CN112489806A - Intelligent management method and system for disease state information of diabetic foot patient - Google Patents
Intelligent management method and system for disease state information of diabetic foot patient Download PDFInfo
- Publication number
- CN112489806A CN112489806A CN202011358240.0A CN202011358240A CN112489806A CN 112489806 A CN112489806 A CN 112489806A CN 202011358240 A CN202011358240 A CN 202011358240A CN 112489806 A CN112489806 A CN 112489806A
- Authority
- CN
- China
- Prior art keywords
- information
- foot
- patient
- obtaining
- disease
- 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.)
- Granted
Links
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 96
- 201000010099 disease Diseases 0.000 title claims abstract description 95
- 208000008960 Diabetic foot Diseases 0.000 title claims abstract description 52
- 238000007726 management method Methods 0.000 title claims abstract description 26
- 208000014770 Foot disease Diseases 0.000 claims abstract description 95
- 239000008280 blood Substances 0.000 claims abstract description 62
- 210000004369 blood Anatomy 0.000 claims abstract description 62
- 238000011161 development Methods 0.000 claims abstract description 26
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims abstract description 20
- 239000008103 glucose Substances 0.000 claims abstract description 20
- 230000007170 pathology Effects 0.000 claims abstract description 20
- 238000000034 method Methods 0.000 claims description 40
- 238000003062 neural network model Methods 0.000 claims description 29
- 238000012549 training Methods 0.000 claims description 26
- 230000008859 change Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 11
- 238000012544 monitoring process Methods 0.000 abstract description 23
- 206010012601 diabetes mellitus Diseases 0.000 abstract description 17
- 230000000694 effects Effects 0.000 abstract description 12
- 208000027418 Wounds and injury Diseases 0.000 description 42
- 238000004458 analytical method Methods 0.000 description 15
- 208000024891 symptom Diseases 0.000 description 14
- 238000010586 diagram Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 10
- 230000006866 deterioration Effects 0.000 description 9
- 238000012545 processing Methods 0.000 description 9
- 238000013528 artificial neural network Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 7
- 238000002636 symptomatic treatment Methods 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000001575 pathological effect Effects 0.000 description 4
- 208000002193 Pain Diseases 0.000 description 3
- 208000025865 Ulcer Diseases 0.000 description 3
- 238000002266 amputation Methods 0.000 description 3
- 231100000397 ulcer Toxicity 0.000 description 3
- 206010028851 Necrosis Diseases 0.000 description 2
- 208000003251 Pruritus Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 230000003111 delayed effect Effects 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 210000003205 muscle Anatomy 0.000 description 2
- 230000017074 necrotic cell death Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 201000001119 neuropathy Diseases 0.000 description 2
- 230000007823 neuropathy Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 208000033808 peripheral neuropathy Diseases 0.000 description 2
- 230000000291 postprandial effect Effects 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 206010014080 Ecchymosis Diseases 0.000 description 1
- 206010028289 Muscle atrophy Diseases 0.000 description 1
- 206010030113 Oedema Diseases 0.000 description 1
- 206010033799 Paralysis Diseases 0.000 description 1
- 206010034568 Peripheral coldness Diseases 0.000 description 1
- 206010037549 Purpura Diseases 0.000 description 1
- 230000003187 abdominal effect Effects 0.000 description 1
- 238000001467 acupuncture Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000003925 brain function Effects 0.000 description 1
- 230000004087 circulation Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000035876 healing Effects 0.000 description 1
- 201000001421 hyperglycemia Diseases 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 210000003141 lower extremity Anatomy 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000020763 muscle atrophy Effects 0.000 description 1
- 201000000585 muscular atrophy Diseases 0.000 description 1
- 210000000653 nervous system Anatomy 0.000 description 1
- 231100000862 numbness Toxicity 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001717 pathogenic effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 206010034754 petechiae Diseases 0.000 description 1
- 230000019612 pigmentation Effects 0.000 description 1
- 239000002243 precursor Substances 0.000 description 1
Images
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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- 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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- 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
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- 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
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Physics & Mathematics (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Biomedical Technology (AREA)
- General Physics & Mathematics (AREA)
- Pathology (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Databases & Information Systems (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention discloses an intelligent management method and system for pathology information of a diabetic foot patient, which comprises the steps of collecting first image information of the foot of the patient, and obtaining first foot disease grade information according to the first image information; obtaining first blood glucose information of a first patient; judging whether the first blood sugar information meets a first preset threshold value; when the first blood sugar information does not meet the first preset threshold value, the first foot disease grade information is adjusted to obtain second foot disease grade information, a first instruction is obtained according to the second foot disease grade information, and the first instruction is used for sending disease state information to a patient and storing the information in the intelligent terminal. The problem of lack in the prior art to the pathology monitoring mode of diabetes foot patient, have the serious consequence that the condition of an illness worsens that causes in time not discover often is solved. The technical effects of monitoring and analyzing the disease development of the diabetic foot patient in time, knowing the disease state of the foot disease in time and avoiding delaying the disease state to influence the treatment or cause serious consequences are achieved.
Description
Technical Field
The invention relates to the technical field of patient care, in particular to a method and a system for intelligently managing symptom information of a diabetic foot patient.
Background
Diabetic foot is a common complication caused by diabetes, and ulcers and necrosis caused by foot microvascular and neuropathy have great harm to health, and prevention and treatment measures need to be taken in time. There are generally several manifestations: due to poor blood flow and vasculopathy in the foot, the skin of the foot and legs develops sporadic ecchymoses, petechiae, blisters, and pigmentation, usually purplish red or black, which are all precursors to diabetic feet. The skin has symptoms of pruritus, cold extremities, continuous numbness and edema, and the affected part has symptoms of acupuncture, burning pain, etc. Because of poor circulation of the lower limbs, the wound is difficult to heal once a small wound appears, pathogenic infection is easy to cause, and the ulcer is aggravated and is not healed for a long time. The foot nervous system lesion causes changes in both muscles and bones, and finally joint deformation occurs. Because the blood sugar of the diabetic is high, the wound is not easy to heal, if the treatment is not found in time, amputation can be caused when the wound is seriously deteriorated, the perception capability of the diabetic is weakened, and the condition that the wound or foot abnormity cannot be sensed exists, so that the treatment is delayed, and the timely foot disease monitoring of the diabetic is very necessary.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the prior art is lack of a disease condition monitoring mode for diabetic foot patients, and has the technical problem of discovering serious consequences which cause disease deterioration in time.
Disclosure of Invention
The embodiment of the application provides the intelligent management method and system for the disease state information of the diabetic foot patient, and solves the technical problems that a disease state monitoring mode aiming at the diabetic foot patient is lacked, and serious consequences of disease state deterioration are caused in time. The technical effects of timely monitoring and analyzing the disease condition development of the diabetic foot patient, analyzing the disease condition, giving corresponding reminding guidance according to the grade of the foot disease, timely enabling the patient to know the disease condition of the foot disease, avoiding delaying the disease condition to influence treatment or causing serious consequences, and simultaneously utilizing the stored disease condition development database to facilitate the analysis of the disease condition of the patient, help symptomatic treatment and clinical research.
In view of the above problems, the embodiments of the present application provide a method and a system for intelligently managing pathology information of a diabetic foot patient.
In a first aspect, an embodiment of the present application provides an intelligent management method for pathology information of a diabetic foot patient, which is applied to an intelligent terminal, where the intelligent terminal has an image acquisition device, and the method includes: acquiring first image information, wherein the first image information is a first patient foot image acquired by the image acquisition equipment; obtaining first foot disease grade information according to the first image information; obtaining first blood glucose information of the first patient; judging whether the first blood sugar information meets a first preset threshold value; when the first blood sugar information does not meet the first preset threshold value, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first foot disease grade information according to the first blood sugar information; acquiring second foot disease grade information, wherein the second foot disease grade information is the foot disease grade adjusted by the first foot disease grade information; and obtaining a first instruction according to the second podopathy grade information, wherein the first instruction is used for sending the disease state information to the patient and storing the information in the intelligent terminal.
In another aspect, the present application further provides an intelligent management system for pathology information of a diabetic foot patient, the system comprising:
a first obtaining unit, configured to obtain first image information, where the first image information is a first patient foot image acquired by the image acquisition device;
a second obtaining unit, configured to obtain first podiatric grade information according to the first image information;
a third obtaining unit for obtaining first blood glucose information of the first patient;
a first judgment unit configured to judge whether the first blood glucose information satisfies a first predetermined threshold;
a fourth obtaining unit, configured to obtain a first adjustment instruction when the first blood glucose information does not satisfy the first predetermined threshold, where the first adjustment instruction is used to adjust the first foot disease level information according to the first blood glucose information;
a fifth obtaining unit, configured to obtain second podopathy grade information, where the second podopathy grade information is a podopathy grade adjusted by the first podopathy grade information;
a sixth obtaining unit, configured to obtain a first instruction according to the second podopathy grade information, where the first instruction is to send pathology information to a patient, and store the information in the intelligent terminal.
In a third aspect, the present invention provides a method and a system for intelligently managing pathology information of a diabetic foot patient, including a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the embodiment of the application provides an intelligent management method and system for disease state information of a diabetic foot patient, which are applied to an intelligent terminal, wherein the intelligent terminal is provided with an image acquisition device, and the method comprises the following steps: acquiring first image information, wherein the first image information is a first patient foot image acquired by the image acquisition equipment; obtaining first foot disease grade information according to the first image information; obtaining first blood glucose information of the first patient; judging whether the first blood sugar information meets a first preset threshold value; when the first blood sugar information does not meet the first preset threshold value, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first foot disease grade information according to the first blood sugar information; acquiring second foot disease grade information, wherein the second foot disease grade information is the foot disease grade adjusted by the first foot disease grade information; and obtaining a first instruction according to the second podopathy grade information, wherein the first instruction is used for sending the disease state information to the patient and storing the information in the intelligent terminal. The technical effects of timely monitoring and analyzing the disease condition development of the diabetic foot patient, analyzing the disease condition, giving corresponding reminding guidance according to the grade of the foot disease, timely enabling the patient to know the disease condition of the foot disease, avoiding delaying the disease condition to influence treatment or causing serious consequences, and simultaneously utilizing the stored disease condition development database to facilitate the analysis of the disease condition of the patient, help symptomatic treatment and clinical research. Therefore, the technical problem that serious consequences of illness deterioration are caused due to the fact that a symptom monitoring mode aiming at a diabetic foot patient is lacked in the prior art is solved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a method for intelligently managing pathology information of a diabetic foot patient according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of an intelligent management system for condition information of a diabetic foot patient according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first judging unit 14, a fourth obtaining unit 15, a fifth obtaining unit 16, a sixth obtaining unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application provides the intelligent management method and system for the disease state information of the diabetic foot patient, and solves the technical problems that a disease state monitoring mode aiming at the diabetic foot patient is lacked, and serious consequences of disease state deterioration are caused in time. The technical effects of timely monitoring and analyzing the disease condition development of the diabetic foot patient, analyzing the disease condition, giving corresponding reminding guidance according to the grade of the foot disease, timely enabling the patient to know the disease condition of the foot disease, avoiding delaying the disease condition to influence treatment or causing serious consequences, and simultaneously utilizing the stored disease condition development database to facilitate the analysis of the disease condition of the patient, help symptomatic treatment and clinical research.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Diabetic foot is a common complication caused by diabetes, and ulcers and necrosis caused by foot microvascular and neuropathy have great harm to health, and prevention and treatment measures need to be taken in time. Because the blood sugar of the diabetic is high, the wound is not easy to heal, if the treatment is not found in time, amputation can be caused when the wound is seriously deteriorated, the perception capability of the diabetic is weakened, and the condition that the wound or foot abnormity cannot be sensed exists, so that the treatment is delayed, and the timely foot disease monitoring of the diabetic is very necessary. However, the prior art lacks a disease state monitoring mode for diabetic foot patients, and has the technical problem of discovering serious consequences which cause disease deterioration in time.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
acquiring first image information, wherein the first image information is a first patient foot image acquired by the image acquisition equipment; obtaining first foot disease grade information according to the first image information; obtaining first blood glucose information of the first patient; judging whether the first blood sugar information meets a first preset threshold value; when the first blood sugar information does not meet the first preset threshold value, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first foot disease grade information according to the first blood sugar information; acquiring second foot disease grade information, wherein the second foot disease grade information is the foot disease grade adjusted by the first foot disease grade information; and obtaining a first instruction according to the second podopathy grade information, wherein the first instruction is used for sending the disease state information to the patient and storing the information in the intelligent terminal. The technical effects of timely monitoring and analyzing the disease condition development of the diabetic foot patient, analyzing the disease condition, giving corresponding reminding guidance according to the grade of the foot disease, timely enabling the patient to know the disease condition of the foot disease, avoiding delaying the disease condition to influence treatment or causing serious consequences, and simultaneously utilizing the stored disease condition development database to facilitate the analysis of the disease condition of the patient, help symptomatic treatment and clinical research.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
Fig. 1 is a schematic flowchart of a method for intelligently managing pathology information of a diabetic foot patient according to an embodiment of the present application, and as shown in fig. 1, an embodiment of the present application provides a method for intelligently managing pathology information of a diabetic foot patient, which is applied to an intelligent terminal, where the intelligent terminal has an image acquisition device, and the method includes:
step 100: acquiring first image information, wherein the first image information is a first patient foot image acquired by the image acquisition equipment;
specifically, the image information of the foot of the patient is acquired by using an intelligent terminal worn by the patient or an intelligent terminal installed at a specific position of the living place of the patient, and the first image information, which is the foot image information of the patient, is obtained.
Step 200: obtaining first foot disease grade information according to the first image information;
further, in order to improve the accuracy of data analysis, step 200 in the embodiment of the present application includes:
step 210: taking the first image information as first input information;
step 220: inputting the first input information into a first neural network model, the model being trained using a plurality of sets of training data, each of the sets of training data comprising: the first input information and identification information identifying the degree information of the foot disease;
step 230: obtaining first output information of the first neural network model, wherein the first output information includes a first result representing foot condition rating information for the first patient.
Specifically, the current pathological condition of the foot of the patient is determined by analyzing first image information acquired from the foot of the patient, the disease grade is evaluated according to the pathological condition, the pathological condition of the diabetic foot patient is classified into multiple grades according to the degree of severity so as to be convenient for symptom management, the observation frequency of mild symptoms is different from the observation frequency of severe symptoms, the management center of gravity of mild symptoms is different from the management center of gravity of severe symptoms, when the disease grade of the patient is analyzed according to the image information, in order to improve the accuracy of an analysis result, a neural network model is used for processing, the first image information is used as first input information, statistical calculation is carried out by using the data, the foot disease grade information of the first patient is evaluated, and the evaluation accuracy is improved. The first neural network model is a training model which is constructed by performing model training by using a plurality of groups of first image information and identification information for identifying the foot disease grade information as training data, and the accuracy of the neural network model is ensured by training and correcting a large amount of data. Thus, the input information is input into the training model, and the output result of the podopathy grade information matched with the identification can be obtained.
Further, the Neural network model, i.e., Neural network model in machine learning, Neural Network (NN), is a complex Neural network system formed by a large number of simple processing units (called neurons) widely connected to each other, reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. In the embodiment of the present application, the neural network model is trained by a large amount of the first image information and identification information identifying the podopathy grade information.
Still further, the training process is essentially a supervised learning process. Each set of training data of the plurality of sets of training data comprises: first image information and identification information identifying the footherapy grade information. Comparing the output foot disease grade information with the identification information for identifying the foot disease grade information by inputting the first image information and the identification information for identifying the foot disease grade information, and finishing the group of data supervised learning and performing the supervised learning of the next group of data if the output foot disease grade information is consistent with the identification information for identifying the foot disease grade information; if the foot disease grade information is not consistent with the identification information for identifying the foot disease grade information, the neural network learning model adjusts and corrects the neural network learning model, when the foot disease grade information output by the neural network learning model is consistent with the identification information for identifying the foot disease grade information, the supervised learning of the next group of data is carried out, and the supervised learning process is finished until the neural network model reaches the expected accuracy rate. The neural network model is continuously corrected and optimized through training data, the accuracy of the neural network model for processing the information is improved through the process of supervised learning, so that accurate output foot disease grade information is obtained, the accuracy of foot disease grade information evaluation is improved, and the technical effects of timely knowing and managing the foot disease patient condition are achieved through timely confirmation and accurate evaluation of the foot condition of the patient.
Step 300: obtaining first blood glucose information of the first patient;
step 400: judging whether the first blood sugar information meets a first preset threshold value;
step 500: when the first blood sugar information does not meet the first preset threshold value, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first foot disease grade information according to the first blood sugar information;
step 600: acquiring second foot disease grade information, wherein the second foot disease grade information is the foot disease grade adjusted by the first foot disease grade information;
step 700: and obtaining a first instruction according to the second podopathy grade information, wherein the first instruction is used for sending the disease state information to the patient and storing the information in the intelligent terminal.
Further, in step 700 of the embodiment of the present application, obtaining a first instruction according to the second podopathy grade information, where the first instruction is to send disease condition information to a patient and store the information in the intelligent terminal, and the obtaining the first instruction includes:
step 710: judging whether the second podopathy grade information meets a third preset threshold value;
step 720: when the first early warning information is met, sending second early warning information, wherein the second early warning information is used for sending warning information to a patient;
step 730: and when the current foot disease level does not meet the requirement, obtaining first prompt information, wherein the first prompt information comprises current foot disease level information and disease treatment opinions for prompting the user.
Specifically, since hyperglycemia of a diabetic patient is a main cause affecting foot diseases, and the cure speed of the foot diseases is also affected by the level of blood sugar, in order to accurately manage and control the course of a diabetic foot patient, treatment should be performed in combination with blood sugar concentration, when the blood sugar concentration of the patient is higher than a set threshold, it indicates that the current blood sugar control condition of the patient is not good, and the treatment affecting foot diseases is accelerated to aggravate the disease conditions, for example, the set blood sugar value may be classified as a fasting blood sugar value or a postprandial blood sugar value, according to different setting contents, the patient is prompted to collect corresponding blood sugar values, the two types may also be switched, for example, when the space-time abdominal blood sugar value collected by the patient, an option of fasting blood sugar is selected, and vice versa, different predetermined thresholds are set according to different blood sugar types, and when the threshold is a fasting blood sugar threshold is set to be 9, the threshold value is set to 13 when the blood glucose is postprandial blood glucose, and the corresponding setting adjustment is not limited to this, as a matter of course. When the blood sugar value of a patient exceeds a set threshold, the blood sugar value cannot be processed only according to the foot image, and is comprehensively judged by combining the blood sugar, when the blood sugar concentration is high, the treatment of the foot disease is seriously influenced, and the development of the disease condition is accelerated, so that the evaluation is carried out by referring to the blood sugar data during grade confirmation, the method is more accurate and reliable, the guidance is provided for the patient according to the determined disease condition grade, if the blood sugar level is too high and exceeds the threshold, reminding information is sent to the patient or the grade is not high, the reminding information is sent to the patient, the current foot disease condition is correspondingly processed, the patient can know the disease condition of the foot disease in time to avoid delaying the disease condition to influence the treatment or cause serious consequences, meanwhile, the data storage is carried out by utilizing the determined foot disease grade information of the patient, the disease condition of the patient can be conveniently mastered, the disease course management of the patient is carried, the stored data can be used for analyzing, researching and grasping the treatment process of the diabetic foot disease patient, and the method has important significance for medical clinical guidance. The problem of lack in the prior art to the pathology monitoring mode of diabetes foot patient, have the serious consequence that the condition of an illness worsens that causes in time not discover often is solved. The technical effects of timely monitoring and analyzing the disease development of the diabetic foot patient, analyzing the symptoms and giving corresponding guidance according to the grade of the foot disease, and timely knowing the symptoms of the foot disease by the patient to avoid delaying the disease condition to influence treatment or cause serious consequences are achieved.
Further, in the step 200 of the embodiment of the present application, obtaining first podopathy grade information according to the first image information further includes: step 240: judging whether a wound exists according to the first image information; step 250: obtaining the wound size when a wound is present; step 260: obtaining the first podopathy grade information according to the wound size.
Further, after determining whether a wound exists according to the first image information in step 240 of the embodiment of the present application, the method includes: step 270: obtaining first medical record information when no wound exists; step 280: obtaining first foot information according to the first image information; step 290: and determining the first podiatric grade information according to the first medical record information and the first foot information.
Further, the determining whether a wound exists according to the first image information in step 240 of the embodiment of the present application includes: step 241: inputting the first image information into a second neural network model, the second neural network model being trained using a plurality of sets of training data, each set of training data in the plurality of sets comprising: the first image information and identification information identifying wound information; step 242: obtaining second output information of the second neural network model, wherein the second output information includes a second result representing information of whether a wound is present in the first image information.
Specifically, when the foot disease grade evaluation is performed, the embodiment of the present application performs targeted analysis on a wound of a foot, and analyzes and processes the acquired image information to determine whether the wound exists, when the wound exists, the corresponding foot disease grade is relatively high, once the wound of a diabetic patient occurs, the wound is difficult to heal, the blood sugar concentration directly affects the healing of the wound, even aggravates the wound, and if the wound is serious, the diabetic patient may die to cause amputation, so that attention must be paid to the diabetic patient, but because the pain of the diabetic patient is weakened and the diabetic patient sometimes hardly finds his own disease, monitoring and timely feedback need to be given, when the wound of the diabetic patient is analyzed, in order to improve the accuracy of the analysis result, the neural network model that is also adopted is to perform statistical calculation by using first image information as input information to determine whether the wound exists on the foot, the accuracy of analysis processing is improved. The second neural network model is a training model which is formed by training and establishing a plurality of groups of first image information and identification information for identifying whether wound information exists or not as training data, and the accuracy of the neural network model is ensured through training and correcting a large amount of data. Therefore, whether a wound information output result matched with the identification exists or not can be obtained by inputting the input information into the training model, when the identification information exists in the image, the output result is that the wound exists, and when the identification information does not exist in the image information, the output result is that the wound does not exist. The second neural network model is trained by a large amount of data and is created by using a large amount of supervised learning, so that the output result is more accurate. When the wound of the patient is determined to exist, the wound is further analyzed to obtain the size and depth of the wound, the size is correspondingly judged, if the size exceeds a set range, namely, the size is too large, alarm information needs to be sent to the patient, and the alarm information is processed as soon as possible so as to avoid delaying and causing serious consequences. And when the condition is not met, the current evaluation and diagnosis result and the corresponding treatment guidance are sent to the patient, so that the patient can know the own symptoms and make correct treatment and maintenance in time, the foot disease is effectively controlled, and the development and deterioration are avoided. When no wound appears, the specific situation analysis is carried out by combining the picture and the medical record of the patient, for example, the picture comparison in a patient database in multiple periods is carried out, whether the muscle and the skin color of the foot part are changed, whether the conditions of muscle atrophy, skin redness or purple blackening exist, whether the records of pruritus, pain, paralysis and the like exist in the medical record are judged, the grade of the foot disease is analyzed according to the medical record, the analysis result and the corresponding treatment suggestion are sent to the patient, and all information is synchronously stored so as to facilitate the later analysis. The method achieves the technical effects of monitoring and analyzing the disease condition development of a diabetic foot patient in time, giving corresponding reminding guidance according to the grade of the foot disease by analyzing the disease condition, enabling the patient to know the disease condition of the foot disease in time, avoiding delaying the disease condition to influence treatment or cause serious consequences, and facilitating the analysis of the disease condition of the patient, the symptomatic treatment and the clinical research by utilizing the stored disease condition development database. Further solves the technical problems that the prior art lacks a disease state monitoring mode aiming at a diabetic foot patient and has serious consequences of disease deterioration caused by untimely discovery.
Further, the embodiment of the present application includes: step 810: obtaining a first time of the first image information, wherein the first time is the acquisition time of the first image information; step 820: acquiring second image information through the image acquisition equipment, wherein the acquisition time of the second image information is a second time, and the second time is earlier than the first time; step 830: obtaining first change information according to the first image information and the second image information; step 840: obtaining second medical record information according to the first time; step 850: obtaining third medical record information according to the second time; step 860: obtaining second change information according to the second medical record information and the third medical record information; step 870: and obtaining first disease development data according to the first change information and the second change information.
Further, the embodiment of the present application further includes: step 910: determining whether the first disease development data meets a second predetermined threshold; step 920: and when the first warning information is satisfied, first warning information is obtained, and the first warning information is used for sending warning information to the patient.
Specifically, the embodiment of the application also combines the previous data and the next data for comparison in the information monitoring and management of the diabetic foot patient so as to ensure the comprehensiveness and the accuracy in the process of managing the pathological condition information. The image information collected at present and the image information collected at the last time are compared, the difference between the two image information is found, whether the condition is improved or deteriorated is judged, the medical record information of two time points is also required to be combined for comprehensive analysis in the judging process in order to improve the accuracy of the condition, the development condition and the development speed are obtained, when the condition is developed too fast to exceed the threshold value set by the system, the feedback is required to be sent back to a patient in time, measures are correspondingly taken, and delay is avoided. The technical effects of timely monitoring and analyzing the disease condition development of the diabetic foot patient, analyzing the disease condition, giving corresponding reminding guidance according to the grade of the foot disease, timely enabling the patient to know the disease condition of the foot disease, avoiding delaying the disease condition to influence treatment or causing serious consequences, and simultaneously utilizing the stored disease condition development database to facilitate the analysis of the disease condition of the patient, help symptomatic treatment and clinical research. Therefore, the technical problem that serious consequences of illness deterioration are caused due to the fact that a symptom monitoring mode aiming at a diabetic foot patient is lacked in the prior art is solved.
Example two
Based on the same inventive concept as the method for intelligently managing the disease condition information of the diabetic foot patient in the previous embodiment, the invention also provides an intelligent management system for the disease condition information of the diabetic foot patient, as shown in fig. 2, the system comprises:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first image information, where the first image information is a first patient foot image acquired by the image acquisition device;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first podiatric grade information according to the first image information;
a third obtaining unit 13, the third obtaining unit 13 being configured to obtain first blood glucose information of the first patient;
a first judging unit 14, wherein the first judging unit 14 is used for judging whether the first blood sugar information meets a first preset threshold value;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain a first adjustment instruction when the first blood glucose information does not satisfy the first predetermined threshold, where the first adjustment instruction is used to adjust the first foot disease level information according to the first blood glucose information;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to obtain second podopathy grade information, where the second podopathy grade information is a podopathy grade adjusted by the first podopathy grade information;
a sixth obtaining unit 17, where the sixth obtaining unit 17 is configured to obtain a first instruction according to the second podopathy grade information, where the first instruction is to send pathology information to a patient, and store the information in the intelligent terminal.
Further, the system further comprises:
a first execution unit configured to take the first image information as first input information;
a first input unit, configured to input the first input information into a first neural network model, where the model is trained using multiple sets of training data, and each set of training data in the multiple sets includes: the first input information and identification information identifying the degree information of the foot disease;
a seventh obtaining unit for obtaining first output information of the first neural network model, wherein the first output information comprises a first result, and the first result represents foot condition grade information of the first patient.
Further, the system further comprises:
a second judging unit configured to judge whether a wound exists according to the first image information;
an eighth obtaining unit for obtaining the wound size when a wound is present;
a ninth obtaining unit for obtaining the first podopathy grade information according to the wound size.
Further, the system further comprises:
a tenth obtaining unit for obtaining first medical record information when no wound is present;
an eleventh obtaining unit configured to obtain first foot information from the first image information;
a first determining unit, configured to determine the first podiatric information according to the first medical record information and the first foot information.
Further, the system further comprises:
a second input unit, configured to input the first image information into a second neural network model, where the second neural network model is trained using multiple sets of training data, and each set of training data in the multiple sets includes: the first image information and identification information identifying wound information;
a twelfth obtaining unit for obtaining second output information of the second neural network model, wherein the second output information includes a second result representing information whether a wound is present in the first image information.
Further, the system further comprises:
a thirteenth obtaining unit, configured to obtain a first time of the first image information, where the first time is an acquisition time of the first image information;
a fourteenth obtaining unit, configured to obtain, by the image acquisition device, second image information, where an acquisition time of the second image information is a second time, where the second time is earlier than the first time;
a fifteenth obtaining unit, configured to obtain first variation information according to the first image information and the second image information;
a sixteenth obtaining unit, configured to obtain second medical record information according to the first time;
a seventeenth obtaining unit, configured to obtain third medical record information according to the second time;
an eighteenth obtaining unit, configured to obtain second change information according to the second medical record information and the third medical record information;
a nineteenth obtaining unit, configured to obtain first disease development data according to the first change information and the second change information.
Further, the system further comprises:
a third determination unit configured to determine whether the first disease development data satisfies a second predetermined threshold;
a twentieth obtaining unit configured to obtain first warning information when the first warning information is satisfied, the first warning information being used to send warning information to a patient.
Further, the system further comprises:
a fourth judging unit configured to judge whether the second podiatric grade information satisfies a third predetermined threshold;
the first sending unit is used for sending second early warning information when the first early warning information meets the requirement, and the second early warning information is used for sending warning information to a patient;
and the twenty-first obtaining unit is used for obtaining first prompt information when the first prompt information does not meet the requirement, and the first prompt information comprises current foot disease grade information and an illness state treatment suggestion for prompting the user.
Various changes and specific examples of the intelligent management method for pathology information of a diabetic foot patient in the first embodiment of fig. 1 are also applicable to the intelligent management system for pathology information of a diabetic foot patient in the present embodiment, and through the foregoing detailed description of the intelligent management method for pathology information of a diabetic foot patient, a person skilled in the art can clearly know the implementation method of the intelligent management system for pathology information of a diabetic foot patient in the present embodiment, so for the brevity of the description, detailed description is not repeated here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the intelligent management method for the disease condition information of the diabetic foot patient in the foregoing embodiment, the invention further provides an intelligent management system for the disease condition information of the diabetic foot patient, wherein a computer program is stored on the intelligent management system, and when the computer program is executed by a processor, the steps of any one of the above intelligent management methods for the disease condition information of the diabetic foot patient are realized.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the embodiment of the application provides an intelligent management method and system for disease state information of a diabetic foot patient, which are applied to an intelligent terminal, wherein the intelligent terminal is provided with an image acquisition device, and the method comprises the following steps: acquiring first image information, wherein the first image information is a first patient foot image acquired by the image acquisition equipment; obtaining first foot disease grade information according to the first image information; obtaining first blood glucose information of the first patient; judging whether the first blood sugar information meets a first preset threshold value; when the first blood sugar information does not meet the first preset threshold value, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first foot disease grade information according to the first blood sugar information; acquiring second foot disease grade information, wherein the second foot disease grade information is the foot disease grade adjusted by the first foot disease grade information; and obtaining a first instruction according to the second podopathy grade information, wherein the first instruction is used for sending the disease state information to the patient and storing the information in the intelligent terminal. The technical effects of timely monitoring and analyzing the disease condition development of the diabetic foot patient, analyzing the disease condition, giving corresponding reminding guidance according to the grade of the foot disease, timely enabling the patient to know the disease condition of the foot disease, avoiding delaying the disease condition to influence treatment or causing serious consequences, and simultaneously utilizing the stored disease condition development database to facilitate the analysis of the disease condition of the patient, help symptomatic treatment and clinical research. Therefore, the technical problem that serious consequences of illness deterioration are caused due to the fact that a symptom monitoring mode aiming at a diabetic foot patient is lacked in the prior art is solved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An intelligent management method for pathology information of a diabetic foot patient is applied to an intelligent terminal, wherein the intelligent terminal is provided with an image acquisition device, and the method comprises the following steps:
acquiring first image information, wherein the first image information is a first patient foot image acquired by the image acquisition equipment;
obtaining first foot disease grade information according to the first image information;
obtaining first blood glucose information of the first patient;
judging whether the first blood sugar information meets a first preset threshold value;
when the first blood sugar information does not meet the first preset threshold value, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first foot disease grade information according to the first blood sugar information;
acquiring second foot disease grade information, wherein the second foot disease grade information is the foot disease grade adjusted by the first foot disease grade information;
and obtaining a first instruction according to the second podopathy grade information, wherein the first instruction is used for sending the disease state information to the patient and storing the information in the intelligent terminal.
2. The method of claim 1, wherein said obtaining first podopathy grade information from the first image information comprises:
taking the first image information as first input information;
inputting the first input information into a first neural network model, the model being trained using a plurality of sets of training data, each of the sets of training data comprising: the first input information and identification information identifying the degree information of the foot disease;
obtaining first output information of the first neural network model, wherein the first output information includes a first result representing foot condition rating information for the first patient.
3. The method of claim 1, wherein said obtaining first podopathy grade information from the first image information comprises:
judging whether a wound exists according to the first image information;
obtaining the wound size when a wound is present;
obtaining the first podopathy grade information according to the wound size.
4. The method of claim 3, wherein said determining whether a wound is present from said first image information comprises:
obtaining first medical record information when no wound exists;
obtaining first foot information according to the first image information;
and determining the first podiatric grade information according to the first medical record information and the first foot information.
5. The method of claim 3, wherein said determining whether a wound is present from said first image information comprises:
inputting the first image information into a second neural network model, the second neural network model being trained using a plurality of sets of training data, each set of training data in the plurality of sets comprising: the first image information and identification information identifying wound information;
obtaining second output information of the second neural network model, wherein the second output information includes a second result representing information of whether a wound is present in the first image information.
6. The method of claim 1, wherein the method comprises:
obtaining a first time of the first image information, wherein the first time is the acquisition time of the first image information;
acquiring second image information through the image acquisition equipment, wherein the acquisition time of the second image information is a second time, and the second time is earlier than the first time;
obtaining first change information according to the first image information and the second image information;
obtaining second medical record information according to the first time;
obtaining third medical record information according to the second time;
obtaining second change information according to the second medical record information and the third medical record information;
and obtaining first disease development data according to the first change information and the second change information.
7. The method of claim 6, wherein the method comprises:
determining whether the first disease development data meets a second predetermined threshold;
and when the first warning information is satisfied, first warning information is obtained, and the first warning information is used for sending warning information to the patient.
8. The method of claim 1, wherein the obtaining of the first instruction according to the second podopathy grade information, the first instruction being to send disease state information to a patient and storing the information in the intelligent terminal, comprises:
judging whether the second podopathy grade information meets a third preset threshold value;
when the first early warning information is met, sending second early warning information, wherein the second early warning information is used for sending warning information to a patient;
and when the current foot disease level does not meet the requirement, obtaining first prompt information, wherein the first prompt information comprises current foot disease level information and disease treatment opinions for prompting the user.
9. An intelligent management system for pathology information of a diabetic foot patient, wherein the system comprises:
a first obtaining unit, configured to obtain first image information, where the first image information is a first patient foot image acquired by the image acquisition device;
a second obtaining unit, configured to obtain first podiatric grade information according to the first image information;
a third obtaining unit for obtaining first blood glucose information of the first patient;
a first judgment unit configured to judge whether the first blood glucose information satisfies a first predetermined threshold;
a fourth obtaining unit, configured to obtain a first adjustment instruction when the first blood glucose information does not satisfy the first predetermined threshold, where the first adjustment instruction is used to adjust the first foot disease level information according to the first blood glucose information;
a fifth obtaining unit, configured to obtain second podopathy grade information, where the second podopathy grade information is a podopathy grade adjusted by the first podopathy grade information;
a sixth obtaining unit, configured to obtain a first instruction according to the second podopathy grade information, where the first instruction is to send pathology information to a patient, and store the information in the intelligent terminal.
10. An intelligent management system for pathology information of a diabetic foot patient, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-8 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011358240.0A CN112489806B (en) | 2020-11-27 | 2020-11-27 | Intelligent management method and system for disease state information of diabetic foot patient |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011358240.0A CN112489806B (en) | 2020-11-27 | 2020-11-27 | Intelligent management method and system for disease state information of diabetic foot patient |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112489806A true CN112489806A (en) | 2021-03-12 |
| CN112489806B CN112489806B (en) | 2022-02-15 |
Family
ID=74936227
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011358240.0A Active CN112489806B (en) | 2020-11-27 | 2020-11-27 | Intelligent management method and system for disease state information of diabetic foot patient |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112489806B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096811A (en) * | 2021-04-29 | 2021-07-09 | 中国科学院合肥物质科学研究院 | Diabetic foot image processing and risk early warning equipment based on infrared thermal imaging |
| CN117174274A (en) * | 2023-11-03 | 2023-12-05 | 四川省医学科学院·四川省人民医院 | Intelligent access system for patient after diabetic foot interventional operation |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2626704A1 (en) * | 2012-02-08 | 2013-08-14 | Mehmet Ali Soylemez | Diagnosis and complication risk assessment of pancreatic diabetes within normal value ranges of leucocytes using procalcitonin |
| CN104899816A (en) * | 2015-05-23 | 2015-09-09 | 深圳市前海安测信息技术有限公司 | Primary medical platform and diabetes patient monitoring method based on same |
| CN106108857A (en) * | 2016-08-23 | 2016-11-16 | 王伏声 | Diabetic foot classification of severity evaluates system |
| CN108108757A (en) * | 2017-12-18 | 2018-06-01 | 深圳市唯特视科技有限公司 | A kind of diabetic foot ulcers sorting technique based on convolutional neural networks |
| CN110600126A (en) * | 2019-09-19 | 2019-12-20 | 江苏大学附属医院 | Auxiliary evaluation method for arterial calcified plaque image of lower limb of diabetic foot |
| CN210203559U (en) * | 2019-05-09 | 2020-03-31 | 中国科学院福建物质结构研究所 | Image acquisition assembly for diabetic foot and diabetic foot measuring device |
| CN111028232A (en) * | 2019-12-31 | 2020-04-17 | 上海鹰瞳医疗科技有限公司 | Diabetes classification method and equipment based on fundus images |
| CN111265188A (en) * | 2020-01-21 | 2020-06-12 | 温州医科大学 | Diabetic foot risk assessment device and method based on local microcirculation |
| CN111667467A (en) * | 2020-05-28 | 2020-09-15 | 江苏大学附属医院 | Clustering algorithm-based lower limb vascular calcification index multi-parameter accumulation calculation method |
| WO2020190839A1 (en) * | 2019-03-21 | 2020-09-24 | Drexel University | Methods, computer-readable media, and systems for assessing wounds and candidate treatments |
-
2020
- 2020-11-27 CN CN202011358240.0A patent/CN112489806B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2626704A1 (en) * | 2012-02-08 | 2013-08-14 | Mehmet Ali Soylemez | Diagnosis and complication risk assessment of pancreatic diabetes within normal value ranges of leucocytes using procalcitonin |
| CN104899816A (en) * | 2015-05-23 | 2015-09-09 | 深圳市前海安测信息技术有限公司 | Primary medical platform and diabetes patient monitoring method based on same |
| CN106108857A (en) * | 2016-08-23 | 2016-11-16 | 王伏声 | Diabetic foot classification of severity evaluates system |
| CN108108757A (en) * | 2017-12-18 | 2018-06-01 | 深圳市唯特视科技有限公司 | A kind of diabetic foot ulcers sorting technique based on convolutional neural networks |
| WO2020190839A1 (en) * | 2019-03-21 | 2020-09-24 | Drexel University | Methods, computer-readable media, and systems for assessing wounds and candidate treatments |
| CN210203559U (en) * | 2019-05-09 | 2020-03-31 | 中国科学院福建物质结构研究所 | Image acquisition assembly for diabetic foot and diabetic foot measuring device |
| CN110600126A (en) * | 2019-09-19 | 2019-12-20 | 江苏大学附属医院 | Auxiliary evaluation method for arterial calcified plaque image of lower limb of diabetic foot |
| CN111028232A (en) * | 2019-12-31 | 2020-04-17 | 上海鹰瞳医疗科技有限公司 | Diabetes classification method and equipment based on fundus images |
| CN111265188A (en) * | 2020-01-21 | 2020-06-12 | 温州医科大学 | Diabetic foot risk assessment device and method based on local microcirculation |
| CN111667467A (en) * | 2020-05-28 | 2020-09-15 | 江苏大学附属医院 | Clustering algorithm-based lower limb vascular calcification index multi-parameter accumulation calculation method |
Non-Patent Citations (2)
| Title |
|---|
| SHARMA, A 等: "Impaired Insulin Action Is Associated With Increased Glucagon Concentrations in Nondiabetic Humans", 《JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM》 * |
| 王占国: "社区管理糖尿病患者糖尿病足病知信行状况及其影响因素研究", 《中国优秀博硕士学位论文全文数据库(硕士) 医药卫生科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096811A (en) * | 2021-04-29 | 2021-07-09 | 中国科学院合肥物质科学研究院 | Diabetic foot image processing and risk early warning equipment based on infrared thermal imaging |
| CN113096811B (en) * | 2021-04-29 | 2024-06-07 | 中国科学院合肥物质科学研究院 | Diabetes foot image processing and risk early warning equipment based on infrared thermal imaging |
| CN117174274A (en) * | 2023-11-03 | 2023-12-05 | 四川省医学科学院·四川省人民医院 | Intelligent access system for patient after diabetic foot interventional operation |
| CN117174274B (en) * | 2023-11-03 | 2024-02-02 | 四川省医学科学院·四川省人民医院 | Intelligent access system for patient after diabetic foot interventional operation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112489806B (en) | 2022-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dunton et al. | Nurse staffing and patient falls on acute care hospital units | |
| CN112489806B (en) | Intelligent management method and system for disease state information of diabetic foot patient | |
| CN112117008A (en) | Health monitoring method and system based on big data | |
| CN112712882A (en) | Method and system for improving intensive care management level | |
| CN112734746A (en) | Orthopedic injury spectrum matching system and method based on image recognition | |
| CN112102940A (en) | Refractive detection method, device, computer equipment and storage medium | |
| CN118016270A (en) | Internet-based cervical cancer perioperative traditional Chinese medicine nursing monitoring method and system | |
| CN111916213A (en) | Medical service method and device based on cloud computing | |
| CN117352175A (en) | Fracture intelligent auxiliary monitoring system for orthopedic nursing | |
| CN113384241B (en) | Wearable device assisted chronic patient clinical monitoring platform and method | |
| CN112509685B (en) | Standardized system for screening operation modes | |
| CN112992355A (en) | Preoperative assessment method and system for esophageal cancer patient | |
| CN108122232B (en) | Intelligent identification method for different actions of affected part of medical splint | |
| CN110570949A (en) | Health monitoring method and equipment for old people in smart community and readable storage medium | |
| CN113053482B (en) | Physical sign monitoring and analyzing system and method for nephrology department patient | |
| CN112992312B (en) | Qualified monitoring method and system for spinal cord injury rehabilitation training | |
| CN112908472B (en) | Chronic ulcer infection risk assessment method and system | |
| JP2018519983A (en) | A system to detect and predict early onset of symptom deterioration in patients with chronic obstructive bronchopneumonia | |
| CN112951422A (en) | Rehabilitation self-checking method and system for orthopedic patient | |
| CN112420208B (en) | Medical staff epidemic situation protection monitoring method and system | |
| CN113192603A (en) | Mental state assessment method and system based on big data | |
| CN113130070B (en) | Comprehensive diagnosis method and system for craniocerebral diseases | |
| CN112951408B (en) | System and method for preventing respiratory tract infection | |
| CN112562830B (en) | Diabetic foot prevention system and method | |
| CN113035307B (en) | Nursing system and method for preventing potential complications of cardiology |
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 | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240301 Address after: 222000 No. 38, Huanghe Road, Lianyungang Economic and Technological Development Zone, Lianyungang City, Jiangsu Province Patentee after: JIANGSU HENGRUI MEDICINE Co.,Ltd. Country or region after: China Address before: 226001 Jiangsu city of Nantong province Haier Lane Road, No. 6 Patentee before: Nantong First People's Hospital Country or region before: China |