CN112582052A - Hierarchical diagnosis and treatment system - Google Patents
Hierarchical diagnosis and treatment system Download PDFInfo
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- CN112582052A CN112582052A CN202011360786.XA CN202011360786A CN112582052A CN 112582052 A CN112582052 A CN 112582052A CN 202011360786 A CN202011360786 A CN 202011360786A CN 112582052 A CN112582052 A CN 112582052A
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- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
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- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
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- 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
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- 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
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
The application provides a system is diagnose in grades, includes: the sample acquisition module is used for acquiring sample information from a basic medical institution and uploading the sample information to the cloud platform; the sample inspection module is used for acquiring the acquired sample data from the cloud platform, inspecting the acquired sample data and uploading an inspection result to the cloud platform; the inspection result feedback module is used for feeding back the inspection result to the patient and the doctor; and the judging module is used for judging whether the acquired sample data can be brought into the sample acquisition module according to the diet condition, the movement and emotion, the body position and the medicine taking condition of the patient. The application provides a system is diagnose in grades for the testing result is more accurate. And the purpose of quality control is achieved by establishing a naive Bayes classification mining model.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a hierarchical diagnosis and treatment system.
Background
According to statistics, only 10% of diseases need to be treated in modern large hospitals, a large number of diseases can be treated in community medical service organizations, and treatment of part of chronic diseases can be completed through home care. Therefore, the method that the small diseases enter the community, the large diseases enter the hospital and the patients recover to the community is a feasible and ideal mode for seeking medical advice, and a basic grading diagnosis and treatment mode of basic level first diagnosis, bidirectional referral, quick and slow treatment and up-down linkage needs to be constructed for realizing the environment for seeking medical advice.
At present, a hierarchical diagnosis and treatment information architecture system based on the Internet of things and cloud computing is built in many places and is divided into an online part and an offline part in actual production application. Scattered medical institutions such as community hospitals, private clinics and village and town health hospitals are taken as specimen collection points offline to form a collection service network close to patients. The collected specimens are transported through a professional cold-chain logistics motorcade, and the temperature, the humidity, the tightness and the positions of the specimens can be monitored in real time through an Internet of things platform. The sample is sent to and is accomplished the sample inspection and generate the inspection result by detection mechanism center laboratory behind the detection mechanism, back propelling movement to cloud inspection platform, and the platform is unscrambled through the professional and is sent for patient and doctor again.
However, in the process of collecting, transmitting and inspecting the specimen, the quality control of the specimen is an important index affecting the inspection result, and how to improve the quality control of the specimen becomes a problem which needs to be solved urgently.
Disclosure of Invention
The present invention is directed to overcome the above-mentioned drawbacks of the prior art, and to provide a system for fractional diagnosis and treatment that can effectively improve the quality control of specimens.
A staged diagnostic system comprising:
the sample acquisition module is used for acquiring sample information from a basic medical institution and uploading the sample information to the cloud platform;
the sample inspection module is used for acquiring the acquired sample data from the cloud platform, inspecting the acquired sample data and uploading an inspection result to the cloud platform;
the inspection result feedback module is used for feeding back the inspection result to the patient and the doctor;
further comprising: and the judging module is used for judging whether the collected sample data can be brought into the sample collecting module according to the diet condition, the movement and emotion, the body position and the medicine taking condition of the patient.
Further, according to the hierarchical diagnosis and treatment system, the judging module is further configured to judge whether the pre-nurse-post technical training is in place, whether the acquired sample data can be included in the sample acquiring module according to the standard of the acquisition specification and whether the nurse is lack of responsibility.
Further, in the above hierarchical diagnosis and treatment system, the sample collection module includes a time detection unit and a temperature detection unit;
the time detection unit is used for detecting the transportation time required by the collected sample and uploading the time to the cloud platform;
the temperature detection unit is used for detecting the temperature of the collected sample in the transportation process and uploading the temperature to the cloud platform according to a certain time frequency.
Further, as mentioned above, the system for staged diagnosis and treatment, the sample collection module further comprises: and the tightness detection unit is used for detecting whether the tightness of the specimen in the intelligent specimen box reaches the standard or not and uploading the detection result to the cloud platform.
Further, as mentioned above, the system for staged diagnosis and treatment, the sample collection module further comprises: and the position detection unit is used for detecting the real-time position of the intelligent specimen box for storing the specimen and uploading the real-time position information to the cloud platform.
Further, in the above-mentioned hierarchical diagnosis and treatment system, the sample inspection module further includes a classification unit, and the classification unit classifies the detected sample data according to a certain attribute.
Further, the system for diagnosis and treatment grading as described above, the attributes include: description attributes and classification attributes; the description attributes include: the method comprises the following steps of (1) detecting the name of a project, the type of a sample, the storage time of the sample, the transmission time of the sample, the temperature of the sample, a detection result, a detection reference value and an abnormality prompt;
the classification attribute classifies inspection data into both standard and non-standard categories.
Further, in the above hierarchical diagnosis and treatment system, the classification unit classifies the collected sample data by using a classification algorithm model based on bayesian theorem.
Has the advantages that:
the application provides a system is diagnose in grades through before inspection sample collection, has carried out the abundant consideration to factors such as patient's diet condition, motion and mood, position, the condition of taking medicine, nurse post front technical training not in place, do not have collection standard, nurse's responsibility lacks to make the testing result more accurate. And the method also realizes the purpose of quality control by establishing a naive Bayes classification mining model, performing classification prediction on newly generated inspection data, and backtracking the inspection process of the specimen if the classification prediction result of the model is not up to the standard.
Drawings
FIG. 1 is a block diagram of a staged diagnostic system according to the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a frame diagram of a staged diagnosis and treatment system according to the present invention, as shown in fig. 1, the staged diagnosis and treatment system according to the present invention comprises: the sample acquisition module is used for acquiring sample information from a basic medical institution and uploading the sample information to the cloud platform; the sample inspection module is used for acquiring the acquired sample data from the cloud platform, inspecting the acquired sample data and uploading an inspection result to the cloud platform; the inspection result feedback module is used for feeding back the inspection result to the patient and the doctor;
further comprising: and the judging module is used for judging whether the collected sample data can be brought into the sample collecting module according to the diet condition, the movement and emotion, the body position and the medicine taking condition of the patient.
The judging module is also used for judging whether the pre-nurse-post technical training is in place or not, whether the acquired sample data can be contained in the sample acquiring module or not according to the acquisition standard and whether the nurse's responsibility is lack or not.
Specifically, the grading diagnosis and treatment system based on the internet of things, cloud computing and big data is divided into an online part and an offline part in actual production application. Scattered medical institutions such as community hospitals, private clinics and village and town health hospitals are taken as sample collection points offline to form a collection service network close to patients. The collected samples are transported by a professional cold-chain logistics fleet. The sample is sent to the detection mechanism, then the central laboratory of the detection mechanism completes sample detection and generates a detection result, the detection result is pushed to the cloud detection platform, and the platform is read by a professional and then sent to a patient and a doctor.
In the process of collecting, transmitting and testing the test samples, the quality control of the samples is a major concern. The quality of the test specimen is managed by applying an analysis method of data mining to data formed in the processes of collection of the test specimen, transmission of the test specimen and control of the test quality.
Before the test specimen is collected, the accuracy of the detection result can be influenced by the diet condition, the movement, the emotion, the body position, the medicine taking condition and other aspects of the patient; the quality of the inspected specimen is unqualified due to the reasons that the technical training before the nurse post is not in place, the standard for collection is not provided, the responsibility of the nurse is lacking and the like. Therefore, before sending the collected sample to the cloud platform, the collected data needs to be judged and screened first.
Further, in the above hierarchical diagnosis and treatment system, the sample collection module includes a time detection unit and a temperature detection unit;
the time detection unit is used for detecting the transportation time required by the collected sample and uploading the time to the cloud platform;
the temperature detection unit is used for detecting the temperature of the collected sample in the transportation process and uploading the temperature to the cloud platform according to a certain time frequency.
The sample acquisition module further comprises: and the tightness detection unit is used for detecting whether the tightness of the specimen in the intelligent specimen box reaches the standard or not and uploading the detection result to the cloud platform.
The sample acquisition module further comprises: and the position detection unit is used for detecting the real-time position of the intelligent specimen box for storing the specimen and uploading the real-time position information to the cloud platform.
Specifically, the sample transportation monitoring system consists of an intelligent sample box, a professional cold-chain logistics and a sample monitoring center. The basic medical institution collects the samples and then puts the samples into an intelligent sample box for professional cold-chain logistics transportation. Under the small batch transportation scene of biological samples such as blood, bacterin, DNA, the intelligent sample case is through data such as humiture, leakproofness, position of built-in sensor real-time collection sample incasement biological sample to upload to the cloud platform in real time, customer accessible cloud platform propelling movement information, various parameters, the box position of real-time monitoring incasement sample, can also when data collection surpassed the setting parameter threshold value, appointed personnel are notified to the very first time, quick, accurate processing alarm information.
The factors influencing the quality of the sample in the sample conveying link are mainly time and temperature. The effects of time and temperature on specimen quality have been widely recognized to date.
Further, in the above-mentioned hierarchical diagnosis and treatment system, the sample inspection module further includes a classification unit, and the classification unit classifies the detected sample data according to a certain attribute.
The attributes include: description attributes and classification attributes; the description attributes include: the method comprises the following steps of (1) detecting the name of a project, the type of a sample, the storage time of the sample, the transmission time of the sample, the temperature of the sample, a detection result, a detection reference value and an abnormality prompt;
the classification attribute classifies inspection data into both standard and non-standard categories.
And the classification unit classifies the acquired sample data by using a classification algorithm model based on Bayesian theorem.
Specifically, the basic idea of the design of the quality control data mining method for checking the sample data is as follows: the data set is formed by existing data and comprises two parts of contents, one part is a description attribute and the other part is a classification attribute. The classification attribute classifies test data into two categories, qualified and unqualified. The method and the device have the advantages that the naive Bayes classification mining model is established, then classification prediction is carried out on newly generated inspection data, and if the classification prediction result of the model is not up to the standard, the inspection process of the specimen is backtracked to achieve the purpose of quality control.
In the stage of specimen collection, the name of the inspection item, the type of the specimen, the storage time of the specimen and the like are mainly extracted as description attributes. The data items are collected without the specific information of the patient, so that the desensitization treatment effect is achieved, and in order to represent each test item, the test quality tracking is carried out, and the test strip number is recorded.
In the test specimen transmission stage, two data items of specimen transmission time and specimen temperature are taken as description attributes. And under the condition that the temperature of the specimen has no abnormal value, selecting the average temperature of the transmission time period.
In the specimen examination stage, three data items of an examination result, an examination reference value and an abnormality prompt are extracted as description attributes.
And constructing a data set by the inspection data of all the samples according to the extracted description attributes through early-stage work accumulation, simultaneously bringing in data with qualified inspection quality and data with unqualified inspection quality, and dividing the data into two types of standard inspection quality and unqualified inspection quality. The basic form of the constituent data sets is shown in table 1.
TABLE 1 data constitution of test quality data sets
The naive Bayes model building for quality control of test sample data is set forth below:
the Microsoft Naive Bayes algorithm is a Bayesian-based classification algorithm provided by Microsoft SQL Server Analysis Services, and performs predictive modeling by finding the relationship between input columns and predictable columns. For each state of the predictability variable, a naive bayes algorithm computes a probability for each possible state of the input. These probabilities can then be used to predict new targets. Since the algorithm is quite simple, it builds models very quickly.
In the application, the extracted data is stored in the SQLServer, and a naive Bayesian classification model is constructed through the analysis service of the SQLServer. The operation steps are more, and only the main steps are described as follows.
(1) Installing the analysis service configured with the SQLServer;
(2) a data set prepared according to the method of section 3.1;
(3) establishing a Microsoft Naive Bayes data mining model in SQLServerDataTools;
(4) carrying out classification prediction on newly generated inspection data through the established mining model;
(5) and backtracking the test specimen with the quality not reaching the standard according to the classification prediction result so as to improve the test quality.
In a hierarchical diagnosis and treatment system, the sample cloud inspection is carried out, so that rural and community hospitals can share more advanced inspection equipment, and the time and expense of routes are reduced for patients. However, the test samples need to be stored for a period of time after being sampled and transported for a long distance through a cold chain, and the test quality may be affected. Therefore, the method for classifying the inspection quality of the inspection sample by using the naive Bayesian algorithm can help hospitals and inspection institutions to find samples with abnormal inspection quality from a large number of inspection samples. Through the establishment of the algorithm system, the quality control process of the test specimen can be improved, and the specimen test capability of each participant in the hierarchical diagnosis system can be improved.
Finally, it should be noted that: the above examples are only used to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A staged diagnostic system comprising:
the sample acquisition module is used for acquiring sample information from a basic medical institution and uploading the sample information to the cloud platform;
the sample inspection module is used for acquiring the acquired sample data from the cloud platform, inspecting the acquired sample data and uploading an inspection result to the cloud platform;
the inspection result feedback module is used for feeding back the inspection result to the patient and the doctor;
it is characterized by also comprising: and the judging module is used for judging whether the acquired sample data can be brought into the sample acquisition module according to the diet condition, the movement and emotion, the body position and the medicine taking condition of the patient.
2. The staged diagnosis and treatment system according to claim 1, wherein said determining module is further configured to determine whether the pre-nurse training is in place, whether the collected sample data can be collected into the sample collecting module according to the standard of collection standard and whether the nurse is lack of responsibility.
3. The staged diagnosis and treatment system according to claim 1, wherein the sample collection module comprises a time detection unit and a temperature detection unit;
the time detection unit is used for detecting the transportation time required by the collected sample and uploading the time to the cloud platform;
the temperature detection unit is used for detecting the temperature of the collected sample in the transportation process and uploading the temperature to the cloud platform according to a certain time frequency.
4. The staged diagnostic system of claim 3, wherein the sample collection module further comprises: and the tightness detection unit is used for detecting whether the tightness of the specimen in the intelligent specimen box reaches the standard or not and uploading the detection result to the cloud platform.
5. The staged diagnostic system of claim 4, wherein the sample collection module further comprises: and the position detection unit is used for detecting the real-time position of the intelligent specimen box for storing the specimen and uploading the real-time position information to the cloud platform.
6. The staged diagnosis and treatment system according to claim 5, wherein the sample testing module further comprises a classifying unit, wherein the classifying unit classifies the detected sample data according to certain attributes.
7. The staged diagnostic system of claim 6, wherein the attributes comprise: description attributes and classification attributes; the description attributes include: the method comprises the following steps of (1) detecting the name of a project, the type of a sample, the storage time of the sample, the transmission time of the sample, the temperature of the sample, a detection result, a detection reference value and an abnormality prompt;
the classification attribute classifies inspection data into both standard and non-standard categories.
8. The staged diagnosis and treatment system according to claim 7, wherein the classification unit classifies the collected sample data using a classification algorithm model based on Bayesian theorem.
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