CN115762789A - Hypertensive nephropathy prediction method and care device - Google Patents
Hypertensive nephropathy prediction method and care device Download PDFInfo
- Publication number
- CN115762789A CN115762789A CN202211515128.2A CN202211515128A CN115762789A CN 115762789 A CN115762789 A CN 115762789A CN 202211515128 A CN202211515128 A CN 202211515128A CN 115762789 A CN115762789 A CN 115762789A
- Authority
- CN
- China
- Prior art keywords
- plate
- prediction
- hypertensive nephropathy
- factor
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention belongs to the technical field of hypertensive nephropathy nursing, and particularly discloses a hypertensive nephropathy prediction method and a hypertensive nephropathy nursing device, wherein the method comprises the steps of obtaining clinical data of a plurality of samples, and screening effective characteristic parameters in the clinical data by using a single-factor analysis method; based on a multi-factor analysis method, providing a prediction factor in the effective characteristic parameters; taking the prediction factor as input and taking whether hypertensive nephropathy occurs as output to construct a machine prediction model; and collecting the prediction factor data of the person to be tested, and inputting the prediction factor data into the machine prediction model to obtain a prediction result of whether the hypertensive nephropathy occurs. By adopting the technical scheme, the hypertensive nephropathy is predicted, and early intervention treatment and nursing are facilitated.
Description
Technical Field
The invention belongs to the technical field of hypertensive nephropathy nursing, and relates to a hypertensive nephropathy prediction method and a hypertensive nephropathy nursing device.
Background
Hypertensive Nephropathy (HN) is a structural and functional impairment of the kidney caused by essential hypertension, divided into benign Hypertensive nephrosclerosis and malignant Hypertensive nephrosclerosis, which is a significant risk factor for cardiovascular events. According to research, more than 12.8 hundred million adults with hypertension in 2019 all over the world are shown, about 2.45 hundred million adults suffer from hypertension in China, the prevalence rate is as high as 23.2%, and the prevalence rate is in an increasing trend.
Many of the hypertensive patients develop into hypertensive nephropathy, and finally develop into End-Stage nephropathy (ESRD), the prevalence rate of hypertensive nephropathy is on the rise year by year, in the united states, the number of hypertensive nephropathy patients accounts for about 27.5% of newly added Renal dialysis, and chinese Renal Disease network data (CK-NET 2016) shows that the prevalence rate of hypertensive nephropathy in Chronic Kidney Disease (CKD) patients in China is as high as 20.78%, which is the second most dead cause of diabetic nephropathy, and the early identification and diagnosis of hypertensive nephropathy is difficult because HN has atypical clinical symptoms or common symptoms of other diseases. There is therefore a need for early risk prediction for hypertensive nephropathy for early intervention therapy and care to be assessed in advance by medical personnel.
Disclosure of Invention
The invention aims to provide a hypertensive nephropathy prediction method and a hypertensive nephropathy nursing device, which are used for predicting hypertensive nephropathy and are beneficial to early intervention treatment and nursing.
In order to achieve the purpose, the basic scheme of the invention is as follows: a hypertensive nephropathy prediction method comprises the following steps:
acquiring clinical data of a plurality of samples;
screening effective characteristic parameters in clinical data by using a single-factor analysis method;
extracting a prediction factor in the effective characteristic parameters based on a multi-factor analysis method;
taking the prediction factor as input and taking whether hypertensive nephropathy occurs as output to construct a machine prediction model;
and collecting the prediction factor data of the person to be tested, and inputting the prediction factor data into the machine prediction model to obtain a prediction result of whether the hypertensive nephropathy occurs.
The working principle and the beneficial effects of the basic scheme are as follows: and by utilizing the clinical data of the sample, extracting a key prediction factor and reducing the subsequent data processing amount. And a required machine prediction model is constructed by utilizing the prediction factors of the samples, so that the operation is simple and the use is convenient. The prediction factors of the person to be tested are collected in a targeted manner and then input into the machine prediction model to obtain a prediction result, and whether the patient has hypertensive nephropathy or not is judged, so that medical staff can evaluate early intervention treatment and nursing in advance.
Further, comparing the prediction factor data of the sample with the prediction factor data under the normal condition, and dividing the belonging risk grade of the prediction factor according to the comparison result;
adding the risk levels of the prediction factors of each sample, and calculating an average value to obtain the risk level of the sample;
extracting the medication scheme of the sample corresponding to the risk grade, and taking the medicine with the use rate exceeding a preset value in the risk grade as a standby medicine;
and after judging that the person to be tested has hypertensive nephropathy, comparing the prediction factor data of the person to be tested with the prediction range of the risk level, judging the risk level of the hypertensive nephropathy of the person to be tested, and acquiring a standby medicine scheme.
According to the deviation value of the prediction factor and the normal prediction factor of the patient, different danger levels are divided, the larger the deviation value is, the larger the danger level is, so that the medicine taking on the patient is judged, and the phenomenon that the patient takes medicine by mistake and cannot produce a better treatment effect is avoided.
Further, dividing the clinical data of a plurality of samples into a training set and a testing set;
and training the machine prediction model by using a training set through a 5-fold cross validation method, and evaluating the performance of the machine prediction model by using a test set.
The model is trained to optimize the model.
Further, the predictor factors include sex, age, albumin, urine white blood cell count, creatinine, chloride ion, uric acid, mean red blood cell volume, prealbumin, basophil count, ph, fibrinogen and total cholesterol.
And proper prediction factors are extracted, so that the prediction result is more accurate.
Further, the machine prediction model adopts a logistic regression model, a random forest model, an XGboost model or an AdaBoost model.
And selecting a proper machine prediction model according to the requirement, so that the use is facilitated.
The invention also provides a hypertensive nephropathy nursing device, which comprises a bedstead, a fixed plate, a movable plate and a detection mechanism;
the fixed plates are fixedly arranged on two sides of the top of the bedstead and are horizontally arranged, and the side walls of the fixed plates, which are opposite to each other, are provided with transverse sliding chutes;
the two sides of the movable plate are respectively connected with the sliding grooves of the fixed plate in a sliding mode, the movable plate can slide along a connecting line between the bed tail and the bed head, the movable plate is divided into a first plate and a second plate along a transverse line perpendicular to the fixed plate, the first plate and the second plate are rotatably hinged, the bottom of the first plate is fixedly connected with at least three vertical supporting rods, the bottoms of the supporting rods are respectively provided with a roller, one side or two sides of the first plate are provided with a binding band, one end of the binding band is fixedly arranged on the first plate, one side or two sides of the second plate are provided with a positioning clamping ring, and the binding band can be detachably connected with the positioning clamping ring;
the detection mechanism is arranged on the second plate, and executes the method of the invention to detect the body of the patient.
The bed frame and the corresponding plate body form a nursing bed, so that a patient can have a rest conveniently, and the detection mechanism is arranged on the second plate of the movable plate, so that the patient can obtain a prediction result in real time conveniently. The movable plate can slide along the sliding groove of the fixed plate, when a patient needs to get off the bed to move, the patient can possibly cause hemiplegia due to high blood pressure, the legs and the feet are inconvenient, and the patient needs to use a wheelchair when going out, but the existing wheelchair is large in size and inconvenient to place. The movable plate is drawn out from the space between the fixed plates, the second plate upwards rotates around the hinged position of the second plate, the first plate and the second plate form a seat structure, and the movable end of the binding band is connected with the positioning clamping ring on the second plate, so that the second plate is limited. And the connection between different positions of the binding band and the positioning snap ring is adjusted, so that the inclination angle of the second plate can be adjusted, and the use is convenient. The patient sits on the first plate and leans against the second plate, and the movement is realized by the support rods and the rollers at the bottom of the first plate, so that the operation is simple.
Further, the detection mechanism comprises a medicine box, a display and a processor;
the medicine box is used for placing medicines, a notch is formed in one side, close to the bed head, of the movable plate, the medicine box is arranged at the notch, the top of the medicine box is flush with the top of the movable plate, and a door plate capable of being opened and closed is arranged at the top of the medicine box;
the display is arranged on the door plate of the medicine box, and the processor obtains the prediction result of the hypertensive nephropathy and the standby medicine scheme and transmits the prediction result and the standby medicine scheme to the display.
The medicine box is arranged on the movable plate, so that medicine taking is facilitated at any time, and the display is arranged on the medicine box, so that a prediction result and a medicine taking prompt are conveniently and visually displayed.
Furthermore, detection mechanism still includes temperature sensor and rhythm of the heart sensor, the top of movable plate is equipped with the recess, and sliding connection has the liftout plate in the recess, is equipped with the elastic component between the bottom in the bottom of liftout plate and the recess, temperature sensor and rhythm of the heart sensor inlay the top at the liftout plate.
The recess is stretched out to the liftout plate under the effect of elastic component, when the patient arranged in on the movable plate, will liftout plate push down to with movable plate upper surface parallel, under the elastic component interact, guarantee that liftout plate and human closely offset, make temperature sensor and the rhythm of the heart sensor on the liftout plate hug closely with the human body and guarantee that information acquisition is accurate.
Drawings
FIG. 1 is a schematic cross-validation diagram of the LASSO algorithm of the hypertensive renal disease prediction method of the present invention;
FIG. 2 is a schematic top view of the hypertensive nephropathy care apparatus according to the present invention;
FIG. 3 is a schematic diagram showing the structure of the moving plate of the hypertensive renal disease nursing device in a right view.
Reference numerals in the drawings of the specification include: the bed comprises a bed frame 1, a fixed plate 2, a movable plate 3, a plate I4, a plate II 5, a support rod 6, a detection mechanism 7, a medicine box 8, an ejector plate 9, a hinge joint 10 and a binding band 11.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The invention discloses a hypertensive nephropathy prediction method, which comprises the following steps:
clinical data were obtained for multiple samples, for example, clinical information data of 2478 patients with essential hypertension were received: age, sex, history of smoking, history of drinking, whether or not there is diabetes, etc.; biochemical index, blood routine index, blood glucose index, blood lipid index, urine routine index, liver function, kidney function, inflammation index, etc.; and eliminating the index with the deletion rate more than 30 percent, and filling the index with the deletion rate less than or equal to 30 percent by adopting a missForest algorithm.
And screening effective characteristic parameters in clinical data by using a single-factor analysis method. The single factor analysis test standard is set as P<0.05, normal distribution-compliant metrology data adoption
To represent, the comparison among groups adopts t test; the measurement data not complying with normal distribution adopts median and quartile space M (P) 25 ，P 75 ) To show that the comparison between groups uses the Mann-Whitney U test (man-Whitney U test, assuming that two samples are respectively from two populations identical except for the mean of the populations, in order to test whether the mean of the two populations are significantly different); the counting data are expressed in percentage, and the comparison among groups adopts X 2 And (6) checking. Effective characteristic parameters include age, gender, smoking history, diastolic blood pressure, albumin, white blood cell count, large platelet percentage, monocyte count, activated partial thromboplastin time, creatinine, lymphocyte percentage, phosphorus, magnesium ions, urea, uric acid, thrombin time, mean erythrocyte volume, mean erythrocyte hemoglobin concentration, prealbumin, basophil count, eosinophil percentage, ph, fibrinogen, total cholesterol, and the like, as shown in table 1:
TABLE 1 valid characteristic parameters
And extracting a prediction factor in the effective characteristic parameters based on a multi-factor analysis method. The indexes with statistical significance of single-factor analysis are subjected to multi-factor analysis by adopting a LASSO algorithm (LASSO algorithm) + logistic regression to screen the significant risk factors of the hypertensive nephropathy. And (3) screening 45 indexes with statistical significance after the single factor analysis by using the LASSO algorithm again, and determining a lambda value (shown in figure 1) through cross validation to screen 19 indexes with statistical significance of the difference. These 19 indices were subjected to logistic regression analysis, of which 13 had statistical significance, i.e., were the predictors screened. Predictors include sex, age, albumin, urine white blood cell count, creatinine, chloride, uric acid, mean red blood cell volume, prealbumin, basophil count, ph, fibrinogen and total cholesterol as shown in table 2:
TABLE 2 predictors
Constructing a machine prediction model by taking the prediction factor as input and taking whether the hypertensive nephropathy occurs (yes =1, no = 0) as output; the machine prediction model adopts a logistic regression model, a Random Forest (Random Forest) model, an XGboost model or an AdaBoost model.
And collecting the prediction factor data of the person to be tested, and inputting the prediction factor data into a machine prediction model to obtain a prediction result of whether hypertensive nephropathy occurs.
In a preferred scheme of the invention, the prediction factor data of a sample is compared with the prediction factor data under the normal condition, and the belonging risk grade of the prediction factor is divided according to the comparison result;
adding the risk levels of the prediction factors of each sample, and calculating an average value to obtain the risk level of the sample;
extracting the medication scheme of the sample corresponding to the risk grade, and taking the medicine with the use rate exceeding a preset value in the risk grade as a standby medicine;
and after judging that the person to be tested has hypertensive nephropathy, comparing the prediction factor data of the person to be tested with the prediction range of the risk level, judging the risk level of the hypertensive nephropathy of the person to be tested, and acquiring a standby medicine scheme.
According to the deviation value of the prediction factor and the normal prediction factor of the patient, different danger levels are divided, the larger the deviation value is, the larger the danger level is, so that the medicine taking on the patient is judged, and the phenomenon that the patient takes medicine by mistake and cannot produce a better treatment effect is avoided.
In a preferred embodiment of the present invention, clinical data of a plurality of samples are divided into a training set and a testing set, 70% (1734) of patients' data are randomly selected as the training set to construct a prediction model, and the remaining 30% (744) are used as the testing set to perform internal verification. The machine prediction model is trained by using a training set through a 5-fold cross validation method, the performance of the machine prediction model is evaluated by using a test set (as shown in table 3), and the prediction performance of the model is evaluated by using the Area under the ROC curve (AUC), the sensitivity, the specificity, the accuracy and the like.
TABLE 3 comparison of performance of machine prediction models in test set validation
The invention also provides a nursing device for hypertensive nephropathy, which comprises a bed frame 1, a fixed plate 2, a movable plate 3 and a detection mechanism 7, as shown in fig. 2 and 3. The bedstead is an existing metal frame, the fixing plates 2 are fixedly installed (such as welded, riveted and the like) on the left side and the right side of the top of the bedstead 1, the fixing plates 2 are horizontally placed, and the side walls of the fixing plates 2, which are opposite to each other, are provided with transverse sliding grooves;
the left side and the right side of the movable plate 3 are respectively connected with the sliding groove extending into the fixed plate 2 in a sliding way, and the movable plate 3 can slide along a connecting line between the bed tail and the bed head. The moving plate 3 is divided into a first plate 4 and a second plate 5 along a transverse line vertical to the fixed plate 2, and the first plate 4 and the second plate 5 are hinged up and down in a rotating mode. The bottom of the first plate 4 is fixedly connected (such as welded, bonded and the like) with at least three vertical support rods 6, and the bottoms of the support rods 6 are provided with rollers. One side or both sides of board 4 are equipped with bandage 11, and bandage 11 one end is fixed to be set up (like welding, riveting or screw connection etc.) on board 4, and board two 5 is located the one side that is close to the head of a bed, and one side or both sides of board two 5 are equipped with the location snap ring, location pull ring and bandage 11 homonymy and one-to-one. The binding band 11 can be detachably connected with the positioning snap ring, for example, the binding band 11 is directly tied up with the positioning pull ring, or a plurality of hooks are fixed at different positions on the binding band 11, so that the hooks are clamped with the positioning pull ring, and when the binding band 11 is fixed with the positioning pull ring, the binding band 11 is in a stretched state.
The detection mechanism 7 is fixedly arranged on the second plate 5, and the detection mechanism 7 executes the method of the invention to detect the body of the patient, so that the patient can conveniently obtain the prediction result in real time.
The movable plate 3 can slide along the sliding groove of the fixed plate 2, when a patient needs to get off the bed to move, the movable plate 3 is drawn out from the space between the fixed plates 2, the second plate 5 rotates upwards around the hinged part 10 of the second plate, the first plate 4 and the second plate 5 form a seat structure, and at the moment, the movable end of the binding band 11 is connected with the positioning clamping ring on the second plate 5, so that the limitation on the second plate 5 is realized. The binding band 11 is a flexible band, so that the volume of the positioning pull ring is small, and the sliding of the moving plate 3 in the sliding groove of the fixed plate 2 is not influenced. And the connection between different positions of the binding band 11 and the positioning snap ring is adjusted, the inclination angle of the second plate 5 can be adjusted, and the use is convenient. The patient sits on the first plate 4 and backs the second plate 5, and the movement is realized by the support rod 6 and the rollers at the bottom of the first plate 4.
In a preferred embodiment of the present invention, the detection mechanism 7 includes a medicine box 8, a display and a processor. Medicine box 8 is used for placing the medicine, and one side that the movable plate 3 is close to the head of a bed is equipped with the breach, and breach department is arranged in to medicine box 8, and the outer wall of medicine box 8 can be with breach welding or bonding, and the top of medicine box 8 flushes with the top of movable plate 3, and the top of medicine box 8 is equipped with the door plant that can open and shut. The display is arranged on the door plate of the medicine box 8 (connection modes such as embedding or bonding can be adopted), the processor obtains the prediction result of the hypertensive nephropathy and the standby medicine scheme and transmits the prediction result and the standby medicine scheme to the display, and the prediction result and the medication prompt can be conveniently and visually displayed.
More preferably, the detection mechanism 7 further comprises a temperature sensor and a heart rate sensor, the top of the moving plate 3 is provided with a groove, and the ejecting plate 9 is slidably connected in the groove. An elastic piece is arranged between the bottom of the ejector plate 9 and the bottom in the groove, the elastic piece can be a spring, one end of the spring is welded with the bottom of the groove, and the other end of the spring is welded with the bottom of the ejector plate 9. Temperature sensor and heart rate sensor inlay at the top of liftout plate 9, and liftout plate 9 stretches out the recess under the natural state.
When the patient is placed on the movable plate 3, the ejector plate 9 is pressed down to be parallel to the upper surface of the movable plate 3, and under the interaction of the elastic parts, the ejector plate 9 is guaranteed to be tightly abutted against a human body, so that the temperature sensor and the heart rate sensor on the ejector plate 9 are tightly attached to the human body, and the accuracy of information acquisition is guaranteed.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. A hypertensive nephropathy prediction method, comprising the steps of:
acquiring clinical data of a plurality of samples;
screening effective characteristic parameters in clinical data by using a single-factor analysis method;
extracting a prediction factor in the effective characteristic parameters based on a multi-factor analysis method;
taking the prediction factor as input and taking whether hypertensive nephropathy occurs as output to construct a machine prediction model;
and collecting the prediction factor data of the person to be tested, and inputting the prediction factor data into a machine prediction model to obtain a prediction result of whether hypertensive nephropathy occurs.
2. The method according to claim 1, wherein the predictor data of the sample is compared with the predictor data in a normal condition, and the risk level of the predictor is classified according to the comparison result;
adding the risk levels of the prediction factors of each sample and calculating an average value to obtain the risk level of the sample;
extracting the medication scheme of the sample corresponding to the risk level, and taking the medicine with the utilization rate exceeding a preset value in the risk level as a standby medicine;
and after judging that the person to be tested has hypertensive nephropathy, comparing the prediction factor data of the person to be tested with the prediction range of the risk level, judging the risk level of the hypertensive nephropathy of the person to be tested, and acquiring a standby medicine scheme.
3. The method of predicting hypertensive nephropathy according to claim 1, wherein clinical data of the plurality of samples are divided into a training set and a test set;
and training the machine prediction model by using a training set through a 5-fold cross validation method, and evaluating the performance of the machine prediction model by using a test set.
4. The method of predicting hypertensive nephropathy according to claim 1, wherein the prediction factors include gender, age, albumin, urine white blood cell count, creatinine, chloride ions, uric acid, mean red blood cell volume, prealbumin, basophil count, ph, fibrinogen, and total cholesterol.
5. The method for predicting hypertensive nephropathy according to claim 1, wherein the machine prediction model employs a logistic regression model, a random forest model, an XGboost model, or an AdaBoost model.
6. A hypertensive nephropathy nursing device is characterized by comprising a bedstead, a fixed plate, a movable plate and a detection mechanism;
the fixed plates are fixedly arranged on two sides of the top of the bedstead and are horizontally arranged, and the side walls of the fixed plates, which are opposite to each other, are provided with transverse sliding chutes;
the two sides of the movable plate are respectively connected with the sliding grooves of the fixed plate in a sliding mode, the movable plate can slide along a connecting line between the bed tail and the bed head, the movable plate is divided into a first plate and a second plate along a transverse line perpendicular to the fixed plate, the first plate and the second plate are rotatably hinged, the bottom of the first plate is fixedly connected with at least three vertical supporting rods, the bottoms of the supporting rods are respectively provided with a roller, one side or two sides of the first plate are provided with a binding band, one end of the binding band is fixedly arranged on the first plate, one side or two sides of the second plate are provided with a positioning clamping ring, and the binding band can be detachably connected with the positioning clamping ring;
the detection mechanism is mounted on the second plate, and the detection mechanism performs the method of any one of claims 1-5 to perform physical examination on the patient.
7. The hypertensive nephropathy care apparatus of claim 6, wherein the detection mechanism includes a cartridge, a display, and a processor;
the medicine box is used for placing medicines, a notch is formed in one side, close to the bed head, of the movable plate, the medicine box is arranged at the notch, the top of the medicine box is flush with the top of the movable plate, and a door plate capable of being opened and closed is arranged at the top of the medicine box;
the display is arranged on the door panel of the medicine box, and the processor acquires the prediction result of the hypertensive nephropathy and the standby medicine scheme and transmits the prediction result and the standby medicine scheme to the display.
8. The nursing device for hypertensive nephropathy according to claim 6, wherein the detecting mechanism further comprises a temperature sensor and a heart rate sensor, the top of the moving plate is provided with a groove, an eject plate is slidably connected in the groove, an elastic member is arranged between the bottom of the eject plate and the bottom in the groove, and the temperature sensor and the heart rate sensor are embedded in the top of the eject plate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211515128.2A CN115762789A (en) | 2022-11-30 | 2022-11-30 | Hypertensive nephropathy prediction method and care device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211515128.2A CN115762789A (en) | 2022-11-30 | 2022-11-30 | Hypertensive nephropathy prediction method and care device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115762789A true CN115762789A (en) | 2023-03-07 |
Family
ID=85340638
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211515128.2A Pending CN115762789A (en) | 2022-11-30 | 2022-11-30 | Hypertensive nephropathy prediction method and care device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115762789A (en) |
-
2022
- 2022-11-30 CN CN202211515128.2A patent/CN115762789A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sinn et al. | Development of chronic kidney disease in patients with non-alcoholic fatty liver disease: a cohort study | |
| CN108139384B (en) | Respiratory gas analysis | |
| CN103565521B (en) | A kind of diabetes, cardiovascular and cerebrovascular disease monitoring and treatment system | |
| US20160033476A1 (en) | System and method for utilizing exhaled breath for monitoring inflammatory states | |
| Choi et al. | Markers of poor outcome in patients with acute hypoxemic respiratory failure | |
| US20230096779A1 (en) | Toilet, testing, and monitoring systems | |
| CN112992343A (en) | Coronary heart disease auxiliary diagnosis system for type 2 diabetes patients | |
| CN108604464A (en) | Determine the method with variation in subject between the subject of biomarker signal | |
| CN109085163A (en) | A kind of Novel urine testing and analysis system | |
| CN112835316B (en) | Neonatal sepsis shock prediction system and monitoring equipment | |
| CN115762789A (en) | Hypertensive nephropathy prediction method and care device | |
| CN104698160A (en) | Kit for evaluating male urine sodium value within 24 hours | |
| CN108508211A (en) | Starting non-medication schizophrenic patients blood serum designated object FGF9 and its application | |
| CN202393731U (en) | Portable non-invasive blood glucose monitor | |
| CN105433901B (en) | A kind of method and its application measuring human body body fat | |
| CN117116462A (en) | Method, system and device for predicting glycosylated hemoglobin value | |
| CN104569438B (en) | A kind of test kit for assessing women twenty-four-hour urine sodium value | |
| CN111883248B (en) | Prediction system for childhood obesity | |
| Liu et al. | Over time, do anthropometric measures still predict diabetes incidence in Chinese Han nationality population from Chengdu community? | |
| CN113456501A (en) | Monitoring and drug administration device and prediction system for essential hypertension | |
| Radha | Hybrid prediction model for the risk of cardiovascular disease in type-2 diabetic patients | |
| KR100855605B1 (en) | A system for prediction and diagnosis of metabolic syndrome | |
| CN113151443B (en) | Cytokine combined analysis as schizophrenia marker and application thereof | |
| CN104545910A (en) | Method and system for chronic disease early period electrophysiological detection | |
| CN109730657A (en) | A kind of system and method for realizing monitoring physiological and pathological data |
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 |