CN117727468B - Cloud-edge collaboration-based intelligent diagnosis and separation system for health screening - Google Patents
Cloud-edge collaboration-based intelligent diagnosis and separation system for health screening Download PDFInfo
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Abstract
The invention belongs to the field of health screening, relates to a data analysis technology, and is used for solving the problem that a health screening intelligent diagnosis system in the prior art cannot combine historical data such as inspection data and treatment data of corresponding diseases to carry out treatment decision analysis; the user management module is used for carrying out management analysis on physical examination data of the user, sending the matched disease seeds to the intelligent diagnosis and separation platform, and sending the matched disease seeds to the group analysis module after the intelligent diagnosis and separation platform receives the matched disease seeds.
Description
Technical Field
The invention belongs to the field of health screening, relates to a data analysis technology, and particularly relates to a cloud-edge collaboration-based intelligent diagnosis and separation system for health screening.
Background
Effective health check and screening programs can help to find potential diseases or health problems early, common health check and screening programs include: blood pressure tests, blood tests, electrocardiography, cancer screening, eye screening, etc., the specific health tests and screening program should be based on the age, sex, family history, and health condition of the individual.
The intelligent diagnosis and screening system for health screening in the prior art only performs disease diagnosis according to the inspection data of the user, but cannot combine the historical data such as the inspection data and the treatment data of the corresponding disease to perform treatment decision analysis, and cannot directly output treatment schemes and treatment decisions for the user.
The application provides a solution to the technical problem.
Disclosure of Invention
The invention aims to provide a cloud-edge-collaboration-based intelligent diagnosis and screening system for health screening, which is used for solving the problem that the intelligent diagnosis and screening system for health screening in the prior art cannot be combined with historical data such as inspection data and treatment data of corresponding diseases to carry out treatment decision analysis;
The technical problems to be solved by the invention are as follows: how to provide a cloud-edge collaboration-based intelligent health screening diagnosis system which can combine historical data such as inspection data and treatment data of corresponding diseases to carry out treatment decision analysis.
The aim of the invention can be achieved by the following technical scheme:
the intelligent triage system based on cloud-edge cooperation for health screening comprises an intelligent triage platform, wherein the intelligent triage platform is in communication connection with a user management module, a group analysis module, a diagnosis analysis module and a storage module;
The user management module is used for performing management analysis on physical examination data of a user, obtaining matched disease types of the user, sending the matched disease types to the intelligent triage platform, and sending the matched disease types to the group analysis module after the intelligent triage platform receives the matched disease types;
The group analysis module is used for carrying out characteristic analysis according to the diseased crowd matched with the disease species and obtaining the diseased crowd, sending the diseased crowd to the intelligent triage platform, and sending the diseased crowd to the diagnosis analysis module after the intelligent triage platform receives the diseased crowd;
The diagnostic analysis module is used for carrying out disease diagnostic analysis on the users according to the disease population: the method comprises the steps of obtaining historical physical examination data of a user, marking time marks, in which abnormal indexes exceed a normal range for the first time, in the historical physical examination data as first time, marking time difference values of current system time and the first time as analysis duration, then carrying out matching analysis on diseased groups and obtaining diagnosis objects, obtaining treatment data of the diagnosis objects, and sending the treatment data of the diagnosis objects to a mobile phone terminal of the user through an intelligent diagnosis platform, wherein the treatment data of the diagnosis objects comprise treatment schemes, review periods and notes.
As a preferred embodiment of the present invention, the process for obtaining the matched disease seeds of the user includes: marking indexes exceeding a normal range in physical examination data of a user as abnormal indexes, forming an abnormal set by all the abnormal indexes of the user, acquiring an observation set of all disease types through a storage module, wherein the observation set of the disease types comprises a plurality of observation indexes, and comparing the abnormal set of the user with the observation set one by one.
As a preferred embodiment of the present invention, the specific process of comparing the abnormal set of the user with the observed set one by one includes: selecting one observation set and marking the observation set as a comparison set, marking the number of elements in the abnormal set and the comparison set as an abnormal value YC and a comparison value BD respectively, and marking the number of common elements in the abnormal set and the comparison set as a common value GY; the matching coefficient PP of the comparison set is obtained by carrying out numerical calculation on the abnormal value YC, the comparison value BD and the common value GY; and then selecting the next observation set as a comparison set and re-calculating the matching coefficient PP until all the observation sets are used as the comparison set to finish the calculation of the matching coefficient PP, marking the disease corresponding to the observation set with the largest value of the matching coefficient PP as a matching disease, and marking the observation index of the matching disease as a matching index.
As a preferred embodiment of the present invention, the acquisition process of the diseased population includes: the identity information of all patients matched with the disease is obtained through a storage module, the identity information of the patients comprises the gender and the age of the patients, the age range is formed by the maximum value and the minimum value of the ages of the patients, the age range is divided into a plurality of age ranges, the age ranges matched with the ages of the users are marked as matching ranges, and all the patients with the same gender as the users and the ages within the matching ranges form a disease group.
As a preferred embodiment of the invention, the specific process of matching analysis of a diseased population comprises: randomly selecting one patient in a diseased group and marking the patient as an analysis object, taking the time mark of the analysis object history physical examination data, in which the abnormal index exceeds the normal range for the first time, as analysis time, establishing a plurality of index-time coordinate systems for the analysis object, wherein the index-time coordinate systems are in one-to-one correspondence with the matching index, drawing a graph of the matching index corresponding to the patient and the user in the index-time coordinate systems and marking the graph as a patient curve and a user curve respectively, making an intercepting straight line perpendicular to the X axis in the index-time coordinate systems, intercepting the abscissa of the intersection of the straight line and the X axis as A1, taking the time difference between the A1 and the analysis time as analysis time, obtaining the closed data FB and the uniform data JY of the analysis object, and performing numerical calculation to obtain the diagnosis coefficient ZD of the analysis object; randomly selecting the next patient in the diseased group and marking the next patient as an analysis object, and recalculating the diagnosis coefficient ZD of the analysis object until all the patients are marked as the analysis object and completing calculation of the diagnosis coefficient ZD; marking the analysis object as a matching object or an irrelevant object through the numerical value of the diagnosis coefficient ZD; and screening the matched object with the best treatment effect according to the change trend of the matched index of the matched object, and marking the matched object as a diagnosis object.
As a preferred embodiment of the present invention, the process for acquiring the closed data FB and the uniform data JY of the analysis object includes: a U-shaped area is formed by a Y axis, an X axis and a cut straight line, the sum of the area values of all the closed areas formed between the patient curve and the user curve in the U-shaped area is marked as a closed value, the closed values corresponding to all the matched indexes of the analysis objects are summed and averaged to obtain closed data FB, and variance calculation is carried out on the closed values corresponding to all the matched indexes of all the analysis objects to obtain uniform data JY.
As a preferred embodiment of the present invention, the specific process of marking an analysis object as a matching object or an irrelevant object includes: the diagnostic threshold ZDmax is obtained by the storage module, and the diagnostic coefficients ZD of all the analysis objects are compared with the diagnostic threshold ZDmax one by one: if the diagnostic coefficient ZD is smaller than the diagnostic threshold ZDmax, marking the corresponding analysis object as a matching object; if the diagnostic coefficient ZD is equal to or greater than the diagnostic threshold ZDmax, the corresponding analysis object is marked as an irrelevant object.
A cloud-edge collaboration-based intelligent diagnosis and treatment system for health screening comprises the following steps:
Step one: management analysis is carried out on physical examination data of the user: marking indexes exceeding a normal range in physical examination data of a user as abnormal indexes, forming an abnormal set by all abnormal indexes of the user, acquiring an observation set of all disease types through a storage module, comparing the abnormal set of the user with the observation set one by one, and marking matched disease types through comparison results;
Step two: and carrying out feature analysis on the diseased population according to the matched disease species: the method comprises the steps that identity information of all patients matched with a disease is obtained through a storage module, the identity information of the patients comprises the gender and the age of the patients, an age range is formed by the maximum value and the minimum value of the ages of the patients, the age range is divided into a plurality of age ranges, and all the patients with the same gender as the users and the ages within the matched range form a disease group;
step three: performing a diagnostic analysis of the condition for the user based on the diseased population: the method comprises the steps of obtaining historical physical examination data of a user, marking time marks, in which abnormal indexes exceed a normal range for the first time, in the historical physical examination data as first time, marking time difference values of current system time and the first time as analysis duration, and then carrying out matching analysis on diseased groups to obtain a diagnosis object.
The invention has the following beneficial effects:
1. The physical examination data of the user can be managed and analyzed through the user management module, the disease types are matched according to the abnormal indexes in the physical examination data of the user, the observation indexes of each disease type are compared with the abnormal indexes of the user, then the matched disease types are obtained through screening according to the comparison result, and the disease condition of the user is screened rapidly;
2. the group analysis module can perform feature analysis on the diseased group according to the matched disease types, and perform patient group screening according to the identity information of the user and the patient, and perform diseased group construction according to the patient which is most in line with the current physical condition of the user, so as to provide data support for the diagnosis and analysis process;
3. the diagnosis analysis module can be used for carrying out disease diagnosis analysis on the users according to the disease groups, analyzing the trend of the matching index of each patient in the disease groups, then generating corresponding sealing values by combining the corresponding index-time coordinate system of the matching index, feeding back the matching degree of the matching index development trend of the patient and the user through the sealing values, finally obtaining diagnosis objects, carrying out auxiliary treatment on the users through the diagnosis objects, and improving the efficiency of health screening and diagnosis.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, the intelligent triage system for health screening based on cloud edge cooperation comprises an intelligent triage platform, wherein the intelligent triage platform is in communication connection with a user management module, a group analysis module, a diagnosis analysis module and a storage module.
The user management module is used for performing management analysis on physical examination data of the user: marking indexes exceeding a normal range in physical examination data of a user as abnormal indexes, forming an abnormal set by all the abnormal indexes of the user, acquiring an observation set of all disease types through a storage module, wherein the observation set of the disease types comprises a plurality of observation indexes, and comparing the abnormal set of the user with the observation set one by one: selecting one observation set and marking the observation set as a comparison set, marking the number of elements in the abnormal set and the comparison set as an abnormal value YC and a comparison value BD respectively, and marking the number of common elements in the abnormal set and the comparison set as a common value GY; obtaining a matching coefficient PP of a comparison set through a formula PP=α1×GY/YC+α2×GY/BD, wherein α1 and α2 are both proportional coefficients, and α1 > α2 > 1; then selecting the next observation set as a comparison set and re-calculating the matching coefficient PP until all the observation sets are used as the comparison set to finish the calculation of the matching coefficient PP, marking the disease corresponding to the observation set with the largest value of the matching coefficient PP as a matching disease, and marking the observation index of the matching disease as a matching index; the method comprises the steps that the matched disease seeds are sent to an intelligent triage platform, and the intelligent triage platform sends the matched disease seeds to a group analysis module after receiving the matched disease seeds; and performing management analysis on physical examination data of the user, matching disease types according to abnormal indexes in the physical examination data of the user, comparing the observation indexes of each disease type with the abnormal indexes of the user, screening according to the comparison result to obtain matched disease types, and rapidly screening the disease condition of the user.
The population analysis module is used for carrying out feature analysis on the diseased population according to the matched disease seeds: the method comprises the steps that identity information of all patients matched with a disease is obtained through a storage module, the identity information of the patients comprises the gender and the age of the patients, an age range is formed by the maximum value and the minimum value of the ages of the patients, the age range is divided into a plurality of age ranges, the age ranges matched with the ages of users are marked as matching ranges, all the patients with the same gender as the users and the ages within the matching ranges form a disease group, the disease group is sent to an intelligent diagnosis and separation platform, and the disease group is sent to a diagnosis analysis module after the intelligent diagnosis and separation platform receives the disease group; and carrying out feature analysis on the diseased group according to the matched disease types, screening the diseased group by using identity information of the user and the patient, constructing the diseased group by using the patient which is most in line with the current physical condition of the user, and providing data support for the diagnosis analysis process.
The diagnostic analysis module is used for carrying out disease diagnostic analysis on the users according to the disease population: acquiring historical physical examination data of a user, marking the time mark with the abnormal index exceeding the normal range in the historical physical examination data for the first time as the first time, marking the time difference value between the current system time and the first time as analysis duration, and then carrying out matching analysis on the diseased group: randomly selecting one patient in a diseased group and marking the patient as an analysis object, marking the time when an abnormal index exceeds a normal range for the first time in historical physical examination data of the analysis object as analysis time, establishing a plurality of index-time coordinate systems for the analysis object, wherein the index-time coordinate systems are in one-to-one correspondence with the matched index, drawing a graph of the matched index corresponding to the patient and a user in the index-time coordinate systems and marking the graph as a patient curve and a user curve respectively, making an intercepting straight line perpendicular to an X axis in the index-time coordinate systems, wherein the abscissa of the intersection of the intercepting straight line and the X axis is A1, and the time difference between the A1 and the analysis time is analysis duration; forming a U-shaped area by a Y-axis, an X-axis and a cut straight line, marking the sum of the area values of all closed areas formed between a patient curve and a user curve in the U-shaped area as a closed value, summing the closed values corresponding to all the matched indexes of the analysis objects, taking an average value to obtain closed data FB, and carrying out variance calculation on the closed values corresponding to all the matched indexes of all the analysis objects to obtain uniform data JY; obtaining a diagnosis coefficient ZD of an analysis object through a formula ZD=β1×FB+β2×JY, wherein β1 and β2 are proportionality coefficients, and β1 > β2 > 1; randomly selecting the next patient in the diseased group and marking the next patient as an analysis object, and recalculating the diagnosis coefficient ZD of the analysis object until all the patients are marked as the analysis object and completing calculation of the diagnosis coefficient ZD; the diagnostic threshold ZDmax is obtained by the storage module, and the diagnostic coefficients ZD of all the analysis objects are compared with the diagnostic threshold ZDmax one by one: if the diagnostic coefficient ZD is smaller than the diagnostic threshold ZDmax, marking the corresponding analysis object as a matching object; if the diagnosis coefficient ZD is larger than or equal to the diagnosis threshold ZDmax, marking the corresponding analysis object as an irrelevant object; screening a matched object with the best treatment effect according to the change trend of the matched index of the matched object, marking the matched object as a diagnosis object, acquiring treatment data of the diagnosis object, and transmitting the treatment data of the diagnosis object to a mobile phone terminal of a user through an intelligent diagnosis platform, wherein the treatment data of the diagnosis object comprises a treatment scheme, a rechecking period and notes; and carrying out disease diagnosis analysis on users according to the diseased group, analyzing the trend of the matching index of each patient in the diseased group, then generating a corresponding sealing value by combining the corresponding index-time coordinate system of the matching index, feeding back the matching degree of the matching index development trend of the patient and the user through the sealing value, finally obtaining a diagnosis object, carrying out auxiliary treatment of the users through the diagnosis object, and improving the efficiency of health screening diagnosis.
Example two
As shown in fig. 2, the intelligent triage method for health screening based on cloud-edge coordination comprises the following steps:
Step one: management analysis is carried out on physical examination data of the user: marking indexes exceeding a normal range in physical examination data of a user as abnormal indexes, forming an abnormal set by all abnormal indexes of the user, acquiring an observation set of all disease types through a storage module, comparing the abnormal set of the user with the observation set one by one, and marking matched disease types through comparison results;
Step two: and carrying out feature analysis on the diseased population according to the matched disease species: the method comprises the steps that identity information of all patients matched with a disease is obtained through a storage module, the identity information of the patients comprises the gender and the age of the patients, an age range is formed by the maximum value and the minimum value of the ages of the patients, the age range is divided into a plurality of age ranges, and all the patients with the same gender as the users and the ages within the matched range form a disease group;
step three: performing a diagnostic analysis of the condition for the user based on the diseased population: the method comprises the steps of obtaining historical physical examination data of a user, marking time marks, in which abnormal indexes exceed a normal range for the first time, in the historical physical examination data as first time, marking time difference values of current system time and the first time as analysis duration, and then carrying out matching analysis on diseased groups to obtain a diagnosis object.
When the intelligent triage system for health screening based on cloud edge cooperation works, indexes exceeding a normal range in physical examination data of a user are marked as abnormal indexes, an abnormal set is formed by all abnormal indexes of the user, an observation set of all disease types is obtained through a storage module, the abnormal set of the user and the observation set are compared one by one, and matched disease types are marked through comparison results; the method comprises the steps that identity information of all patients matched with a disease is obtained through a storage module, the identity information of the patients comprises the gender and the age of the patients, an age range is formed by the maximum value and the minimum value of the ages of the patients, the age range is divided into a plurality of age ranges, and all the patients with the same gender as the users and the ages within the matched range form a disease group; the method comprises the steps of obtaining historical physical examination data of a user, marking time marks, in which abnormal indexes exceed a normal range for the first time, in the historical physical examination data as first time, marking time difference values of current system time and the first time as analysis duration, and then carrying out matching analysis on diseased groups to obtain a diagnosis object.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula pp=α1×gy/yc+α2×gy/BD; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding matching coefficient for each group of sample data; substituting the set matching coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1 and alpha 2 which are respectively 3.25 and 2.86;
The size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding matching coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the matching coefficient is proportional to the value of the outlier.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. The intelligent triage system for health screening based on cloud edge cooperation is characterized by comprising an intelligent triage platform, wherein the intelligent triage platform is in communication connection with a user management module, a group analysis module, a diagnosis analysis module and a storage module;
The user management module is used for performing management analysis on physical examination data of a user, obtaining matched disease types of the user, sending the matched disease types to the intelligent triage platform, and sending the matched disease types to the group analysis module after the intelligent triage platform receives the matched disease types;
The group analysis module is used for carrying out characteristic analysis according to the diseased crowd matched with the disease species and obtaining the diseased crowd, sending the diseased crowd to the intelligent triage platform, and sending the diseased crowd to the diagnosis analysis module after the intelligent triage platform receives the diseased crowd;
The diagnostic analysis module is used for carrying out disease diagnostic analysis on the users according to the disease population: acquiring historical physical examination data of a user, marking the time mark of the historical physical examination data, in which the abnormal index exceeds the normal range for the first time, as the first time, marking the time difference between the current system time and the first time as analysis duration, then carrying out matching analysis on a diseased group and obtaining a diagnosis object, acquiring treatment data of the diagnosis object, and sending the treatment data of the diagnosis object to a mobile phone terminal of the user through an intelligent diagnosis platform, wherein the treatment data of the diagnosis object comprises a treatment scheme, a review period and notes;
the acquisition process of the matched disease seeds of the user comprises the following steps: marking indexes exceeding a normal range in physical examination data of a user as abnormal indexes, forming an abnormal set by all the abnormal indexes of the user, acquiring an observation set of all disease types through a storage module, wherein the observation set of the disease types comprises a plurality of observation indexes, and comparing the abnormal set of the user with the observation set one by one;
The specific process for comparing the abnormal set of the user with the observation set one by one comprises the following steps: selecting one observation set and marking the observation set as a comparison set, marking the number of elements in the abnormal set and the comparison set as an abnormal value YC and a comparison value BD respectively, and marking the number of common elements in the abnormal set and the comparison set as a common value GY; the matching coefficient PP of the comparison set is obtained by carrying out numerical calculation on the abnormal value YC, the comparison value BD and the common value GY; then selecting the next observation set as a comparison set and re-calculating the matching coefficient PP until all the observation sets are used as the comparison set to finish the calculation of the matching coefficient PP, marking the disease corresponding to the observation set with the largest value of the matching coefficient PP as a matching disease, and marking the observation index of the matching disease as a matching index;
The acquisition process of the diseased population comprises the following steps: the method comprises the steps that identity information of all patients matched with a disease is obtained through a storage module, the identity information of the patients comprises the gender and the age of the patients, an age range is formed by the maximum value and the minimum value of the ages of the patients, the age range is divided into a plurality of age ranges, the age ranges matched with the ages of the users are marked as matching ranges, and all the patients with the same gender as the users and the ages within the matching ranges form a disease group;
The specific process of matching analysis of diseased populations includes: randomly selecting one patient in a diseased group and marking the patient as an analysis object, taking the time mark of the analysis object history physical examination data, in which the abnormal index exceeds the normal range for the first time, as analysis time, establishing a plurality of index-time coordinate systems for the analysis object, wherein the index-time coordinate systems are in one-to-one correspondence with the matching index, drawing a graph of the matching index corresponding to the patient and the user in the index-time coordinate systems and marking the graph as a patient curve and a user curve respectively, making an intercepting straight line perpendicular to the X axis in the index-time coordinate systems, intercepting the abscissa of the intersection of the straight line and the X axis as A1, taking the time difference between the A1 and the analysis time as analysis time, obtaining the closed data FB and the uniform data JY of the analysis object, and performing numerical calculation to obtain the diagnosis coefficient ZD of the analysis object; randomly selecting the next patient in the diseased group and marking the next patient as an analysis object, and recalculating the diagnosis coefficient ZD of the analysis object until all the patients are marked as the analysis object and completing calculation of the diagnosis coefficient ZD; marking the analysis object as a matching object or an irrelevant object through the numerical value of the diagnosis coefficient ZD; screening the matched object with the best treatment effect according to the change trend of the matched index of the matched object and marking the matched object as a diagnosis object;
The process for acquiring the closed data FB and the uniform data JY of the analysis object comprises the following steps: a U-shaped area is formed by a Y axis, an X axis and a cut straight line, the sum of the area values of all the closed areas formed between the patient curve and the user curve in the U-shaped area is marked as a closed value, the closed values corresponding to all the matched indexes of the analysis objects are summed and averaged to obtain closed data FB, and variance calculation is carried out on the closed values corresponding to all the matched indexes of all the analysis objects to obtain uniform data JY.
2. The cloud-based collaborative health screening intelligent triage system according to claim 1, wherein the specific process of marking an analysis object as a matching object or an unrelated object comprises: the diagnostic threshold ZDmax is obtained by the storage module, and the diagnostic coefficients ZD of all the analysis objects are compared with the diagnostic threshold ZDmax one by one: if the diagnostic coefficient ZD is smaller than the diagnostic threshold ZDmax, marking the corresponding analysis object as a matching object; if the diagnostic coefficient ZD is equal to or greater than the diagnostic threshold ZDmax, the corresponding analysis object is marked as an irrelevant object.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20210061066A (en) * | 2019-11-19 | 2021-05-27 | 서울대학교산학협력단 | Method and apparatus for predicting prognosis of epithelial ovarian cancer |
| CN114072088A (en) * | 2019-05-09 | 2022-02-18 | 物化股份有限公司 | Surgical planning system with automated defect quantification |
| CN116313078A (en) * | 2023-02-21 | 2023-06-23 | 华中科技大学同济医学院附属同济医院 | Data processing method for physical examination report |
| CN116486982A (en) * | 2023-03-17 | 2023-07-25 | 浙江云圳技术有限公司 | Auxiliary generation method and device for AI doctor advice |
| CN116884605A (en) * | 2023-08-24 | 2023-10-13 | 上海罗根医疗科技有限公司 | Intelligent management system for operation anesthesia information |
| CN117373624A (en) * | 2023-09-13 | 2024-01-09 | 杭州莱文科技有限公司 | Intelligent medical insurance fee control method and system |
-
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114072088A (en) * | 2019-05-09 | 2022-02-18 | 物化股份有限公司 | Surgical planning system with automated defect quantification |
| KR20210061066A (en) * | 2019-11-19 | 2021-05-27 | 서울대학교산학협력단 | Method and apparatus for predicting prognosis of epithelial ovarian cancer |
| CN116313078A (en) * | 2023-02-21 | 2023-06-23 | 华中科技大学同济医学院附属同济医院 | Data processing method for physical examination report |
| CN116486982A (en) * | 2023-03-17 | 2023-07-25 | 浙江云圳技术有限公司 | Auxiliary generation method and device for AI doctor advice |
| CN116884605A (en) * | 2023-08-24 | 2023-10-13 | 上海罗根医疗科技有限公司 | Intelligent management system for operation anesthesia information |
| CN117373624A (en) * | 2023-09-13 | 2024-01-09 | 杭州莱文科技有限公司 | Intelligent medical insurance fee control method and system |
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