CN117393106A - Blood purification quality control AI auxiliary system based on weight score - Google Patents
Blood purification quality control AI auxiliary system based on weight score Download PDFInfo
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- 238000003908 quality control method Methods 0.000 title claims abstract description 66
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- 201000010099 disease Diseases 0.000 claims abstract description 18
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 18
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- 238000003745 diagnosis Methods 0.000 claims abstract description 10
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- 238000000502 dialysis Methods 0.000 claims description 15
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- 229940079593 drug Drugs 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000003556 assay Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000011161 development Methods 0.000 abstract description 3
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- 238000001631 haemodialysis Methods 0.000 description 8
- 230000000322 hemodialysis Effects 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 6
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- 238000011160 research Methods 0.000 description 3
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- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 2
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- 102000003982 Parathyroid hormone Human genes 0.000 description 2
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- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 2
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- 239000011574 phosphorus Substances 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 239000011591 potassium Substances 0.000 description 2
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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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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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
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
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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
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- 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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Abstract
The invention discloses a blood purification quality control AI auxiliary system based on weight assignment, which comprises a data acquisition module, a data analysis module, a quality control module, a report generation module, a diagnosis and treatment scheme module and a disease writing module, wherein the modules are sequentially connected, the data analysis module obtains quality control scores and trend graphs based on the weight assignment of blood purification quality control indexes, the report generation module is used for generating various reports, including treatment reports, quality control reports and daily management plans of patients, the generated reports are sent to users, local and cloud servers, the report generation module comprises treatment reports and quality control reports of the patients, the AI analyzes the data, can sensitively discover problems in blood purification, can warn the problems, further provides guarantee for medical safety, is beneficial to alleviating rural medical gaps, and is beneficial to solving the problem of unbalanced development of medical resources in China.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a blood purification quality control AI auxiliary system based on weight assignment. By giving weights to indexes reflecting the quality control of the blood purification treatment, the quality control of the blood purification treatment is evaluated while giving a quality-sustaining improvement scheme.
Background
Along with the increasing number of chronic kidney disease patients, blood purification technology is increasingly widely applied in China. However, the quality control index of blood purification is complex, quality management is mostly based on experience judgment, and individual management is required, so that the comprehensive quality improvement is not obvious. Therefore, there is a need for standardized, intelligent auxiliary systems that can help medical institutions to efficiently perform quality control assessments and improvements. Under the background of big data age, digital health application has become an important means for effectively managing chronic diseases, and intelligent diagnosis and treatment will become an important direction of blood purification technology. By giving weight to the test result of the related quality control test of the patient, a standardized and intelligent AI auxiliary system for controlling the quality of the blood purification is developed, so that the data of the index of controlling the quality of the blood purification is rapidly and effectively used and analyzed, the problems existing in the blood purification are timely found and adjusted, and a basis is provided for the decision of medical staff. The scarcity of high-quality medical resources, the continuous rise of medical cost and the rapid and accurate requirements of patients on diagnosis and treatment means are contradicted.
However, there is rarely a blood purification quality control AI auxiliary system based on weight assignment in the market at present, or there is a certain defect in the related technology, such as low working efficiency and large medical gap in urban and rural areas.
Disclosure of Invention
The invention aims to overcome the technical defects, and provides a blood purification quality control AI auxiliary system based on weight assignment, which solves the problems of low working efficiency and large urban and rural medical treatment gap.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: the blood purification quality control AI auxiliary system based on weight assignment comprises a data acquisition module, a data analysis module, a quality control module, a report generation module and a disease aspiration writing module, wherein the data analysis module, the quality control module, the report generation module and the disease aspiration writing module are sequentially connected.
Further, the data acquisition module is used for acquiring the baseline information of the user, including the basic information of the patient, the blood purification treatment information and the like, and sending the baseline information to the local database and the cloud server.
Further, the data analysis module, AI, analyzes the collected data based on the standard weight model to obtain a quality control score and draw a trend graph.
Further, the quality control module is used for performing quality control (including the use condition of the filter, the actual operation process, the medicine taking condition before dialysis and the like) according to the result of the data analysis module to obtain a quality control score.
Further, the report generating module is configured to generate various reports, including a treatment report, a quality control report, a daily management plan, etc. of the patient, where the generated reports are sent to the user, the local server, and the cloud server, and the report generating module is linked with the diagnosis and treatment scheme module and the disease aspiration writing module.
Further, the data acquisition module comprises monitoring information, assay checking information and basic information, the data analysis module comprises AI (advanced technology attachment) for analyzing the acquired data based on a standard weight model, and the quality control module comprises the use condition, the actual operation process, the drug taking condition before dialysis and the like of the filter.
Further, the report generation module comprises a daily management plan, a diagnosis and treatment scheme and a quality control report of the patient.
Compared with the prior art, the invention has the advantages that: based on medical teaching and research platform, work efficiency is improved: at present, large-scale modern medical centers in China bear a great deal of medical teaching and research work, so that the work energy is limited, and the AI auxiliary improvement of the work efficiency is required for the follow-up of chronic diseases. Meanwhile, the center with medical teaching and research can be provided with a capability development technical platform. But to date, artificial intelligence is in research and development hospitals. The combination of AI and medical treatment will release great productivity, compared with traditional medical treatment, the doctor judges the disease by experience, AI can replace doctor to memorize the beginning and end of the disease of the patient, assist doctor's diagnosis and treatment and even doctor with poor clinical experience can make the most reasonable clinical decision to the patient through AI auxiliary system. Not only improves the working efficiency of doctors, but also solves the problem of insufficient experienced doctors.
Auxiliary diagnosis and treatment: AI can be through the article of analysis domestic and abroad kidney system disease as giving weight's data reference, provide diagnosis and treatment reference basis for the doctor. Standardization, normalization, programming and data acquisition facilitation: AI may first implement the treatment course criteria; second, the dialysis parameter specification, the large amount of data generated by the patient during treatment can be compared and analyzed; thirdly, the countermeasures are unified, and unified clinical guideline specifications are provided; fourth, new data can be generated to form new data queues and models.
Providing safety guarantee: the AI can sensitively discover the problems existing in blood purification by analyzing the data, and the AI can carry out dangerous warning on the existing problems, so that the medical safety is guaranteed.
The urban and rural medical treatment gap is reduced: the AI is beneficial to relieving urban and rural medical gaps, and the AI+remote diagnosis and treatment can help solve the problem of unbalanced development of medical resources in China.
In a word, the AI auxiliary system not only enhances the productivity, but also provides a guarantee for medical safety, and solves the problem of urban and rural medical gap.
Drawings
Fig. 1 is a schematic flow diagram of a weight model of a blood purification quality control AI auxiliary system based on weight assignment according to the present invention.
Fig. 2 is a schematic flow chart of a blood purification quality control AI auxiliary system based on weight assignment according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention.
Example 1:
hemodialysis patients were admitted after:
hemodialysis patients were admitted after:
1, data acquisition: basic information (e.g., 1. Demographic characteristics include age, sex, race, education level, etc.), 2. Disease characteristics include disease type, course, severity, 3. Physical examination: height, weight, blood pressure, etc., laboratory examination: hematology index, biochemistry index, 4. Lifestyle: eating habits, exercise amount, etc., 5. Prior art history including medication type, dosage, duration, etc., 6. Mental health assessment including depression symptom assessment), 2 hemodialysis blood index assessment including (1) urea clearance index (Kt/V) and urea decline rate (URR) (2) beta 2 microglobulin (3) blood convention, (4) hematobiochemistry, (5) whole-segment parathyroid hormone (iPTH), (6) serum albumin, (7) transferrin saturation, (8) serum ferritin (9)C reactive protein (CRP), electrolyte conditions: calcium, phosphorus, potassium, sodium, etc.), 3 hemodialysis parameters (e.g., fluid temperature, dialysis time, dialysis conditions, etc.), 4 parameters such as parameters for operating a hemodialysis machine, blood pressure of a patient, etc. 5 hemodialysis ultrafiltration volume.
2 data analysis: the collected data is analyzed to assess quality control and efficiency of the hemodialysis process. For example, the use of the filter may be analyzed to assess the efficiency and life of the filter; the normative of the operation process can be analyzed, and the operation skill and the operation normative of operators can be evaluated; the treatment effect of the patient can be analyzed, and the rationality and treatment effect of the treatment scheme can be evaluated.
3, quality control: quality control is performed based on the analysis results, including monitoring the use of filters, normalizing the operation process, and the like. For example, the use of the filter can be monitored, and if the use efficiency or the service life of the filter is not reasonable, the use strategy of the filter can be timely adjusted; the operation process can be standardized, and if the operation skill or operation standardization of the operator is found to be problematic, training and assessment can be performed to improve the operation quality control.
4 report generation: for generating various reports including patient treatment reports, quality control reports, and the like. For example, a patient's treatment report may be generated, including patient basic information, data during treatment, treatment effects, etc.; quality control reports may be generated including the implementation of quality control measures, quality control effects, etc.
5. Writing of the emotion: AI can integrate vital signs, physical examination, medical advice and examination results and combine the disease aspiration writing template to write daily disease.
Example 2:
the patient for peritoneal dialysis is admitted to the hospital,
1, data acquisition: basic information (such as demographic characteristics including age, sex, race, education degree and the like, disease characteristics including disease type, disease course, severity and the like, physical examination including height, weight, blood pressure and the like, laboratory examination including blood science index, biochemical index, life style including eating habit, exercise amount and the like, past medication history including medication type, dosage, duration and the like, mental health evaluation including depression symptom evaluation), blood index evaluation of 2 peritoneal dialysis including (1) urea clearance index (Kt/V) and urea decline rate (URR) control rate (2) beta 2 microglobulin timing test completion rate (3) blood routine, (4) blood biochemistry, (5) whole-segment parathyroid hormone (iPTH), (6) serum albumin, (7) transferrin saturation, (8) serum ferritin, (9)C) reactive protein (CRP), electrolyte conditions including calcium, phosphorus, potassium, sodium and the like), peritoneal dialysis parameters including (such as dialysis solution temperature, dialysis time and the like), 4 peritoneal balance 5 sterile peritoneal operation residual peritoneal membrane function of 7, and ultrafiltration volume evaluation.
2 data analysis: the collected data is analyzed to assess the quality control and efficiency of the peritoneal dialysis process. For example, the use of the peritoneum can be analyzed, and the efficiency and life of the peritoneum can be assessed; the normative of the operation process can be analyzed, and the operation skill and operation normative of the operator (whether to follow the sterile principle) can be evaluated; the treatment effect of the patient can be analyzed, and the rationality and treatment effect of the treatment scheme can be evaluated.
3, quality control: quality control is performed based on the analysis results, including monitoring the use of filters, normalizing the operation process, and the like. For example, the filtration effect of the peritoneum can be evaluated, and the benefits of hemodialysis and peritoneal dialysis can be analyzed; the operating process may be standardized and if problems are found with the operator's operating skills or operating normative, training may be performed to improve the quality control of the operation.
4 report generation: for generating various reports including patient treatment reports, quality control reports, and the like. For example, a patient's treatment report may be generated, including patient basic information, data during treatment, treatment effects, etc.; quality control reports may be generated including the implementation of quality control measures, quality control effects, etc.
5. Writing of the emotion: AI can integrate vital signs, physical examination, medical advice and examination results and combine the disease aspiration writing template to write daily disease.
In the implementation of the invention, the data acquisition module is networked with the medical institution, and the data is uploaded to the local and cloud servers.
The data analysis module analyzes the collected data to assess quality control and efficiency of the blood purification process. The content of the analysis may include the use of the filter, normalization of the procedure, the therapeutic effect of the patient, etc. The method of analysis may employ various data analysis tools and algorithms, such as machine learning algorithms, data mining algorithms, and the like.
The quality control module performs quality control according to the analysis result, including monitoring the use of the filter, normalizing the operation process, and the like. The control method can adopt various quality control tools and algorithms, such as statistical quality control tools, process capability index algorithms and the like.
The report generation module is used to generate various reports including patient treatment reports, quality control reports, and the like. The generated report may employ various report generation tools and algorithms, such as text generation algorithms, chart generation algorithms, and the like.
In practicing the present invention, different hardware components and software tools may be selected according to different blood purification devices and systems. For example, for a dialysis machine, a sensor such as a temperature sensor, a pressure sensor, a flow sensor, etc., and a data collector on the dialysis machine may be selected; for the hemofilter, a blood flow sensor, a filter usage sensor, and the like, and a data collector may be selected. In terms of data analysis and quality control, different data analysis tools and algorithms, and quality control tools and algorithms may be selected as desired. In terms of report generation, different report generation tools and algorithms may be selected as desired.
In practicing the present invention, security and privacy protection issues of the data need to be considered. Various measures can be taken, such as encrypting data for transmission, adopting a security algorithm when storing data, etc., so as to protect the security and privacy of the data.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (7)
1. The blood purification quality control AI auxiliary system based on weight assignment comprises a data acquisition module, a data analysis module, a quality control module and a report generation module, and is characterized in that the weight module, the quality control module and the report generation module are sequentially connected.
2. The weight-scoring-based blood purification quality control AI-assistance system of claim 1, wherein the data acquisition module is configured to acquire baseline information of the user, including basic information of the patient, blood purification treatment information, etc., and send the baseline information to the local database and the cloud server.
3. The weight-scoring-based blood purification quality control AI-assistance system of claim 1, wherein the data analysis module, AI, based on a standard weight model, analyzes the collected data to derive a quality control score and map a trend graph.
4. The weight-based blood purification quality control AI auxiliary system according to claim 1, wherein the quality control module is configured to perform quality control (including usage of a filter, actual operation process, and administration of a drug before dialysis, etc.) according to the result of the data analysis module to obtain a quality control score.
5. The weight score-based blood purification quality control AI assistance system of claim 1, wherein the report generation module is configured to generate various reports including patient treatment reports, quality control reports, daily management plans, etc., the generated reports are sent to the user, local and cloud servers, and the report generation module is linked with the diagnosis and treatment plan module and the disease writing module.
6. The data acquisition module according to claim 1, comprising monitoring information, assay examination information and basic information, wherein the data analysis module comprises AI for analyzing the acquired data based on a standard weight model, and the quality control module comprises the use condition of the filter, the actual operation process, the administration condition of the drug before dialysis, and the like.
7. The weight score-based blood purification quality control AI assistance system of claim 5 wherein the report generation module is configured to generate various reports including patient treatment reports, quality control reports, daily management plans, etc., the generated reports are sent to the user, local and cloud servers, and the report generation module is linked to the diagnosis and treatment protocol module and the disease writing module.
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