Melanoma diagnosis database interaction method and system

Abstract

The invention provides a melanoma diagnosis database interaction method and system, and relates to the technical field of database interaction.The method comprises the steps that historical diagnosis logic of a user during melanoma diagnosis is non-sensitively analyzed; wherein the historical diagnosis logic comprises a plurality of steady-state logics and a unique transient-state logic in sequence; based on each steady state logic, matching diagnosis auxiliary contents from a melanoma diagnosis database; and when the interference degree that the diagnosis auxiliary content dynamically reflected by the unique transient logic currently assists the user to interfere with continuous diagnosis of the user is lower than an interference degree threshold value, the user is assisted based on the diagnosis auxiliary content. A doctor does not need to retrieve information from a database by himself, the time cost of the diagnosis process is reduced, independent judgment of the doctor cannot be interfered, the diagnosis depth and efficiency of the doctor are improved, the doctor cannot fully concentrate in the diagnosis process, and cognitive fatigue is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of database interaction, in particular to a melanoma diagnosis database interaction method and system. BACKGROUND

[0002] At present, with the increasing complexity of clinical diagnosis of melanoma, especially due to its high mutual prime, doctors often need to face a large amount of reference materials and historical cases in the diagnosis process. Under this background, constructing a melanoma diagnosis database to collect relevant diagnosis cases, scientific literature, expert opinions and other content has become an effective means to improve the efficiency and accuracy of diagnosis.

[0003] However, the traditional melanoma diagnosis database interaction method has certain limitations: on the one hand, the huge database content requires doctors to spend a lot of time reviewing relevant materials, increasing the time cost of the diagnosis process; on the other hand, frequent queries and excessive reliance on the database may interfere with the doctor's independent judgment, reduce the depth and efficiency of diagnosis, and even may cause the doctor to be unable to fully concentrate during the diagnosis process, resulting in cognitive fatigue.

[0004] Therefore, an effective solution is needed. SUMMARY

[0005] One of the purposes of the present application is to provide a melanoma diagnosis database interaction method to solve the problems in the background art.

[0006] The melanoma diagnosis database interaction method provided by the embodiments of the present application comprises:

[0007] The history diagnosis logic of the user when diagnosing melanoma is analyzed without induction; wherein the history diagnosis logic contains a plurality of steady-state logic and a unique transient logic in turn;

[0008] Based on each steady-state logic, the diagnosis auxiliary content is matched from the melanoma diagnosis database;

[0009] When the unique transient logic continues to change dynamically and reflects that the interference degree of the diagnosis auxiliary content currently assisting the user to interfere with the user to continue the diagnosis is lower than the interference degree threshold, the user is assisted based on the diagnosis auxiliary content.

[0010] Optionally, the history diagnosis logic of the user when diagnosing melanoma is analyzed without induction, comprising:

[0011] The history diagnosis logic expressed by the multi-modal history behavior generated by the user when diagnosing melanoma is analyzed.

[0012] Optionally, the diagnosis auxiliary content is matched from the melanoma diagnosis database based on each steady-state logic, comprising:

[0013] constructing a diagnosis auxiliary situation by taking a plurality of logical features of each steady-state logic and a plurality of logical association features between two steady-state logics as components of the situation;

[0014] matching diagnosis auxiliary content corresponding to the diagnosis auxiliary situation from a melanoma diagnosis database.

[0015] Optionally, the step of obtaining the interference degree of the diagnosis auxiliary content currently assisting the user from interfering with the user's continuous diagnosis as the unique transient logic continues to dynamically change includes:

[0016] whenever the unique transient logic continues to dynamically change to a new transient logic, analyzing the new transient logic reflecting the immersion degree of the user's continuous diagnosis, the possibility of the user's continuous diagnosis being able to autonomously decide the diagnosis auxiliary content, and the undesirability degree of the user's undesired assistance;

[0017] based on the weights set in advance according to the influence degree of the immersion degree, the possibility and the undesirability on the interference degree, the immersion degree, the possibility and the undesirability are weighted calculated to obtain the interference degree.

[0018] Optionally, when assisting the user based on the diagnosis auxiliary content, if the user's operation of the diagnosis auxiliary content reflects that the user has generated a distrust factor for the diagnosis auxiliary content, the user is assisted to eliminate the distrust factor.

[0019] Optionally, the assisting the user to eliminate the distrust factor includes:

[0020] analyzing the ability degree of each material of the diagnosis auxiliary content reflected by each steady-state logic and the personal portrait of the user to eliminate the distrust factor and at least one ability gap;

[0021] when the number of materials whose ability degree exceeds the ability degree threshold exceeds the number threshold, the materials whose ability degree exceeds the ability degree threshold are sequentially output to the user in order of the ability degree from small to large; otherwise, each material is sequentially traversed;

[0022] each time the material is traversed, at least one ability gap of the traversed material is analyzed to obtain a plurality of compensation opportunities and their respective compensation strategies;

[0023] based on the plurality of compensation opportunities and their respective compensation strategies, a compensation mechanism for the ability gap of the traversed material is planned;

[0024] the output priority value of the traversed material is obtained by weighted calculating the concentration degree of each compensation opportunity in the output process of the traversed material, the total number of the compensation opportunities, and the ability degree of the traversed material.

[0025] After traversing each material in turn, each material is output to the user in turn according to the output priority value from large to small, and the ability gap compensation mechanism of the output material is executed each time.

[0026] Optionally, the at least one ability gap of the traversed material that can be compensated by other materials and the respective compensation strategies of the plurality of compensation opportunities include:

[0027] Based on the compensation content requirement of the at least one ability gap of the traversed material, the corresponding compensation content is determined from other materials;

[0028] Any ability gap that occurs when the traversed material is output to the user is taken as a plurality of compensation opportunities, and the use of compensation content to compensate for the corresponding ability gap when entering each compensation opportunity is taken as the compensation strategy of each compensation opportunity.

[0029] Optionally, the intensity of each compensation opportunity in the output process of the traversed material, the total number of each compensation opportunity, and the ability degree of the traversed material are weighted and calculated, including:

[0030] Based on the weights set in advance according to the influence degree of the intensity, the total number of each compensation opportunity, and the ability degree on the output priority value, the intensity, the total number of each compensation opportunity, and the ability degree of the traversed material are weighted and calculated to obtain the output priority value.

[0031] Optionally, the ability gap compensation mechanism of the traversed material is planned, including:

[0032] When any compensation opportunity is entered when the traversed material is output to the user, the corresponding compensation strategy is executed.

[0033] The melanoma diagnosis database interaction system provided by the embodiment of the application comprises:

[0034] The analysis module is configured to analyze historical diagnosis logic of the user when the user performs melanoma diagnosis without interest; wherein the historical diagnosis logic comprises a plurality of steady-state logics in turn and a unique transient logic.

[0035] The matching module is configured to match diagnosis auxiliary content from the melanoma diagnosis database based on each steady-state logic.

[0036] The interaction module is configured to assist the user based on the diagnosis auxiliary content when the unique transient logic continues to dynamically change and reflects that the interference degree of the diagnosis auxiliary content currently assisting the user interferes with the user to continue diagnosis is lower than an interference degree threshold.

[0037] The application has the following beneficial effects:

[0038] The application analyzes the historical diagnosis logic of the user when diagnosing melanoma without inductance, avoids interfering with the doctor, matches the diagnosis auxiliary content from the melanoma diagnosis database based on each steady-state logic in the historical diagnosis logic, realizes the matching of suitable diagnosis auxiliary content for the logic of the user diagnosing melanoma, and when the interference degree of the diagnosis auxiliary content currently assisting the user in continuing to diagnose is lower than the interference degree threshold in the unique transient logic that continues to dynamically change, the user is further assisted based on the diagnosis auxiliary content, the interference with the doctor is further avoided, the doctor does not need to manually retrieve information from the database, the time cost of the diagnosis process is reduced, the independent judgment of the doctor is not interfered, the diagnosis depth and efficiency are improved, the doctor cannot fully concentrate during the diagnosis process, and cognitive fatigue is avoided.

[0039] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings.

[0040] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation of the present application. In the drawings:

[0042] Figure 1 A flow chart of a melanoma diagnosis database interaction method in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a melanoma diagnosis database interaction system in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The preferred embodiments of the present application will be described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to explain and illustrate the present application, and do not constitute a limitation of the present application.

[0045] Example 1:

[0046] The embodiment of the present application provides a melanoma diagnosis database interaction method, as shown in Figure 1 The method comprises the following steps:

[0047] S1, analyzing the historical diagnosis logic of the user when diagnosing melanoma without inductance; wherein the historical diagnosis logic comprises a plurality of steady-state logics in sequence and a unique transient logic;

[0048] S2, matching the diagnosis auxiliary content from the melanoma diagnosis database based on each steady-state logic;

[0049] S3, when the unique transient logic continues to dynamically change and reflects that the interference degree of the current diagnosis auxiliary content assisting the user to continue the diagnosis is lower than the interference degree threshold, assisting the user based on the diagnosis auxiliary content again.

[0050] In the embodiment of the present application, the user is a doctor diagnosing melanoma; the non-sensory analysis refers to making the user not perceive that the system is analyzing him / her; the historical diagnosis logic is the logic generated when the user historically diagnosed melanoma, which includes the first N-1 steady-state logic (N is the total number of steady-state logic and transient logic) and the last unique transient logic; the steady-state logic refers to the stable diagnosis logic embodied by the diagnosis process that the user has decided and will not change; on the contrary, the transient logic refers to the temporary diagnosis logic embodied by the diagnosis process that the user has not decided and will change at any time, which is dynamically changed and generally arranged after each steady-state logic to determine the relevant diagnosis process that the user is currently performing.

[0051] Since the steady-state logic has been stabilized, the diagnosis auxiliary content can be matched from the melanoma diagnosis database based on the steady-state logic.

[0052] However, at this time, if the user is directly assisted based on the matched diagnosis auxiliary content, it may interfere with the user to continue the diagnosis. Therefore, when the unique transient logic continues to dynamically change and reflects that the interference degree of the current diagnosis auxiliary content assisting the user to continue the diagnosis is lower than the interference degree threshold, it is basically not interfered with the user to continue the diagnosis, at this time, the user is assisted based on the diagnosis auxiliary content again; the interference degree threshold is a threshold representing a smaller interference degree which is set in advance.

[0053] The embodiment of the present application analyzes the historical diagnosis logic of the user when diagnosing melanoma without sensing, avoids interfering with the doctor, matches the diagnosis auxiliary content based on each steady-state logic in the historical diagnosis logic from the melanoma diagnosis database, realizes the matching of appropriate diagnosis auxiliary content for the logic of the user diagnosing melanoma, and further avoids interfering with the doctor when the unique transient logic continues to dynamically change and reflects that the interference degree of the current diagnosis auxiliary content assisting the user to continue the diagnosis is lower than the interference degree threshold, assists the user based on the diagnosis auxiliary content again, reduces the time cost of the diagnosis process without the doctor having to retrieve information from the database, also does not interfere with the independent judgment of the doctor, improves the diagnosis depth and efficiency, makes the doctor unable to fully concentrate during the diagnosis process, and avoids cognitive fatigue.

[0054] Embodiment 2:

[0055] In the embodiment of the present application, the non-sensory analysis of the historical diagnosis logic of the user when diagnosing melanoma includes:

[0056] analyze the historical diagnosis logic expressed by the multi-modal historical behavior generated by the user when diagnosing the melanoma.

[0057] In the embodiment of the present application, the multi-modal historical behavior at least includes: conversation records between the user and the patient, operations (such as input patient condition description, decided diagnosis process, etc.) generated by the user on the terminal (such as computer, etc.); through semantic analysis technology and behavior analysis technology, the historical diagnosis logic expressed by the multi-modal historical behavior can be analyzed; since the whole process does not involve any content output, the user will not perceive it, and the analysis is realized without feeling.

[0058] Embodiment 3:

[0059] In the embodiment of the present application, the matching of the diagnosis auxiliary content from the melanoma diagnosis database based on each steady-state logic includes:

[0060] The plurality of logic features of each steady-state logic and the plurality of logic association features between two steady-state logics are taken as the composition of the situation, and the diagnosis auxiliary situation is constructed;

[0061] The diagnosis auxiliary content corresponding to the diagnosis auxiliary situation is matched from the melanoma diagnosis database.

[0062] In the embodiment of the present application, the logic features at least include: logic type, logic generation time, etc.; the logic association features at least include: logic sequence, cause-effect relationship, progressive relationship, etc.; the plurality of logic features of each steady-state logic and the plurality of logic association features between two steady-state logics are taken as the composition of the situation, and the diagnosis auxiliary situation is constructed; the diagnosis auxiliary content suitable for assisting the user in diagnosing the melanoma under different diagnosis auxiliary situations is pre-set in the melanoma diagnosis database, and the diagnosis auxiliary content corresponding to the constructed diagnosis auxiliary situation is directly matched from the library; the diagnosis auxiliary content can be related diagnosis cases, scientific literature and expert opinions, etc.

[0063] Embodiment 4:

[0064] In the embodiment of the present application, the acquisition step of the unique transient logic which continues to dynamically change to reflect the interference degree of the diagnosis auxiliary content currently assisting the user to interfere with the user to continue diagnosing includes:

[0065] Whenever the unique transient logic continues to dynamically change to a new transient logic, the new transient logic reflects the immersion degree of the user to continue diagnosing, the possibility of the user to continue diagnosing to autonomously decide the diagnosis auxiliary content, and the undesired degree of the user's undesired assistance, respectively;

[0066] The immersion degree, the possibility degree and the undesired degree are weighted and calculated based on weights set in advance according to influence degrees of the immersion degree, the possibility degree and the undesired degree on the interference degree respectively, so as to obtain the interference degree.

[0067] In the embodiment of the application, the only transient logic is dynamically changed, and each new transient logic changed to reflects the current diagnosis thought state of the user in melanoma diagnosis; specifically, the new transient logic can reflect the immersion degree of the user in continuing diagnosis (the immersion degree is positively correlated with the interval time length from the last dynamic change of the new transient logic, the longer the interval time length, the longer time the user takes to generate the new dynamic change of the new transient logic, and the greater the degree of immersion of the user in continuing diagnosis; specifically, when quantifying the immersion degree, a quantification rule can be set in advance to quantify the immersion degree according to the interval time length), can reflect the possibility degree of the user in continuing diagnosis to autonomously decide the diagnosis auxiliary content (the possibility degree is positively correlated with the similarity between the new transient logic and the diagnosis logic embodied by the diagnosis auxiliary content, the greater the similarity, the closer the current diagnosis thought state of the user to the diagnosis auxiliary content, and the greater the possibility degree; similarly, when quantifying the possibility degree, a quantification rule can be set in advance to quantify the possibility degree according to the similarity), and can reflect the undesired degree of the user in undesired assistance (the undesired degree is positively correlated with the completeness of the new transient logic, and the completeness refers to the ratio of the number of elements currently contained in the new transient logic to the total number of elements that a single complete diagnosis logic should contain, the greater the completeness, the closer the user to the complete diagnosis logic, and the greater the undesired degree).

[0068] After the immersion degree, the possibility degree and the undesired degree are analyzed, the three are positively correlated with the interference degree, the greater the immersion degree, the greater the interference to the assistance at this time, the greater the possibility degree, the more likely the user to independently obtain the diagnosis auxiliary content, the greater the interference to the assistance at this time, the greater the undesired degree, and the greater the interference to the assistance at this time; the interference degree is weighted and calculated based on weights set in advance according to influence degrees of the three on the interference degree respectively, so as to obtain the interference degree; specifically, the formula of the weighted calculation is G=k1·c+k2·p+k3·f, wherein G is the interference degree, c is the immersion degree, p is the possibility degree, f is the undesired degree, k1 is the weight of the immersion degree, k2 is the weight of the possibility degree, and k3 is the weight of the undesired degree.

[0069] The embodiment of the present application reflects three dimensions of the immersion degree of the user continuing diagnosis, the possibility of the user continuing diagnosis being able to autonomously decide the diagnosis auxiliary content and the undesirability degree of the user's undesired assistance, respectively, in combination with the new transient logic, comprehensively analyzes the interference degree of the diagnosis auxiliary content currently assisting the user interfering with the user continuing diagnosis due to the dynamic change of the unique transient logic, greatly improves the accuracy, comprehensiveness and efficiency of the interference degree analysis, and greatly improves the accuracy of the system based on the diagnosis auxiliary content in assisting the user to avoid interference on the user.

[0070] Embodiment 5:

[0071] In real applications, when receiving assistance of the diagnosis auxiliary content, the doctor will have untrusted factors for the diagnosis auxiliary content due to the responsibility for the patient, the diagnosis responsibility attribution and the professional judgment, for example: suspecting the accuracy, comprehensiveness and scientificity of the diagnosis auxiliary content, worrying about potential errors or biases. If the doctor cannot eliminate the untrusted factors, the effect of using the diagnosis auxiliary content to assist the doctor will be greatly reduced, and the overall applicability of the system will be affected.

[0072] Therefore, in the embodiment of the present application, when assisting the user based on the diagnosis auxiliary content, if the user's operation of the diagnosis auxiliary content reflects that the user has untrusted factors for the diagnosis auxiliary content, the user is assisted to eliminate the untrusted factors.

[0073] In the embodiment of the present application, the user's operation of the diagnosis auxiliary content includes at least: operation viewing time, adjustment and modification operation, different content area viewing time and repeated switching viewing operation; based on the behavior analysis technology, the untrusted factors of the user for the diagnosis auxiliary content can be determined by analyzing these behaviors, for example: the user's operation viewing time is only 5 seconds, which means that the user directly considers that the diagnosis auxiliary content is not trustworthy and does not need to be viewed; for example: the user frequently adjusts and modifies some content, which means that the user has untrusted factors for this content.

[0074] When it is determined that the user has untrusted factors, the user is assisted to eliminate the untrusted factors, which helps the user to quickly eliminate the untrusted factors.

[0075] The embodiment of the present application reflects three dimensions of the immersion degree of the user continuing diagnosis, the possibility of the user continuing diagnosis being able to autonomously decide the diagnosis auxiliary content and the undesirability degree of the user's undesired assistance, respectively, in combination with the new transient logic, comprehensively analyzes the interference degree of the diagnosis auxiliary content currently assisting the user interfering with the user continuing diagnosis due to the dynamic change of the unique transient logic, greatly improves the accuracy, comprehensiveness and efficiency of the interference degree analysis, and greatly improves the accuracy of the system based on the diagnosis auxiliary content in assisting the user to avoid interference on the user.

[0076] Embodiment 6:

[0077] For the diagnostic assistance content, a plurality of materials for helping the user eliminate the distrust factor can be prepared, such as: feedback of doctors who have received help from the diagnostic assistance content in history, results of the latest clinical trial research of the diagnostic assistance content, experience degree and certification information of experts publishing the diagnostic assistance content, and the like. However, since the actual diagnosis and personal conditions of different doctors are different, the abilities of different materials to eliminate the distrust factor of the doctors are different, and if the user is only assisted by sequentially outputting these materials to eliminate the distrust factor, the effect of helping the user to eliminate the distrust factor will be poor, and the diagnosis time of the user will be more likely to be delayed. Therefore, how to use these materials to most optimally, most comprehensively and most quickly help the user to eliminate the distrust factor has become a problem to be solved.

[0078] Therefore, in the embodiment of the present application, the assisting the user to eliminate the distrust factor includes:

[0079] analyzing the ability degree of each material of the diagnostic assistance content reflected by the steady-state logic and the personal portrait of the user to eliminate the distrust factor and at least one ability gap;

[0080] When the number of materials whose ability degree exceeds the ability degree threshold value exceeds the number threshold value, the materials whose ability degree exceeds the ability degree threshold value are sequentially output to the user in ascending order of the ability degree; otherwise, each material is sequentially traversed;

[0081] At each traversal, analyzing a plurality of compensation opportunities and their respective compensation strategies that can compensate for the at least one ability gap of the traversed material by other materials;

[0082] Based on the plurality of compensation opportunities and their respective compensation strategies, a compensation mechanism for the ability gap of the traversed material is planned;

[0083] The output priority value of the traversed material is obtained by weighted calculation of the density of each compensation opportunity in the output process of the traversed material, the total number of the compensation opportunities, and the ability degree of the traversed material;

[0084] After sequentially traversing each material, each material is sequentially output to the user in descending order of the output priority value, and at each output, the compensation mechanism of the output material is executed.

[0085] In the embodiment of the present application, each steady-state logic represents the actual diagnosis situation of the user, the personal profile (such as the employed hospital, the history of participating in academic research, etc.) represents the personal situation of the user, and both of them can reflect the ability degree of each material of the diagnosis auxiliary content to eliminate the distrust factor and at least one ability gap. The ability degree represents the degree of the ability of the material to eliminate the distrust factor, and the ability gap refers to the ability that cannot be achieved by using the material alone to eliminate the distrust factor. Specifically, for example: the actual diagnosis situation of the user represented by each steady-state logic is that the communication with the patient is in the history stage, the personal situation of the user represented by the personal profile is that the user is employed in hospital A and has historically participated in academic research project B, and the diagnosis auxiliary content is a historical treatment case. The material is the history proof of the patient in the case, and the doctor in charge of the case is also employed in hospital A. The history proof in the material can be used by the user to combine the history communicated to perform trust evaluation, the doctor in charge of the historical treatment case in the diagnosis auxiliary content is in the same hospital as the user, and the user has a higher trust degree. Two of the three items in the actual diagnosis situation of the user and the personal situation of the user are covered by the correlation, and the ability degree of the material is set to 2 / 3, and the ability gap is that the historical participation in academic research project B in the personal situation of the user cannot be covered by the correlation.

[0086] The ability degree threshold is a preset threshold representing a larger degree of ability to eliminate the distrust factor, and the number threshold is a preset threshold representing a larger number of materials.

[0087] When the number of materials whose ability degree exceeds the ability degree threshold exceeds the number threshold, it means that the number of materials with a larger degree of ability to eliminate the distrust factor is larger, and their joint help to eliminate the distrust factor of the user is certainly sufficient. Therefore, the materials whose ability degree exceeds the ability degree threshold are output to the user in order of the ability degree from small to large. It should be noted that the materials are not output to the user in order of the ability degree from large to small, but a special effect can be achieved: if the user cannot achieve his expectation after viewing the first material, he will have expectations for the next material. If the materials are output to the user in order of the ability degree from large to small, the expectations cannot be positively fed back. Therefore, the embodiment of the present application can leave room for distrust elimination assistance and avoid this situation. In addition, when the user views the materials with larger and larger ability degrees, he will think more and more that the expectation will be achieved. This kind of positive feedback with a gap each time greatly improves the effect of helping to eliminate the distrust factor.

[0088] When the number of materials whose capability degree exceeds the capability degree threshold value does not exceed the number threshold value, it indicates that the number of materials with a larger capability degree that can eliminate the distrust factor is smaller, and all materials need to jointly help the user eliminate the distrust factor to have an effect; at this time, each material is traversed in turn, and each time the traversal is performed, multiple compensation opportunities and their respective compensation strategies for which at least one capability gap of the traversed material can be compensated for by other materials are analyzed, the compensation opportunity refers to the opportunity to compensate for the capability gap when the traversed material is output to the user, and the corresponding compensation strategy is the execution strategy for corresponding compensation. Specifically, for example: the capability gap is the inability to cover the historical participation in the B academic research project in the user's personal situation, the compensation opportunity is the time when the user starts to view other materials related to the capability gap, such as the time when the user starts to view the material of the responsible doctor of the historical treatment case in the diagnostic auxiliary content and the same hospital (i.e., the time when the system starts to output the time), and the corresponding compensation strategy is to output to the user the content in other materials that can prove the relationship between the historical treatment case in the diagnostic auxiliary content and the B academic research project.

[0089] The material has a fixed pre-set content output duration, which is determined by the size of the content, and the above describes the compensation opportunity as an output time, then the density of each compensation opportunity in the output process of the traversed material refers to the density of the output time of each compensation opportunity in the time period of the fixed content output duration of the traversed material, which can be calculated as the inverse of the average of the time interval between each two time intervals (the smaller the average time interval, the more dense, and the larger the inverse).

[0090] The density of each compensation opportunity in the output process of the traversed material, the total number of each compensation opportunity, and the capability degree of the traversed material are positively correlated with the degree of output priority of the traversed material; the greater the density, the more intensive the user receives the content of other materials in a short period of time because of the execution of the compensation strategy corresponding to the capability gap, which will give the user a hint that there is enough evidence to make him trust the diagnostic auxiliary content, thereby improving the auxiliary effect, and thus the need for priority output; the more the total number of compensation opportunities, the more opportunities and times the output material is compensated for by other materials, and thus the need for priority output; the greater the capability degree of the traversed material, the greater the capability of the output material to help the user eliminate the distrust factor, and thus the need for priority output.

[0091] Finally, after the traversal of each material in turn is completed, each material is output to the user in turn according to the output priority value from large to small, and each time the output is performed, the capability gap compensation mechanism of the output material is executed.

[0092] The embodiment of the present application realizes the optimization, the most comprehensive and the fastest help of the user to eliminate the distrust factors by using the diagnostic auxiliary content prepared multiple materials, can adapt to the actual diagnosis of different doctors and their personal situation, and provides the best auxiliary for the doctor to eliminate the distrust factors, avoids the delay of the doctor's diagnosis time, and greatly improves the applicability of the system.

[0093] Embodiment 7:

[0094] In the embodiment of the present application, the at least one capability gap of the material traversed by the analysis can be filled by multiple filling opportunities and their respective filling strategies, including:

[0095] Based on the filling content requirement of the at least one capability gap of the traversed material, the corresponding filling content is determined from other materials;

[0096] Any capability gap that occurs when the traversed material is output to the user is regarded as a plurality of filling opportunities, and the use of filling content to fill the corresponding capability gap when entering each filling opportunity is regarded as the filling strategy of each filling opportunity.

[0097] Embodiment 8:

[0098] In the embodiment of the present application, the intensity of each filling opportunity in the output process of the traversed material, the total number of each filling opportunity and the capability degree of the traversed material are weighted and calculated, including:

[0099] Based on the weights set in advance according to the influence degree of the intensity, the total number of each filling opportunity and the capability degree on the output priority value, the intensity, the total number of each filling opportunity and the capability degree of the traversed material are weighted and calculated to obtain the output priority value.

[0100] In the embodiment of the present application, the formula for weighting and calculating the intensity, the total number of each filling opportunity and the capability degree of the traversed material is: N=d1·m+d2·s+d3·a, wherein N is the output priority value, m is the intensity, s is the total number of each filling opportunity, a is the capability degree of the traversed material, d1 is the weight of the intensity, d2 is the weight of the total number of each filling opportunity, and d3 is the weight of the capability degree of the traversed material.

[0101] Embodiment 9:

[0102] In the embodiment of the present application, the capability gap filling mechanism of the traversed material is planned, including:

[0103] When the traversed material is output to the user, if any filling opportunity is entered, the corresponding filling strategy is executed.

[0104] In the embodiment of the present application, when the material being traversed is output to the user, if any remediation opportunity is entered, the mechanism of executing the corresponding remediation strategy is used as the ability gap remediation mechanism of the material being traversed, so that when the subsequent material being traversed is truly output to the user, if any remediation opportunity is entered, the corresponding remediation strategy can be executed immediately.

[0105] Embodiment 10:

[0106] The embodiment of the present application provides a melanoma diagnosis database interaction system, as shown in the figure, which comprises: Figure 2

[0107] The analysis module 1 is used for analyzing the historical diagnosis logic of the user in the melanoma diagnosis without inductance; wherein the historical diagnosis logic comprises a plurality of steady-state logics and a unique transient logic in sequence;

[0108] The matching module 2 is used for matching diagnosis auxiliary content from the melanoma diagnosis database based on each steady-state logic;

[0109] The interaction module 3 is used for assisting the user based on the diagnosis auxiliary content when the unique transient logic continues to dynamically change and reflects that the interference degree of the diagnosis auxiliary content currently assisting the user to interfere with the user to continue the diagnosis is lower than the interference degree threshold.

[0110] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.​

Claims

1. A melanoma diagnosis database interaction method, characterized by, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

2. The melanoma diagnostic database interaction method according to claim 1, wherein, The method comprises the following steps: The method comprises the following steps:

3. The melanoma diagnostic database interaction method of claim 1, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

4. The melanoma diagnostic database interaction method of claim 1, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

5. The melanoma diagnostic database interaction method of claim 1, wherein, The method comprises the following steps:

6. The melanoma diagnostic database interaction method of claim 5, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

7. The melanoma diagnostic database interaction method according to claim 6, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the determine the corresponding remedial content from other materials based on the remedial content requirement of the at least one capability gap of the traversed material; any capability gap of the traversed material when output to the user as a plurality of remedial opportunities, and entering each remedial opportunity with remedial content to remedy the corresponding capability gap as the remedial strategy of each remedial opportunity.

8. The melanoma diagnostic database interaction method according to claim 6, wherein, The intensity of each remedial opportunity in the output process of the traversed material, the total number of each remedial opportunity, and the capability degree of the traversed material are weighted and calculated, including: Based on the weight set in advance according to the influence degree of the output priority value of the intensity, the total number of each remedial opportunity, and the capability degree respectively, the intensity, the total number of each remedial opportunity, and the capability degree of the traversed material are weighted and calculated to obtain the output priority value.

9. The melanoma diagnostic database interaction method of claim 6, wherein, The capability gap remediation mechanism of the traversed material in the plan includes: When any remedial opportunity is entered when the traversed material is output to the user, the corresponding remedial strategy is executed.

10. A melanoma diagnosis database interactive system, characterized by, Including: The analysis module is used for non-sensitization analysis of the historical diagnosis logic of the user in the diagnosis of melanoma; wherein the historical diagnosis logic contains a plurality of steady-state logic, a unique transient logic; The matching module is used for matching diagnosis auxiliary content from the melanoma diagnosis database based on each steady-state logic; The interaction module is used to assist the user based on the diagnosis auxiliary content when the unique transient logic continues to dynamically change and reflects that the interference degree of the diagnosis auxiliary content currently assisting the user interferes with the user to continue the diagnosis is lower than the interference degree threshold.

Images