.0A CN201710658657A CN107349489B CN 107349489 B CN107349489 B CN 107349489B CN 201710658657 A CN201710658657 A CN 201710658657A CN 107349489 B CN107349489 B CN 107349489B
- Authority
- CN
- China
- Prior art keywords
- infusion
- neural network
- speed
- network model
- patient
- 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
- 238000001802 infusion Methods 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 11
- 238000003062 neural network model Methods 0.000 claims abstract description 43
- 239000003814 drug Substances 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 12
- 230000004913 activation Effects 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 3
- 239000007788 liquid Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 17
- 238000011282 treatment Methods 0.000 abstract description 12
- 229940079593 drug Drugs 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001990 intravenous administration Methods 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 238000011369 optimal treatment Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
- A61M5/16804—Flow controllers
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
- A61M5/16877—Adjusting flow; Devices for setting a flow rate
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
- A61M5/16886—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body for measuring fluid flow rate, i.e. flowmeters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2205/00—General characteristics of the apparatus
- A61M2205/33—Controlling, regulating or measuring
- A61M2205/3331—Pressure; Flow
- A61M2205/3334—Measuring or controlling the flow rate
Abstract
The invention discloses a neural network-based infusion dripping speed determination method, which comprises the steps of reading existing case information on a case library and establishing a neural network model; detecting the physical condition of a patient needing transfusion at present; inputting the detected physical condition of the patient and the type of the required infusion medicine into a neural network model to obtain a theoretical value of the infusion speed; and controlling the actual value of the infusion speed in the infusion process according to the infusion speed output by the neural network model. The invention utilizes the information of a plurality of cases existing in a case library to establish a complete neural network model, the neural network model can calculate the optimal theoretical value of the infusion speed according to the physical condition of the patient and the type of the needed infusion medicine, and medical care personnel controls the actual value of the actual infusion speed according to the calculated theoretical value of the infusion speed, so that the infusion effect and the treatment effect of the patient are optimal.
Description
Technical Field
The invention relates to the technical field of intelligent medical treatment, in particular to a method and a system for determining the infusion dropping speed based on a neural network.
Background
At present, the fields of electronic technology, computer technology, network information technology and the like are rapidly developed, and meanwhile, the development of clinical medicine is also driven, and the development of times requires hospitals to realize informatization, automation and intelligent construction. However, in view of the current state of China, the intelligent medical equipment and the technical development of China still cannot meet the requirements of patients, families and society in various aspects.
The intravenous infusion is not only an important treatment means, but also a means for supplementing nutrition to human body, wherein the infusion speed is the most important parameter in the infusion process, and is a factor influencing the exertion of medicine, and is generally determined by medical personnel through the factors of the state of illness, age and medicine type of a patient, so that clinical experience is not very rich, and the medical personnel cannot accurately grasp the infusion speed, and easily cause discomfort of the patient due to the fact that the infusion speed is too high or influence the treatment effect due to the fact that the infusion speed is too low.
Aiming at the problems, the intelligent medical transfusion speed determination system and method are designed to improve the service level and the service quality of a hospital, and the reduction of medical accidents in the transfusion process is particularly important.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: provides a neural network-based infusion dripping speed determining method and an infusion dripping speed determining system thereof.
The solution of the invention for solving the technical problem is as follows:
a transfusion dripping speed determination method based on a neural network comprises the following steps:
establishing a neural network model: reading the existing case information on a case base, and establishing a neural network model;
a patient condition detection step: detecting the physical condition of a patient needing transfusion at present;
a step of determining the infusion speed: inputting the detected physical condition of the patient and the type of the required infusion medicine into a neural network model to obtain a theoretical value of the infusion speed;
controlling the infusion speed: and controlling the actual value of the infusion speed in the infusion process according to the theoretical value of the infusion speed output by the neural network model.
As a further improvement of the technical proposal, the data characteristics of the case information comprise the age, the height, the weight, the sex, the type of the infusion medicine and the actual value of the infusion speed corresponding to each case information.
As a further improvement of the above technical solution, the neural network model includes an input layer, a hidden layer, and an output layer, the input layer is provided with m first nodes, the hidden layer is provided with n second nodes, the output layer is provided with j third nodes, and the number of the second nodes is consistent with the number of data features of case information; and a connection weight is arranged between each first node and each second node, and the second nodes are provided with activation functions.
As a further improvement of the above technical solution, the step of establishing the neural network model includes:
step A: initializing connection weights between each first node and each second node in an input layer, setting an activation function of each second node, and setting an error threshold and training times;
and B: reading case information of a case base, and standardizing data characteristics in the case information;
and C: inputting the case information into a neural network model input layer, and obtaining the transfusion speed theoretical value of the case information by an output layer;
step D: calculating the error between the theoretical value of the infusion speed of the case information and the corresponding actual value of the infusion speed;
step E: according to the error calculated in the step D, modifying the connection weight between each first node and each second node to reduce the error between the theoretical infusion speed value of the case information and the corresponding actual infusion speed value;
step F: and C, judging whether the error meets the requirement of an error threshold value, if so, finishing the training, finishing the establishment of the neural network model, if not, judging whether the requirement of the training times is met, if so, finishing the training, finishing the establishment of the neural network model, if not, continuously reading the information of the next case in the case base, and returning to the step B.
The invention has the beneficial effects that: the invention utilizes the information of a plurality of cases existing in a case library to establish a complete neural network model, the neural network model can calculate the optimal theoretical value of the infusion speed according to the physical condition of the patient and the type of the required infusion medicine, and finally, medical care personnel control the actual value of the actual infusion speed according to the calculated theoretical value of the infusion speed, so that the infusion effect and the treatment effect of the patient are optimal. According to the invention, through the neural network model, the medical staff can accurately grasp the infusion time of various patients, so that the infusion treatment effect is optimal.
The invention also discloses a transfusion dripping speed determining system based on a neural network, which comprises a PC upper computer, a single chip microcomputer, a height detection module for detecting the liquid level height of a medicine bottle, a dripping speed detection module for detecting the transfusion speed and a stepping motor for controlling the transfusion speed, wherein the PC upper computer calculates the transfusion speed of a patient by using the transfusion dripping speed determining method according to any one of claims 1 to 4, the PC upper computer is in communication connection with the single chip microcomputer through a CAN bus, the output end of the height detection module and the output end of the dripping speed detection module are respectively connected with the input end of the single chip microcomputer, and the output end of the single chip microcomputer is connected with the input end of the stepping motor.
The invention has the beneficial effects that: the PC upper computer calculates the transfusion speed of the patient by using a transfusion dripping speed method, transmits the result to the single chip microcomputer, and controls the transfusion speed by the single chip microcomputer through the stepping motor, so that the transfusion effect and the treatment effect of the patient are optimal.
Drawings
In order to more clearly illustrate the technical solution in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is clear that the described figures are only some embodiments of the invention, not all embodiments, and that a person skilled in the art can also derive other designs and figures from them without inventive effort.
FIG. 1 is a flow chart of a drip rate determination method according to the present invention;
FIG. 2 is a flowchart illustrating the steps of establishing a neural network model in the method for determining a drop velocity according to the present invention;
fig. 3 is a block diagram of a drip rate determining system of the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the features and the effects of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and those skilled in the art can obtain other embodiments without inventive effort based on the embodiments of the present invention, and all embodiments are within the protection scope of the present invention.
Referring to fig. 1 to 3, in order to achieve the optimal treatment effect of a patient during infusion treatment, the invention discloses a neural network-based infusion dripping speed determination method, which comprises the following steps:
establishing a neural network model: reading the existing case information on a case base, and establishing a neural network model;
a patient condition detection step: detecting the physical condition of a patient needing transfusion at present;
a step of determining the infusion speed: inputting the detected physical condition of the patient and the type of the required infusion medicine into a neural network model to obtain a theoretical value of the infusion speed;
controlling the infusion speed: and controlling the actual value of the infusion speed in the infusion process according to the theoretical value of the infusion speed output by the neural network model.
Specifically, the invention utilizes a plurality of case information existing in a case library to establish a complete neural network model, the neural network model can calculate an optimal infusion speed theoretical value according to the physical condition of a patient and the type of required infusion drugs, and finally, medical care personnel controls an actual infusion speed value according to the calculated infusion speed theoretical value, so that the infusion effect and the treatment effect of the patient are optimal. According to the invention, through the neural network model, the medical staff can accurately grasp the infusion time of various patients, so that the infusion treatment effect is optimal.
In a further preferred embodiment, the medical staff all need to determine the drug infusion speed according to the actual condition of the patient to ensure the best treatment effect.
Further as a preferred embodiment, in the invention, the neural network model includes an input layer, a hidden layer, and an output layer, the input layer is provided with m first nodes, the hidden layer is provided with n second nodes, the output layer is provided with j third nodes, and the number of the second nodes is consistent with the number of data features of case information; and a connection weight is arranged between each first node and each second node, and the second nodes are provided with activation functions, wherein the activation functions are preferably S-shaped functions and are derivable everywhere.
Further, as a preferred embodiment, the invention creates the dropping speed determining method, and most importantly, establishes a perfect neural network model, the neural network model uses the existing case information in the case library, the age, height, weight, sex and infusion drug type of the patient in the case information are used as input variables, the infusion speed actual value in the case information is used as an expected value, the age, height, weight, sex and infusion drug type in the case information are input into the neural network model to obtain a corresponding infusion speed theoretical value, the infusion speed theoretical value is compared with the corresponding infusion speed actual value to obtain an error between the two data, the error is used to correct the connection weight between the first node and the second node, so that the infusion speed theoretical value is continuously close to the corresponding infusion speed actual value, to complete the neural network model. Specifically, in an embodiment of the present invention, the step of establishing the neural network model includes:
step A: initializing connection weights between each first node and each second node in an input layer, setting an activation function of each second node, and setting an error threshold and training times;
and B: reading case information of a case base, standardizing data characteristics in the case information, and converting some non-digital information into digital information at first because many data in the case information are non-digital information, such as sex and cannot be used for calculation, for example, male can be defined as 0 and female can be defined as 1 in the sex information;
and C: inputting the case information into a neural network model input layer, and obtaining the transfusion speed theoretical value of the case information by an output layer;
step D: calculating the error between the theoretical value of the infusion speed of the case information and the corresponding actual value of the infusion speed;
step E: according to the error calculated in the step D, modifying the connection weight between each first node and each second node to reduce the error between the theoretical infusion speed value of the case information and the corresponding actual infusion speed value;
step F: and C, judging whether the error meets the requirement of an error threshold value, if so, finishing the training, finishing the establishment of the neural network model, if not, judging whether the requirement of the training times is met, if so, finishing the training, finishing the establishment of the neural network model, if not, continuously reading the information of the next case in the case base, and returning to the step B.
The invention also discloses a transfusion dripping speed determining system based on a neural network, which comprises a PC upper computer, a single chip microcomputer, a height detection module for detecting the liquid level height of a medicine bottle, a dripping speed detection module for detecting the transfusion speed and a stepping motor for controlling the transfusion speed, wherein the PC upper computer calculates the transfusion speed of a patient by using the transfusion dripping speed determining method, the PC upper computer is in communication connection with the single chip microcomputer through a CAN bus, the output end of the height detection module and the output end of the dripping speed detection module are respectively connected with the input end of the single chip microcomputer, and the output end of the single chip microcomputer is connected with the input end of the stepping motor.
Specifically, the PC upper computer calculates the theoretical value of the infusion speed of the patient by using an infusion dripping speed method, and transmits the result to the single chip microcomputer, and the single chip microcomputer controls the actual value of the infusion speed through the stepping motor, so that the infusion effect and the treatment effect of the patient are optimal.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that the present invention is not limited to the details of the embodiments shown and described, but is capable of numerous equivalents and substitutions without departing from the spirit of the invention as set forth in the claims appended hereto.
Claims (2)
1. A method for determining the infusion dropping speed based on a neural network is characterized by comprising the following steps:
establishing a neural network model: reading the existing case information on a case base, and establishing a neural network model;
a patient condition detection step: detecting the physical condition of a patient needing transfusion at present;
a step of determining the infusion speed: inputting the detected physical condition of the patient and the type of the required infusion medicine into a neural network model to obtain a theoretical value of the infusion speed;
controlling the infusion speed: controlling an actual infusion speed value in the infusion process according to the theoretical infusion speed value output by the neural network model;
the data characteristics of the case information comprise the age, the height, the weight, the sex, the type of infusion medicine and the actual value of the infusion speed corresponding to each case information of the patient;
the neural network model comprises an input layer, a hidden layer and an output layer, wherein the input layer is provided with m first nodes, the hidden layer is provided with n second nodes, the output layer is provided with j third nodes, and the number of the second nodes is consistent with the number of data characteristics of case information; a connection weight is arranged between each first node and each second node, and the second nodes are provided with activation functions;
the step of establishing the neural network model comprises the following steps:
step A: initializing connection weights between each first node and each second node in an input layer, setting an activation function of each second node, and setting an error threshold and training times;
and B: reading case information of a case base, and standardizing data characteristics in the case information;
and C: inputting the case information into a neural network model input layer, and obtaining the transfusion speed theoretical value of the case information by an output layer;
step D: calculating the error between the theoretical value of the infusion speed of the case information and the corresponding actual value of the infusion speed;
step E: according to the error calculated in the step D, modifying the connection weight between each first node and each second node to reduce the error between the theoretical infusion speed value of the case information and the corresponding actual infusion speed value;
step F: and C, judging whether the error meets the requirement of an error threshold value, if so, finishing the training, finishing the establishment of the neural network model, if not, judging whether the requirement of the training times is met, if so, finishing the training, finishing the establishment of the neural network model, if not, continuously reading the information of the next case in the case base, and returning to the step B.
2. A transfusion dripping speed determining system based on a neural network is characterized in that: the automatic infusion speed determination method comprises a PC upper computer, a single chip microcomputer, a height detection module for detecting the liquid level height of a medicine bottle, a dripping speed detection module for detecting the infusion speed and a stepping motor for controlling the infusion speed, wherein the PC upper computer calculates the theoretical value of the infusion speed of a patient by using the infusion dripping speed determination method according to claim 1, the PC upper computer is in communication connection with the single chip microcomputer through a CAN bus, the output end of the height detection module and the output end of the dripping speed detection module are respectively connected with the input end of the single chip microcomputer, and the output end of the single chip microcomputer is connected with the input end of.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710658657.0A CN107349489B (en) | 2017-08-04 | 2017-08-04 | Neural network-based infusion dripping speed determination method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710658657.0A CN107349489B (en) | 2017-08-04 | 2017-08-04 | Neural network-based infusion dripping speed determination method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107349489A CN107349489A (en) | 2017-11-17 |
| CN107349489B true CN107349489B (en) | 2020-12-01 |
Family
ID=60286886
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710658657.0A Active CN107349489B (en) | 2017-08-04 | 2017-08-04 | Neural network-based infusion dripping speed determination method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107349489B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109172943A (en) * | 2018-10-31 | 2019-01-11 | 广西医科大学附属肿瘤医院 | A kind of infusion liquid drop wisdom control system |
| CN109745596B (en) * | 2019-01-11 | 2021-02-19 | 张颖颖 | Self-adaptive dripping speed adjusting platform |
| CN110021428A (en) * | 2019-04-03 | 2019-07-16 | 北京大学第三医院（北京大学第三临床医学院） | A method of laser freckle effect is improved using neural network |
| CN110639094B (en) * | 2019-09-20 | 2022-02-01 | 和宇健康科技股份有限公司 | Medical infusion control method and device, storage medium and terminal equipment |
| CN112397176A (en) * | 2020-10-16 | 2021-02-23 | 温州医科大学 | Intelligent oxytocin dose regulation and control method and system based on uterine contraction signals and LightGBM |
| CN112169070A (en) * | 2020-11-02 | 2021-01-05 | 华中科技大学同济医学院附属协和医院 | High-precision infusion device and infusion method |
| CN113663156A (en) * | 2021-09-28 | 2021-11-19 | 常德职业技术学院 | Intelligent infusion nursing system based on multi-sensor fusion technology |
| CN115212388A (en) * | 2022-07-18 | 2022-10-21 | 北京柯莱文科技咨询有限公司 | Intelligent preoperative continuous infusion device |
| CN116421824B (en) * | 2023-04-11 | 2024-01-19 | 天津医科大学总医院 | Infusion monitoring method and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103432651B (en) * | 2012-12-31 | 2016-01-20 | 南京理工大学 | A kind of intelligent anesthesia control system of closed loop |
| CN105550509B (en) * | 2015-12-10 | 2018-09-28 | 深圳先进技术研究院 | A kind of fast evaluation method of medical infusion drop and system |
| US10410113B2 (en) * | 2016-01-14 | 2019-09-10 | Preferred Networks, Inc. | Time series data adaptation and sensor fusion systems, methods, and apparatus |
-
2017
- 2017-08-04 CN CN201710658657.0A patent/CN107349489B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN107349489A (en) | 2017-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107349489B (en) | Neural network-based infusion dripping speed determination method and system | |
| CN103948986B (en) | A kind of transfusion real-time monitoring system for intelligent medical treatment | |
| Chang et al. | A context-aware, interactive M-health system for diabetics | |
| US11437139B2 (en) | Method and apparatus for biometric data collection combining visual data with historical health records metadata | |
| US11087873B2 (en) | Context-aware healthcare notification system | |
| WO2017085668A1 (en) | Wireless diagnostic system for indirect flow measurement in artificial heart pumps | |
| JP2018534701A (en) | Pulse oximeter integration for evaluating and updating drug dosing schedules using efficacy assessment | |
| AU2021368555A1 (en) | Real time detection and monitoring of fluid volume and flow rate | |
| CN109394191A (en) | A kind of Intelligent bracelet and a kind of thermal comfort judgment method and device | |
| CN202409695U (en) | Wireless transfusion monitoring system enabling users to observe progress visually and based on weighing | |
| JP6325696B2 (en) | Child health management system and child health management method | |
| CN105229648B (en) | Infusion management platform with infusion data grouping logic | |
| Keerthi et al. | Intravenous infusion monitoring system | |
| US11096580B2 (en) | Adaptive health score | |
| EP2973368B1 (en) | Context-aware healthcare notification system | |
| CN112447271A (en) | Counter device | |
| US20210178055A1 (en) | Modular power and connectivity system for infusion devices | |
| EP4094270B1 (en) | Automated conversion of drug libraries | |
| Chauhan et al. | IoT-based automatic intravenous fluid monitoring system for smart medical environment | |
| CN204815156U (en) | Wireless infusion monitoring alarm and system | |
| CN211383166U (en) | Infusion control system based on Internet of things | |
| Fernandes | Smart Bed IoT-Based Wireless Data Acquisition for Untethered Patients | |
| CN110152116A (en) | Infusion control method, device, terminal and computer readable storage medium | |
| US20200126677A1 (en) | Digital patient engagement | |
| Wijayanto et al. | Automated Infusion Monitoring Device Using Arduino-Based IoT (Internet of Things) |
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 | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20171117 Assignee: Guangzhou HENGQIANG Information Technology Co.,Ltd. Assignor: FOSHAN University Contract record no.: X2022980018387 Denomination of invention: A Method and System for Determining Infusion Dropping Velocity Based on Neural Network Granted publication date: 20201201 License type: Common License Record date: 20221014 |
|
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20171117 Assignee: GUANGZHOU JUENUO MEDICAL EQUIPMENT CO.,LTD. Assignor: FOSHAN University Contract record no.: X2022990000864 Denomination of invention: A Method and System for Determining Infusion Dropping Velocity Based on Neural Network Granted publication date: 20201201 License type: Common License Record date: 20221031 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20171117 Assignee: SHENZHEN SP MICROELECTRONICS CO.,LTD. Assignor: FOSHAN University Contract record no.: X2023980034616 Denomination of invention: A Method and System for Determining the Dropping Rate of Infusion Based on Neural Networks Granted publication date: 20201201 License type: Common License Record date: 20230411 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20171117 Assignee: GUANGZHOU XINGYIN TECHNOLOGY Co.,Ltd. Assignor: FOSHAN University Contract record no.: X2023980044827 Denomination of invention: A Method and System for Determining the Dropping Rate of Infusion Based on Neural Networks Granted publication date: 20201201 License type: Common License Record date: 20231027 Application publication date: 20171117 Assignee: GUANGZHOU HUAJUE BIOTECHNOLOGY CO.,LTD. Assignor: FOSHAN University Contract record no.: X2023980044823 Denomination of invention: A Method and System for Determining the Dropping Rate of Infusion Based on Neural Networks Granted publication date: 20201201 License type: Common License Record date: 20231027 Application publication date: 20171117 Assignee: GUANGZHOU SHANGCHENG TECHNOLOGY Co.,Ltd. Assignor: FOSHAN University Contract record no.: X2023980044821 Denomination of invention: A Method and System for Determining the Dropping Rate of Infusion Based on Neural Networks Granted publication date: 20201201 License type: Common License Record date: 20231027 |
|
| EE01 | Entry into force of recordation of patent licensing contract |