A liver and gall treatment scheme screening system and method based on big data

Abstract

The present application relates to the technical field of hepatobiliary surgery treatment scheme screening, and discloses a hepatobiliary surgery treatment scheme screening system and method based on big data, which comprises: a processing module for obtaining safety evaluation value, effectiveness evaluation value and economy evaluation value of all reference hepatobiliary surgery treatment schemes of a current patient based on all treatment information of the reference hepatobiliary surgery treatment schemes; a construction module for obtaining a treatment scheme analysis matrix of the current patient based on the safety evaluation value, the effectiveness evaluation value and the economy evaluation value of all reference hepatobiliary surgery treatment schemes of the current patient; and a screening module for obtaining the best screening hepatobiliary surgery treatment scheme of the current patient based on the treatment scheme analysis matrix of the current patient. The present application comprehensively considers the safety, effectiveness and economy of the treatment scheme, realizes the accurate screening of the best hepatobiliary surgery treatment scheme of the patient, and improves the scientificity and efficiency of medical decision-making.

Description

Technical Field

[0001] This invention relates to the field of hepatobiliary surgical treatment plan screening technology, and in particular to a hepatobiliary surgical treatment plan screening system and method based on big data. Background Technology

[0002] Currently, there are numerous types of hepatobiliary diseases, including cholecystitis, gallstones, liver cancer, and cirrhosis, each with potentially different treatment plans. Furthermore, with continuous advancements in medical technology, new treatment methods and techniques are constantly emerging, making the selection of hepatobiliary treatment options more complex and diverse. Each patient's lesion nature, lesion diameter, length, and location vary, all influencing the choice of treatment plan. When selecting a hepatobiliary treatment plan, physicians need to comprehensively consider multiple factors, including the safety, effectiveness, and cost-effectiveness of the treatment. However, these factors are often difficult to quantify directly, and there may be trade-offs between different factors. For example, some treatment plans may have high effectiveness but poor safety or cost-effectiveness; while others may perform well in terms of safety and cost-effectiveness but have slightly lower effectiveness. Therefore, a technical method is needed that can comprehensively consider and quantify the safety, effectiveness, and cost-effectiveness of treatment plans. This method can help physicians more objectively and accurately assess the advantages and disadvantages of different treatment plans, thereby selecting the most suitable treatment plan for the patient. At the same time, this method can also improve the transparency and repeatability of treatment options, and reduce the subjectivity and uncertainty of doctors when choosing treatment options.

[0003] However, existing big data-based hepatobiliary surgery treatment plan screening systems and methods only achieve automatic identification and classification, reducing manual costs. They also use adversarial modeling in the identification module to transform the generation of the transfer dataset into a binary minimax game problem, thus more effectively obtaining the transfer dataset for training the liver tumor discrimination network and achieving better results on a larger training set. However, they do not consider comprehensive safety, effectiveness, and cost-effectiveness to screen the most suitable hepatobiliary surgery treatment plan for the current patient. For example, patent publication number "CN110070125A" and patent title "A Method and System for Screening Hepatobiliary Surgery Treatment Plans Based on Big Data Analysis" includes the following steps: a hepatobiliary image acquisition module, a patient condition input module, a central control module, an image classification module, an identification module, a big data processing module, a retrieval module, a matching module, a printing module, and a display module. The aforementioned patent achieves accurate classification results through its image classification module, reducing noise interference and enabling automatic identification and classification, thus lowering labor costs. Simultaneously, it transforms the generation of the transfer dataset into a binary minimax game problem through the recognition module's adversarial model technology, thereby more effectively obtaining the transfer dataset for training the liver tumor discriminator network and achieving better results on a larger training set. However, this patent only achieves automatic identification and classification, reducing labor costs, and transforms the generation of the transfer dataset into a binary minimax game problem through the recognition module's adversarial model technology, thus more effectively obtaining the transfer dataset for training the liver tumor discriminator network and achieving better results on a larger training set. It does not consider comprehensive safety, effectiveness, and cost-effectiveness to select the most suitable hepatobiliary surgical treatment plan for the current patient.

[0004] Therefore, this invention proposes a system and method for screening hepatobiliary surgical treatment plans based on big data. Summary of the Invention

[0005] This invention provides a big data-based system and method for screening hepatobiliary surgical treatment plans. Based on all treatment information of all reference hepatobiliary surgical treatment plans for a current patient, it obtains the safety, effectiveness, and cost-effectiveness assessment values ​​of all reference hepatobiliary surgical treatment plans for that patient. This allows for the separate quantification of the treatment safety, effectiveness, and cost-effectiveness of each reference hepatobiliary surgical treatment plan for the current patient. Furthermore, based on these assessment values, it obtains all plotted coordinate points for each reference hepatobiliary surgical treatment plan for the current patient, facilitating subsequent analysis of the patient's treatment plan. The analysis matrix is ​​constructed by plotting all coordinate points of all reference hepatobiliary surgical treatment plans for the current patient to obtain the current patient's treatment plan analysis matrix. Then, based on the current patient's treatment plan analysis matrix, the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient is obtained. This realizes the quantification of the priority of each reference hepatobiliary surgical treatment plan for the current patient as the best screening hepatobiliary surgical treatment plan. Finally, based on the screening priority value of all reference hepatobiliary surgical treatment plans for the current patient, the best screening hepatobiliary surgical treatment plan for the current patient is obtained. This achieves the comprehensive selection of the most suitable hepatobiliary surgical treatment plan for the current patient from the aspects of safety, effectiveness and economy.

[0006] This invention provides a big data-based system for screening hepatobiliary surgical treatment plans, comprising:

[0007] The acquisition module is used to obtain all reference patients for the current patient based on all class-based basic information and a preset database, and to obtain all reference hepatobiliary surgery treatment plans for the current patient based on all reference patients.

[0008] The processing module is used to obtain the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient based on all types of treatment information of all reference hepatobiliary surgical treatment plans for the current patient.

[0009] The module is used to obtain all plotted coordinate points for each reference hepatobiliary surgical treatment plan for the current patient based on the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient, and to obtain the treatment plan analysis matrix for the current patient based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient.

[0010] The screening module is used to obtain the screening priority value of each reference hepatobiliary surgery treatment plan for the current patient based on the current patient's treatment plan analysis matrix, and to obtain the optimal screening hepatobiliary surgery treatment plan for the current patient based on the screening priority values ​​of all reference hepatobiliary surgery treatment plans for the current patient.

[0011] Preferably, the big data-based hepatobiliary surgery treatment plan screening system includes an acquisition module comprising:

[0012] The reference patient determination submodule is used to obtain all reference patients for the current patient based on all class-based basic information of the current patient and a preset database;

[0013] The treatment plan acquisition submodule is used to extract the hepatobiliary surgery treatment plan for each reference patient from the preset database and use it as the reference hepatobiliary surgery treatment plan for the current patient.

[0014] The preferred method, a big data-based hepatobiliary surgery treatment plan screening system, includes a patient-specific submodule, comprising:

[0015] The basic information acquisition unit is used to acquire all types of basic information of the current patient, including the nature of the lesion, the diameter and length of the lesion, and the location of the lesion.

[0016] The reference patient determination unit is used to treat the corresponding patient as the reference patient of the current patient when all the basic information of each patient stored in the preset database is the same as all the basic information of the current patient.

[0017] The preferred big data-based hepatobiliary surgery treatment plan screening system includes a processing module comprising:

[0018] The preprocessing submodule is used to obtain all types of treatment information for all reference hepatobiliary surgical treatment plans for the current patient. All types of treatment information include safety treatment information, effectiveness treatment information, and cost-effectiveness treatment information. Safety treatment information includes postoperative recovery time, postoperative liver function assessment index values, and intraoperative blood loss. Effectiveness treatment information includes surgical resection rate and postoperative symptom improvement scores. Cost-effectiveness treatment information includes surgical costs, hospitalization time, and subsequent treatment costs.

[0019] The processing submodule is used to obtain the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient based on all types of treatment information of all reference hepatobiliary surgical treatment plans for the current patient.

[0020] The preferred big data-based hepatobiliary surgery treatment plan screening system includes a processing submodule comprising:

[0021] The first processing unit is used to obtain the safety assessment value of each reference hepatobiliary surgical treatment plan for the current patient based on the safety treatment information of all reference hepatobiliary surgical treatment plans for the current patient, namely:

[0022]

[0023] Where α is the safety assessment value of the current calculated reference hepatobiliary surgical treatment plan for the current patient, and A is the postoperative recovery time of the current calculated reference hepatobiliary surgical treatment plan for the current patient. max B represents the maximum postoperative recovery time among all reference hepatobiliary surgical treatment protocols for the current patient, and B represents the postoperative liver function assessment value of the current patient based on the calculated safety information of the reference hepatobiliary surgical treatment protocols. max C represents the maximum value of postoperative liver function assessment indicators among all reference hepatobiliary surgical treatment protocols for the current patient, and C represents the intraoperative blood loss based on the current calculated reference hepatobiliary surgical treatment protocol for the current patient. max The maximum intraoperative blood loss among all reference hepatobiliary surgical treatment protocols for the current patient is given, where ln is the natural logarithm and the natural constant e is 2.718.

[0024] The second processing unit is used to obtain the effectiveness assessment value of each reference hepatobiliary surgical treatment plan for the current patient based on the effectiveness treatment information of all reference hepatobiliary surgical treatment plans for the current patient, namely:

[0025]

[0026] Where β is the effectiveness assessment value of the current calculated reference hepatobiliary surgical treatment plan for the current patient, and D is the surgical resection rate of the current calculated reference hepatobiliary surgical treatment plan for the current patient. max E represents the maximum surgical resection rate among all reference hepatobiliary surgical treatment options for the current patient, and E is the postoperative symptom improvement score assigned to the current patient based on the effectiveness of the current reference hepatobiliary surgical treatment options. max Assign the maximum score among all reference hepatobiliary surgery treatment options for the current patient regarding postoperative symptom improvement.

[0027] The third processing unit is used to obtain the cost assessment value of each reference hepatobiliary surgery treatment plan for the current patient based on the cost-effectiveness information of all reference hepatobiliary surgery treatment plans for the current patient.

[0028] Preferably, in a big data-based hepatobiliary surgery treatment plan screening system, the third processing unit obtains an economic evaluation value for each reference hepatobiliary surgery treatment plan for the current patient based on the economic treatment information of all reference hepatobiliary surgery treatment plans for the current patient, including:

[0029]

[0030] Where γ represents the current calculated cost-effectiveness assessment of the reference hepatobiliary surgical treatment plan for the current patient, and F represents the surgical cost of the current calculated cost-effectiveness treatment information for the reference hepatobiliary surgical treatment plan for the current patient. max G represents the maximum surgical cost among all cost-effective treatment options for the current patient, and G represents the length of hospital stay based on the current calculated cost-effective treatment options for the current patient. max H represents the maximum length of hospital stay among all cost-effective treatment options for the current patient, and H represents the subsequent treatment cost based on the current calculated cost-effective treatment options for the current patient. max This represents the maximum subsequent treatment cost among all cost-effective treatment options available for the current patient in reference hepatobiliary surgery treatment plans.

[0031] The preferred system for screening hepatobiliary surgical treatment plans based on big data comprises the following modules:

[0032] The first construction submodule is used to obtain the first coordinate point of the current patient's corresponding reference hepatobiliary surgical treatment plan by taking the safety assessment value of each reference hepatobiliary surgical treatment plan as the x-axis value and the effectiveness assessment value of the corresponding reference hepatobiliary surgical treatment plan as the y-axis value; to obtain the second coordinate point of the current patient's corresponding reference hepatobiliary surgical treatment plan by taking the safety assessment value of each reference hepatobiliary surgical treatment plan as the x-axis value and the economic assessment value of the corresponding reference hepatobiliary surgical treatment plan as the y-axis value; to obtain the third coordinate point of the current patient's corresponding reference hepatobiliary surgical treatment plan by taking the effectiveness assessment value of each reference hepatobiliary surgical treatment plan as the x-axis value and the economic assessment value of the corresponding reference hepatobiliary surgical treatment plan as the y-axis value; and to obtain the first, second, and third coordinate points of each reference hepatobiliary surgical treatment plan of the current patient, and to use the first, second, and third coordinate points of each reference hepatobiliary surgical treatment plan of the current patient as all the drawing coordinate points of each reference hepatobiliary surgical treatment plan of the current patient.

[0033] The second construction submodule is used to obtain the treatment plan analysis matrix for the current patient based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient.

[0034] The preferred system for screening hepatobiliary surgical treatment plans based on big data, the second construction submodule includes:

[0035] The preprocessing unit is used to take the distance between the first coordinate point and the second coordinate point of each reference hepatobiliary surgical treatment plan for the current patient as the first distance of each reference hepatobiliary surgical treatment plan for the current patient, take the distance between the first coordinate point and the third coordinate point of each reference hepatobiliary surgical treatment plan for the current patient as the second distance of each reference hepatobiliary surgical treatment plan for the current patient, and take the distance between the second coordinate point and the third coordinate point of each reference hepatobiliary surgical treatment plan for the current patient as the third distance of each reference hepatobiliary surgical treatment plan for the current patient.

[0036] A construction unit is used to define ordinal numbers for all reference hepatobiliary surgical treatment plans for the current patient, starting from 1 and increasing sequentially according to the first distance of all reference hepatobiliary surgical treatment plans in descending order. This yields the ordinal definition results for all reference hepatobiliary surgical treatment plans for the current patient. Based on the first, second, and third distances and the ordinal definition results of all reference hepatobiliary surgical treatment plans for the current patient, the treatment plan analysis matrix for the current patient is obtained, which is:

[0037]

[0038] Where δ is the current patient's treatment plan analysis matrix, τ n1 The ordinal number of the current patient is defined as the numerical value of the first distance from the reference hepatobiliary surgical treatment plan to n. n2 The ordinal number of the current patient is defined as the value of the second distance of the reference hepatobiliary surgical treatment plan, τ. n3 The ordinal number of the current patient is defined as the value of the third distance of n from the reference hepatobiliary surgical treatment options, where n is the total number of all reference hepatobiliary surgical treatment options for the current patient.

[0039] Preferred, a big data-based hepatobiliary surgery treatment plan screening system includes a screening module comprising:

[0040] The priority value calculation submodule is used to obtain the priority value of each reference hepatobiliary surgical treatment plan for the current patient based on the current patient's treatment plan analysis matrix, which is:

[0041]

[0042] Where μ is the selection priority value of the currently calculated reference hepatobiliary surgical treatment plan for the current patient, τ1 is the value of the first distance of the currently calculated reference hepatobiliary surgical treatment plan for the current patient, τ2 is the value of the second distance of the currently calculated reference hepatobiliary surgical treatment plan for the current patient, and τ3 is the value of the third distance of the currently calculated reference hepatobiliary surgical treatment plan for the current patient. Let σ1 be the rank of the treatment plan analysis matrix for the current patient, σ2 be the standard deviation of the first distance of all reference hepatobiliary surgical treatment plans for the current patient, σ3 be the standard deviation of the third distance of all reference hepatobiliary surgical treatment plans for the current patient, ln be the natural logarithm, and the natural constant e be 2.718.

[0043] The filtering submodule is used to select the reference hepatobiliary surgical treatment plan with the highest priority value from all reference hepatobiliary surgical treatment plans for the current patient, and then use it as the best reference hepatobiliary surgical treatment plan for the current patient.

[0044] This invention provides a method for screening hepatobiliary surgical treatment plans based on big data, applicable to any one of the big data-based hepatobiliary surgical treatment plan screening systems in Examples 1 to 9, comprising:

[0045] S1: Based on all basic information of the current patient and the preset database, obtain all reference patients of the current patient, and based on all reference patients of the current patient, obtain all reference hepatobiliary surgery treatment plans of the current patient;

[0046] S2: Based on all treatment information of all reference hepatobiliary surgical treatment options for the current patient, obtain the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment options for the current patient.

[0047] S3: Based on the safety assessment value, effectiveness assessment value and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient, obtain all plotted coordinate points for each reference hepatobiliary surgical treatment plan for the current patient, and obtain the treatment plan analysis matrix for the current patient based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient.

[0048] S4: Based on the current patient's treatment plan analysis matrix, obtain the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient, and based on the screening priority values ​​of all reference hepatobiliary surgical treatment plans for the current patient, obtain the optimal screening hepatobiliary surgical treatment plan for the current patient.

[0049] The beneficial effects of this invention compared to existing technologies are as follows: Based on all treatment information of all reference hepatobiliary surgical treatment plans for the current patient, the safety assessment value, effectiveness assessment value, and cost-effectiveness assessment value of all reference hepatobiliary surgical treatment plans for the current patient are obtained. This enables the separate quantification of the treatment safety, effectiveness, and cost-effectiveness of each reference hepatobiliary surgical treatment plan for the current patient. Furthermore, based on the safety assessment value, effectiveness assessment value, and cost-effectiveness assessment value of all reference hepatobiliary surgical treatment plans for the current patient, all plotted coordinate points of each reference hepatobiliary surgical treatment plan for the current patient are obtained, facilitating the subsequent construction of the treatment plan analysis matrix for the current patient. Based on the coordinates of all reference hepatobiliary surgical treatment plans for the current patient, a treatment plan analysis matrix is ​​obtained. Then, based on the treatment plan analysis matrix, the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient is obtained. This quantifies the priority of each reference hepatobiliary surgical treatment plan as the best screening hepatobiliary surgical treatment plan for the current patient. Finally, based on the screening priority values ​​of all reference hepatobiliary surgical treatment plans for the current patient, the best screening hepatobiliary surgical treatment plan for the current patient is obtained. This achieves the selection of the most suitable hepatobiliary surgical treatment plan for the current patient by comprehensively considering safety, effectiveness, and economy.

[0050] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written documents of this application.

[0051] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0053] Figure 1 This is a schematic diagram of a big data-based hepatobiliary surgery treatment plan screening system in an embodiment of the present invention;

[0054] Figure 2 This is a flowchart of a method for screening hepatobiliary surgical treatment plans based on big data, as described in an embodiment of the present invention. Detailed Implementation

[0055] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0056] Example 1:

[0057] This invention provides a big data-based system for screening hepatobiliary surgical treatment plans, referencing... Figure 1 ,include:

[0058] The acquisition module is used to obtain all reference patients for the current patient based on all class-based basic information and a preset database, and to obtain all reference hepatobiliary surgery treatment plans for the current patient based on all reference patients.

[0059] The processing module is used to obtain the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient based on all types of treatment information of all reference hepatobiliary surgical treatment plans for the current patient.

[0060] The module is used to obtain all plotted coordinate points for each reference hepatobiliary surgical treatment plan for the current patient based on the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient, and to obtain the treatment plan analysis matrix for the current patient based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient.

[0061] The screening module is used to obtain the screening priority value of each reference hepatobiliary surgery treatment plan for the current patient based on the current patient's treatment plan analysis matrix, and to obtain the optimal screening hepatobiliary surgery treatment plan for the current patient based on the screening priority values ​​of all reference hepatobiliary surgery treatment plans for the current patient.

[0062] In this embodiment, the current patient is the patient for whom the optimal hepatobiliary surgical treatment plan is determined using a big data-based hepatobiliary surgical treatment plan screening system at the current moment.

[0063] In this embodiment, the preset database is a database of pre-stored basic information of a large number of (hepatobiliary surgery) patients and information such as hepatobiliary surgery treatment plans.

[0064] In this embodiment, the reference patients are a subset of patients from a pre-defined database that are used to analyze and determine the optimal hepatobiliary surgical treatment plan for the current patient.

[0065] In this embodiment, the reference hepatobiliary surgical treatment plan refers to a portion of the hepatobiliary surgical treatment plans that need to be referenced in analyzing and determining the optimal hepatobiliary surgical treatment plan for the current patient.

[0066] In this embodiment, the safety assessment value is a numerical value that is evaluated to characterize the treatment safety of each reference hepatobiliary surgical treatment plan for the current patient.

[0067] In this embodiment, the effectiveness assessment value is a numerical value that is evaluated to characterize the treatment effectiveness of each reference hepatobiliary surgical treatment plan for the current patient.

[0068] In this embodiment, the economic assessment value is a numerical value that is evaluated to characterize the treatment economy of each reference hepatobiliary surgical treatment plan for the current patient.

[0069] In this embodiment, the coordinate points are obtained based on the safety assessment values, effectiveness assessment values, and cost-effectiveness assessment values ​​of all reference hepatobiliary surgical treatment plans for the current patient, and are used to construct the treatment plan analysis matrix for the current patient.

[0070] In this embodiment, the current patient's treatment plan analysis matrix is ​​a matrix used to analyze and calculate the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient.

[0071] In this embodiment, the screening priority value is a numerical value that comprehensively considers safety, effectiveness, and cost-effectiveness, representing the priority of each reference hepatobiliary surgical treatment plan for the current patient as the best screening hepatobiliary surgical treatment plan.

[0072] In this embodiment, the optimal hepatobiliary surgical treatment plan for the current patient is the best overall reference hepatobiliary surgical treatment plan selected from all reference hepatobiliary surgical treatment plans for the current patient.

[0073] The beneficial effects of the above technology are as follows: Based on all treatment information of all reference hepatobiliary surgical treatment plans for the current patient, the safety assessment value, effectiveness assessment value, and cost-effectiveness assessment value of all reference hepatobiliary surgical treatment plans for the current patient are obtained. This allows for the quantification of the treatment safety, effectiveness, and cost-effectiveness of each reference hepatobiliary surgical treatment plan for the current patient. Furthermore, based on the safety assessment value, effectiveness assessment value, and cost-effectiveness assessment value of all reference hepatobiliary surgical treatment plans for the current patient, all plotted coordinate points of each reference hepatobiliary surgical treatment plan for the current patient are obtained, facilitating the construction of the treatment plan analysis matrix for the current patient. Based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient, the treatment plan analysis matrix for the current patient is obtained. Then, based on the treatment plan analysis matrix for the current patient, the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient is obtained, quantifying the priority of each reference hepatobiliary surgical treatment plan for the current patient as the best screening hepatobiliary surgical treatment plan. Finally, based on the screening priority value of all reference hepatobiliary surgical treatment plans for the current patient, the best screening hepatobiliary surgical treatment plan for the current patient is obtained. This achieves the comprehensive selection of the most suitable hepatobiliary surgical treatment plan for the current patient from the aspects of safety, effectiveness, and cost-effectiveness.

[0074] Example 2:

[0075] Based on Example 1, the big data-based hepatobiliary surgery treatment plan screening system includes an acquisition module comprising:

[0076] The reference patient determination submodule is used to obtain all reference patients for the current patient based on all class-based basic information of the current patient and a preset database;

[0077] The treatment plan acquisition submodule is used to extract the hepatobiliary surgery treatment plan for each reference patient from the preset database and use it as the reference hepatobiliary surgery treatment plan for the current patient.

[0078] The beneficial effects of the above technology are: based on the preset database and each reference patient of the current patient, a reference hepatobiliary surgical treatment plan for the current patient can be obtained, which facilitates the subsequent screening of the best hepatobiliary surgical treatment plan for the current patient.

[0079] Example 3:

[0080] Based on Example 2, the big data-based hepatobiliary surgery treatment plan screening system includes a patient-specific submodule, comprising:

[0081] The basic information acquisition unit is used to acquire all types of basic information of the current patient, including the nature of the lesion, the diameter and length of the lesion, and the location of the lesion.

[0082] The reference patient determination unit is used to treat the corresponding patient as the reference patient of the current patient when all the basic information of each patient stored in the preset database is the same as all the basic information of the current patient.

[0083] In this embodiment, the nature of the lesion refers to the nature of the patient's hepatobiliary surgical lesions, such as tumors, stones, inflammation, etc.

[0084] In this embodiment, the lesion diameter is the longest transverse diameter of the lesion (such as tumor, stone, inflammatory area, etc.) observed in medical images (such as ultrasound, CT, MRI, etc.), and the unit of the lesion diameter is millimeters (mm).

[0085] In this embodiment, the lesion location area is determined by using imaging technology to pinpoint the specific location of the patient's hepatobiliary surgical lesion in the liver or biliary system. In this embodiment, all areas of the hepatobiliary system have been pre-divided.

[0086] In this embodiment, the corresponding basic information of each patient is stored in a preset database and is the same as the basic information of the corresponding patient.

[0087] The beneficial effects of the above technology are: it clarifies the specific information items of all basic information of the current patient, and provides a detailed method for obtaining all reference patients of the current patient based on all basic information of the current patient and a preset database.

[0088] Example 4:

[0089] Based on Example 1, the big data-based hepatobiliary surgery treatment plan screening system includes a processing module comprising:

[0090] The preprocessing submodule is used to obtain all types of treatment information for all reference hepatobiliary surgical treatment plans for the current patient. All types of treatment information include safety treatment information, effectiveness treatment information, and cost-effectiveness treatment information. Safety treatment information includes postoperative recovery time, postoperative liver function assessment index values, and intraoperative blood loss. Effectiveness treatment information includes surgical resection rate and postoperative symptom improvement scores. Cost-effectiveness treatment information includes surgical costs, hospitalization time, and subsequent treatment costs.

[0091] The processing submodule is used to obtain the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient based on all types of treatment information of all reference hepatobiliary surgical treatment plans for the current patient.

[0092] In this embodiment, the postoperative recovery time is the time required for the patient to recover after surgery using each reference hepatobiliary surgical treatment plan (if the reference hepatobiliary surgical treatment plan is not surgical, the postoperative recovery time is the time required for the patient's hepatobiliary lesion to heal).

[0093] In this embodiment, the postoperative liver function assessment index is a value that can characterize the liver function of the corresponding patient after the corresponding patient has been treated with each reference hepatobiliary surgery treatment plan, based on multiple liver function assessment indicators (alanine aminotransferase, aspartate aminotransferase, total bilirubin, etc.).

[0094] In this embodiment, intraoperative blood loss refers to the amount of blood loss experienced by the patient during surgery after each reference hepatobiliary surgical treatment plan is performed on the corresponding patient (if the reference hepatobiliary surgical treatment plan is not surgical, then the intraoperative blood loss is 0).

[0095] In this embodiment, the surgical resection rate is the quotient of the area of ​​the patient's lesions (such as tumors, stones, inflammatory areas, etc.) eliminated after treatment with each reference hepatobiliary surgical treatment plan (observed through medical imaging, such as ultrasound), and the area of ​​the patient's lesions (such as tumors, stones, inflammatory areas, etc.) before treatment.

[0096] In this embodiment, the score for postoperative symptom improvement is a numerical value assigned by the attending physician based on various liver function assessment indicators (alanine aminotransferase, aspartate aminotransferase, total bilirubin, etc.) to evaluate the degree of liver function improvement before and after treatment for the corresponding patient for each reference hepatobiliary surgical treatment plan.

[0097] In this embodiment, the surgical cost is the cost required for the patient to undergo surgery using each reference hepatobiliary surgical treatment plan (if the reference hepatobiliary surgical treatment plan is not a surgical procedure, the surgical cost is the cost required for the initial cure of the patient's hepatobiliary lesion).

[0098] In this embodiment, the length of hospital stay is the number of days a patient needs to be hospitalized when each reference hepatobiliary surgery treatment plan is used to treat the corresponding patient.

[0099] In this embodiment, the subsequent treatment costs are the costs of follow-up examinations and treatment of complications required by the patient after each reference hepatobiliary surgery treatment plan has been used to treat the corresponding patient.

[0100] The beneficial effects of the above technology are: it clarifies the specific information items of all types of treatment information for all reference hepatobiliary surgical treatment plans for the current patient, which facilitates the subsequent acquisition of safety assessment values, effectiveness assessment values, and cost assessment values ​​of all reference hepatobiliary surgical treatment plans for the current patient based on all types of treatment information of all reference hepatobiliary surgical treatment plans for the current patient.

[0101] Example 5:

[0102] Based on Example 4, the big data-based hepatobiliary surgery treatment plan screening system includes a processing submodule, comprising:

[0103] The first processing unit is used to obtain the safety assessment value of each reference hepatobiliary surgical treatment plan for the current patient based on the safety treatment information of all reference hepatobiliary surgical treatment plans for the current patient, namely:

[0104]

[0105] Where α is the safety assessment value of the current calculated reference hepatobiliary surgical treatment plan for the current patient, and A is the postoperative recovery time of the current calculated reference hepatobiliary surgical treatment plan for the current patient. max B represents the maximum postoperative recovery time among all reference hepatobiliary surgical treatment protocols for the current patient, and B represents the postoperative liver function assessment value of the current patient based on the calculated safety information of the reference hepatobiliary surgical treatment protocols. maxC represents the maximum value of postoperative liver function assessment indicators among all reference hepatobiliary surgical treatment protocols for the current patient, and C represents the intraoperative blood loss based on the current calculated reference hepatobiliary surgical treatment protocol for the current patient. max The maximum intraoperative blood loss among all reference hepatobiliary surgical treatment protocols for the current patient is given, where ln is the natural logarithm and the natural constant e is 2.718.

[0106] The second processing unit is used to obtain the effectiveness assessment value of each reference hepatobiliary surgical treatment plan for the current patient based on the effectiveness treatment information of all reference hepatobiliary surgical treatment plans for the current patient, namely:

[0107]

[0108] Where β is the effectiveness assessment value of the current calculated reference hepatobiliary surgical treatment plan for the current patient, and D is the surgical resection rate of the current calculated reference hepatobiliary surgical treatment plan for the current patient. max E represents the maximum surgical resection rate among all reference hepatobiliary surgical treatment options for the current patient, and E is the postoperative symptom improvement score assigned to the current patient based on the effectiveness of the current reference hepatobiliary surgical treatment options. max Assign the maximum score among all reference hepatobiliary surgery treatment options for the current patient regarding postoperative symptom improvement.

[0109] The third processing unit is used to obtain the cost assessment value of each reference hepatobiliary surgery treatment plan for the current patient based on the cost-effectiveness information of all reference hepatobiliary surgery treatment plans for the current patient.

[0110] The beneficial effects of the above technology are as follows: Based on all types of treatment information of all reference hepatobiliary surgical treatment plans for the current patient, the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient are obtained. This enables the separate quantification of the treatment safety, treatment effectiveness, and treatment cost of each reference hepatobiliary surgical treatment plan for the current patient, which facilitates the determination of coordinate points for subsequent plotting.

[0111] Example 6:

[0112] Based on Example 5, the big data-based hepatobiliary surgery treatment plan screening system includes a method for the third processing unit to obtain the economic evaluation value of each reference hepatobiliary surgery treatment plan for the current patient based on the economic treatment information of all reference hepatobiliary surgery treatment plans for the current patient, comprising:

[0113]

[0114] Where γ represents the current calculated cost-effectiveness assessment of the reference hepatobiliary surgical treatment plan for the current patient, and F represents the surgical cost of the current calculated cost-effectiveness treatment information for the reference hepatobiliary surgical treatment plan for the current patient. max G represents the maximum surgical cost among all cost-effective treatment options for the current patient, and G represents the length of hospital stay based on the current calculated cost-effective treatment options for the current patient. max H represents the maximum length of hospital stay among all cost-effective treatment options for the current patient, and H represents the subsequent treatment cost based on the current calculated cost-effective treatment options for the current patient. max This represents the maximum subsequent treatment cost among all cost-effective treatment options available for the current patient in reference hepatobiliary surgery treatment plans.

[0115] The beneficial effects of the above technology are as follows: based on the cost-effectiveness information of all reference hepatobiliary surgical treatment options for the current patient, the cost-effectiveness assessment value of each reference hepatobiliary surgical treatment option for the current patient is obtained, thus achieving accurate quantification of the cost-effectiveness of each reference hepatobiliary surgical treatment option for the current patient.

[0116] Example 7:

[0117] Based on Example 1, the big data-based hepatobiliary surgery treatment plan screening system comprises the following modules:

[0118] The first construction submodule is used to obtain the first coordinate point of the current patient's corresponding reference hepatobiliary surgical treatment plan by taking the safety assessment value of each reference hepatobiliary surgical treatment plan as the x-axis value and the effectiveness assessment value of the corresponding reference hepatobiliary surgical treatment plan as the y-axis value; to obtain the second coordinate point of the current patient's corresponding reference hepatobiliary surgical treatment plan by taking the safety assessment value of each reference hepatobiliary surgical treatment plan as the x-axis value and the economic assessment value of the corresponding reference hepatobiliary surgical treatment plan as the y-axis value; to obtain the third coordinate point of the current patient's corresponding reference hepatobiliary surgical treatment plan by taking the effectiveness assessment value of each reference hepatobiliary surgical treatment plan as the x-axis value and the economic assessment value of the corresponding reference hepatobiliary surgical treatment plan as the y-axis value; and to obtain the first, second, and third coordinate points of each reference hepatobiliary surgical treatment plan of the current patient, and to use the first, second, and third coordinate points of each reference hepatobiliary surgical treatment plan of the current patient as all the drawing coordinate points of each reference hepatobiliary surgical treatment plan of the current patient.

[0119] The second construction submodule is used to obtain the treatment plan analysis matrix for the current patient based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient.

[0120] The beneficial effects of the above technology are as follows: Based on the safety assessment values, effectiveness assessment values, and cost-effectiveness assessment values ​​of all reference hepatobiliary surgical treatment plans for the current patient, all plotted coordinate points of each reference hepatobiliary surgical treatment plan for the current patient are obtained, which facilitates the construction of the treatment plan analysis matrix for the current patient. Based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient, the treatment plan analysis matrix for the current patient is obtained, which reduces the influence of subjective judgment in the subsequent determination of the best hepatobiliary surgical treatment plan and enhances the scientificity and objectivity of the decision-making process.

[0121] Example 8:

[0122] Based on Example 7, the second construction submodule of the big data-based hepatobiliary surgery treatment plan screening system includes:

[0123] The preprocessing unit is used to take the distance between the first coordinate point and the second coordinate point of each reference hepatobiliary surgical treatment plan for the current patient as the first distance of each reference hepatobiliary surgical treatment plan for the current patient, take the distance between the first coordinate point and the third coordinate point of each reference hepatobiliary surgical treatment plan for the current patient as the second distance of each reference hepatobiliary surgical treatment plan for the current patient, and take the distance between the second coordinate point and the third coordinate point of each reference hepatobiliary surgical treatment plan for the current patient as the third distance of each reference hepatobiliary surgical treatment plan for the current patient.

[0124] A construction unit is used to define ordinal numbers for all reference hepatobiliary surgical treatment plans for the current patient, starting from 1 and increasing sequentially according to the first distance of all reference hepatobiliary surgical treatment plans in descending order. This yields the ordinal definition results for all reference hepatobiliary surgical treatment plans for the current patient. Based on the first, second, and third distances and the ordinal definition results of all reference hepatobiliary surgical treatment plans for the current patient, the treatment plan analysis matrix for the current patient is obtained, which is:

[0125]

[0126] Where δ is the current patient's treatment plan analysis matrix, τ n1 The ordinal number of the current patient is defined as the numerical value of the first distance from the reference hepatobiliary surgical treatment plan to n. n2 The ordinal number of the current patient is defined as the value of the second distance of the reference hepatobiliary surgical treatment plan, τ. n3The ordinal number of the current patient is defined as the value of the third distance of n from the reference hepatobiliary surgical treatment options, where n is the total number of all reference hepatobiliary surgical treatment options for the current patient.

[0127] The beneficial effects of the above technology are as follows: Based on all the plotted coordinate points of all reference hepatobiliary surgery treatment plans for the current patient, the treatment plan analysis matrix of the current patient can be obtained. This embodiment provides a detailed method for constructing the treatment plan analysis matrix of the current patient.

[0128] Example 9:

[0129] Based on Example 8, the big data-based hepatobiliary surgery treatment plan screening system includes a screening module comprising:

[0130] The priority value calculation submodule is used to obtain the priority value of each reference hepatobiliary surgical treatment plan for the current patient based on the current patient's treatment plan analysis matrix, which is:

[0131]

[0132] Where μ is the selection priority value of the currently calculated reference hepatobiliary surgical treatment plan for the current patient, τ1 is the value of the first distance of the currently calculated reference hepatobiliary surgical treatment plan for the current patient, τ2 is the value of the second distance of the currently calculated reference hepatobiliary surgical treatment plan for the current patient, and τ3 is the value of the third distance of the currently calculated reference hepatobiliary surgical treatment plan for the current patient. Let σ1 be the rank of the treatment plan analysis matrix for the current patient, σ2 be the standard deviation of the first distance of all reference hepatobiliary surgical treatment plans for the current patient, σ3 be the standard deviation of the third distance of all reference hepatobiliary surgical treatment plans for the current patient, ln be the natural logarithm, and the natural constant e be 2.718.

[0133] The filtering submodule is used to select the reference hepatobiliary surgical treatment plan with the highest priority value from all reference hepatobiliary surgical treatment plans for the current patient, and then use it as the best reference hepatobiliary surgical treatment plan for the current patient.

[0134] The beneficial effects of the above technology are as follows: Based on the current patient's treatment plan analysis matrix, the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient is obtained, which realizes the quantification of the priority of each reference hepatobiliary surgical treatment plan as the best screening hepatobiliary surgical treatment plan for the current patient. Finally, based on the screening priority values ​​of all reference hepatobiliary surgical treatment plans for the current patient, the best screening hepatobiliary surgical treatment plan for the current patient is obtained. This achieves the comprehensive consideration of safety, effectiveness, and economy, and selects the most suitable hepatobiliary surgical treatment plan for the current patient.

[0135] Example 10:

[0136] This invention provides a method for screening hepatobiliary surgical treatment plans based on big data, applicable to any of the big data-based hepatobiliary surgical treatment plan screening systems in Examples 1 to 9, with reference to... Figure 2 ,include:

[0137] S1: Based on all basic information of the current patient and the preset database, obtain all reference patients of the current patient, and based on all reference patients of the current patient, obtain all reference hepatobiliary surgery treatment plans of the current patient;

[0138] S2: Based on all treatment information of all reference hepatobiliary surgical treatment options for the current patient, obtain the safety assessment value, effectiveness assessment value, and cost assessment value of all reference hepatobiliary surgical treatment options for the current patient.

[0139] S3: Based on the safety assessment value, effectiveness assessment value and cost assessment value of all reference hepatobiliary surgical treatment plans for the current patient, obtain all plotted coordinate points for each reference hepatobiliary surgical treatment plan for the current patient, and obtain the treatment plan analysis matrix for the current patient based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient.

[0140] S4: Based on the current patient's treatment plan analysis matrix, obtain the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient, and based on the screening priority values ​​of all reference hepatobiliary surgical treatment plans for the current patient, obtain the optimal screening hepatobiliary surgical treatment plan for the current patient.

[0141] The beneficial effects of the above technology are as follows: Based on all treatment information of all reference hepatobiliary surgical treatment plans for the current patient, the safety assessment value, effectiveness assessment value, and cost-effectiveness assessment value of all reference hepatobiliary surgical treatment plans for the current patient are obtained. This allows for the quantification of the treatment safety, effectiveness, and cost-effectiveness of each reference hepatobiliary surgical treatment plan for the current patient. Furthermore, based on the safety assessment value, effectiveness assessment value, and cost-effectiveness assessment value of all reference hepatobiliary surgical treatment plans for the current patient, all plotted coordinate points of each reference hepatobiliary surgical treatment plan for the current patient are obtained, facilitating the construction of the treatment plan analysis matrix for the current patient. Based on all plotted coordinate points of all reference hepatobiliary surgical treatment plans for the current patient, the treatment plan analysis matrix for the current patient is obtained. Then, based on the treatment plan analysis matrix for the current patient, the screening priority value of each reference hepatobiliary surgical treatment plan for the current patient is obtained, quantifying the priority of each reference hepatobiliary surgical treatment plan for the current patient as the best screening hepatobiliary surgical treatment plan. Finally, based on the screening priority value of all reference hepatobiliary surgical treatment plans for the current patient, the best screening hepatobiliary surgical treatment plan for the current patient is obtained. This achieves the comprehensive selection of the most suitable hepatobiliary surgical treatment plan for the current patient from the aspects of safety, effectiveness, and cost-effectiveness.

[0142] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention, and this invention is also intended to include these modifications and variations.

Claims

1. A big data-based hepatobiliary surgery treatment plan screening system, characterized in that, The method comprises the following steps: An acquisition module is configured to obtain all reference patients of a current patient based on all class basic information of the current patient and a preset database, and obtain all reference hepatobiliary surgery treatment schemes of the current patient based on all reference patients of the current patient; A processing module is configured to obtain a safety evaluation value, an effectiveness evaluation value and an economy evaluation value of all reference hepatobiliary surgery treatment schemes of the current patient based on all class treatment information of the reference hepatobiliary surgery treatment schemes; A construction module is configured to obtain all plotted coordinate points of each reference hepatobiliary surgery treatment scheme of the current patient based on the safety evaluation value, the effectiveness evaluation value and the economy evaluation value of all reference hepatobiliary surgery treatment schemes of the current patient, and obtain a treatment scheme analysis matrix of the current patient based on all plotted coordinate points of all reference hepatobiliary surgery treatment schemes of the current patient; A screening module is configured to obtain a screening priority value of each reference hepatobiliary surgery treatment scheme of the current patient based on the treatment scheme analysis matrix of the current patient, and obtain an optimal screening hepatobiliary surgery treatment scheme of the current patient based on the screening priority value of all reference hepatobiliary surgery treatment schemes of the current patient. The processing module comprises: A preprocessing sub-module is configured to obtain all class treatment information of all reference hepatobiliary surgery treatment schemes of the current patient, wherein the all class treatment information comprises safety treatment information, effectiveness treatment information and economy treatment information, and the safety treatment information comprises postoperative recovery time, postoperative liver function evaluation index value and intraoperative bleeding amount, the effectiveness treatment information comprises surgical resection rate and postoperative symptom improvement condition score value, and the economy treatment information comprises surgical cost, hospitalization time and subsequent treatment cost; The processing sub-module is configured to obtain a safety evaluation value, an effectiveness evaluation value and an economy evaluation value of all reference hepatobiliary surgery treatment schemes of the current patient based on all class treatment information of the reference hepatobiliary surgery treatment schemes; The processing sub-module comprises: A first processing unit is configured to obtain a safety evaluation value of each reference hepatobiliary surgery treatment scheme of the current patient based on safety treatment information of all reference hepatobiliary surgery treatment schemes of the current patient, and the safety evaluation value is as follows: wherein, a is the safety evaluation value of the current calculated reference hepatobiliary surgical treatment scheme of the current patient, A is the postoperative recovery time of the safety treatment information of the current calculated reference hepatobiliary surgical treatment scheme of the current patient, A max is the maximum value among the postoperative recovery times of the safety treatment information of all the reference hepatobiliary surgical treatment schemes of the current patient, B is the postoperative liver function evaluation index value of the safety treatment information of the current calculated reference hepatobiliary surgical treatment scheme of the current patient, B max is the maximum value among the postoperative liver function evaluation index values of the safety treatment information of all the reference hepatobiliary surgical treatment schemes of the current patient, C is the intraoperative bleeding amount of the safety treatment information of the current calculated reference hepatobiliary surgical treatment scheme of the current patient, C max is the maximum value among the intraoperative bleeding amounts of the safety treatment information of all the reference hepatobiliary surgical treatment schemes of the current patient, ln is the natural logarithm, and the value of the natural constant e is 2.718; A second processing unit is configured to obtain an effectiveness evaluation value of each reference hepatobiliary surgery treatment scheme of the current patient based on effectiveness treatment information of all reference hepatobiliary surgery treatment schemes of the current patient, and the effectiveness evaluation value is as follows: wherein β is the current calculated effectiveness evaluation value of the reference hepatobiliary surgery treatment scheme for the current patient, D is the surgical resection rate of the effectiveness treatment information of the current calculated reference hepatobiliary surgery treatment scheme for the current patient, D max is the maximum value among the surgical resection rates of the effectiveness treatment information of all the reference hepatobiliary surgery treatment schemes for the current patient, E is the postoperative symptom improvement score value of the effectiveness treatment information of the current calculated reference hepatobiliary surgery treatment scheme for the current patient, E max is the maximum value among the postoperative symptom improvement score values of the effectiveness treatment information of all the reference hepatobiliary surgery treatment schemes for the current patient. A third processing unit is configured to obtain an economy evaluation value of each reference hepatobiliary surgery treatment scheme of the current patient based on economy treatment information of all reference hepatobiliary surgery treatment schemes of the current patient, and the economy evaluation value is as follows. 2.The big data based hepatobiliary surgical treatment plan screening system according to claim 1, wherein, The acquisition module comprises: A reference patient determination sub-module is configured to obtain all reference patients of the current patient based on all class basic information of the current patient and a preset database; A scheme acquisition sub-module is configured to extract hepatobiliary surgery treatment schemes of each reference patient of the current patient from the preset database, and take the hepatobiliary surgery treatment schemes as reference hepatobiliary surgery treatment schemes of the current patient. 3.The big data based hepatobiliary surgical treatment plan screening system according to claim 2, characterized in that, The reference patient determination sub-module comprises: The basic information acquisition unit is configured to acquire all the basic information of the current patient, wherein the all the basic information comprises lesion nature, lesion diameter length and lesion location area; The reference patient determination unit is configured to determine a reference patient of the current patient when all the basic information of each patient stored in the preset database corresponds to all the basic information of the current patient. 4.The big data based hepatobiliary surgical treatment plan screening system according to claim 1, wherein, The third processing unit is configured to obtain the economic evaluation value of each reference liver and gallbladder surgery treatment scheme of the current patient based on the economic treatment information of all the reference liver and gallbladder surgery treatment schemes of the current patient, and the method comprises the following steps of: wherein γ is the current calculated economic assessment value of the reference hepatobiliary surgery treatment plan for the current patient, F is the surgical cost of the current calculated economic treatment information of the reference hepatobiliary surgery treatment plan for the current patient, F max is the maximum value among the surgical costs of the economic treatment information of all the reference hepatobiliary surgery treatment plans for the current patient, G is the hospitalization time of the current calculated economic treatment information of the reference hepatobiliary surgery treatment plan for the current patient, G max is the maximum value among the hospitalization times of the economic treatment information of all the reference hepatobiliary surgery treatment plans for the current patient, H is the subsequent treatment cost of the current calculated economic treatment information of the reference hepatobiliary surgery treatment plan for the current patient, H max is the maximum value among the subsequent treatment costs of the economic treatment information of all the reference hepatobiliary surgery treatment plans for the current patient. 5.The big data based hepatobiliary surgical treatment plan screening system according to claim 1, wherein, The construction module comprises: The first construction submodule is configured to obtain a first coordinate point of the corresponding reference liver and gallbladder surgery treatment scheme of the current patient by taking the safety evaluation value of each reference liver and gallbladder surgery treatment scheme of the current patient as the horizontal coordinate value and taking the effectiveness evaluation value of the corresponding reference liver and gallbladder surgery treatment scheme of the current patient as the vertical coordinate value, obtain a second coordinate point of the corresponding reference liver and gallbladder surgery treatment scheme of the current patient by taking the safety evaluation value of each reference liver and gallbladder surgery treatment scheme of the current patient as the horizontal coordinate value and taking the economic evaluation value of the corresponding reference liver and gallbladder surgery treatment scheme of the current patient as the vertical coordinate value, obtain a third coordinate point of the corresponding reference liver and gallbladder surgery treatment scheme of the current patient by taking the effectiveness evaluation value of each reference liver and gallbladder surgery treatment scheme of the current patient as the horizontal coordinate value and taking the economic evaluation value of the corresponding reference liver and gallbladder surgery treatment scheme of the current patient as the vertical coordinate value, obtain the first coordinate point, the second coordinate point and the third coordinate point of each reference liver and gallbladder surgery treatment scheme of the current patient, and take the first coordinate point, the second coordinate point and the third coordinate point of each reference liver and gallbladder surgery treatment scheme of the current patient as all the drawing coordinate points of each reference liver and gallbladder surgery treatment scheme of the current patient; The second construction submodule is configured to obtain a treatment scheme analysis matrix of the current patient based on all the drawing coordinate points of all the reference liver and gallbladder surgery treatment schemes of the current patient. 6.The big data based hepatobiliary surgical treatment plan screening system according to claim 5, wherein, The second construction submodule comprises: The preprocessing unit is configured to take the distance between the first coordinate point and the second coordinate point of each reference liver and gallbladder surgery treatment scheme of the current patient as the first distance of each reference liver and gallbladder surgery treatment scheme of the current patient, take the distance between the first coordinate point and the third coordinate point of each reference liver and gallbladder surgery treatment scheme of the current patient as the second distance of each reference liver and gallbladder surgery treatment scheme of the current patient, and take the distance between the second coordinate point and the third coordinate point of each reference liver and gallbladder surgery treatment scheme of the current patient as the third distance of each reference liver and gallbladder surgery treatment scheme of the current patient; The construction unit is configured to define a serial number of all the reference liver and gallbladder surgery treatment schemes of the current patient in ascending order according to the first distance from large to small, obtain a serial number definition result of all the reference liver and gallbladder surgery treatment schemes of the current patient, and obtain a treatment scheme analysis matrix of the current patient based on the first distance, the second distance, the third distance and the serial number definition result of all the reference liver and gallbladder surgery treatment schemes of the current patient, that is, wherein δ is the treatment protocol analysis matrix of the current patient, τ n1 is the value of the first distance of the reference hepatobiliary surgery treatment protocol defined for the ordinal number of the current patient n, τ n2 is the value of the second distance of the reference hepatobiliary surgery treatment protocol defined for the ordinal number of the current patient n, τ n3 is the value of the third distance of the reference hepatobiliary surgery treatment protocol defined for the ordinal number of the current patient n, n is the total number of all reference hepatobiliary surgery treatment protocols of the current patient.

7. The big data based hepatobiliary surgical treatment plan screening system according to claim 6, characterized in that, The screening module comprises: The screening priority value calculation submodule is configured to obtain a screening priority value of each reference hepatobiliary surgery treatment scheme of the current patient based on the treatment scheme analysis matrix of the current patient, i.e., to obtain the screening priority value of each reference hepatobiliary surgery treatment scheme of the current patient based on the treatment scheme analysis matrix of the current patient. wherein μ is the current calculated screening priority value of the reference hepatobiliary surgical treatment scheme for the current patient, τ1 is the numerical value of the first distance of the current calculated reference hepatobiliary surgical treatment scheme for the current patient, τ2 is the numerical value of the second distance of the current calculated reference hepatobiliary surgical treatment scheme for the current patient, τ3 is the numerical value of the third distance of the current calculated reference hepatobiliary surgical treatment scheme for the current patient, is the rank of the treatment scheme analysis matrix for the current patient, σ1 is the standard deviation of the numerical values of the first distance of all the reference hepatobiliary surgical treatment schemes for the current patient, σ2 is the standard deviation of the numerical values of the second distance of all the reference hepatobiliary surgical treatment schemes for the current patient, σ3 is the standard deviation of the numerical values of the third distance of all the reference hepatobiliary surgical treatment schemes for the current patient, ln is the natural logarithm, and the value of the natural constant e is 2.718; The screening submodule is configured to select a reference hepatobiliary surgery treatment scheme with the maximum screening priority value from all reference hepatobiliary surgery treatment schemes of the current patient as the optimal screening hepatobiliary surgery treatment scheme of the current patient.

8. A big data-based hepatobiliary surgery treatment plan screening method, characterized in that, The application relates to a hepatobiliary surgery treatment scheme screening system based on big data, which is applied to the hepatobiliary surgery treatment scheme screening system based on big data. S1: obtaining all reference patients of the current patient based on all basic information of the current patient and a preset database, and obtaining all reference hepatobiliary surgery treatment schemes of the current patient based on all reference patients of the current patient; S2: obtaining a safety evaluation value, an effectiveness evaluation value and an economy evaluation value of all reference hepatobiliary surgery treatment schemes of the current patient based on all treatment information of all reference hepatobiliary surgery treatment schemes of the current patient; S3: obtaining all drawing coordinate points of each reference hepatobiliary surgery treatment scheme of the current patient based on the safety evaluation value, the effectiveness evaluation value and the economy evaluation value of all reference hepatobiliary surgery treatment schemes of the current patient, and obtaining a treatment scheme analysis matrix of the current patient based on all drawing coordinate points of all reference hepatobiliary surgery treatment schemes of the current patient; S4: obtaining a screening priority value of each reference hepatobiliary surgery treatment scheme of the current patient based on the treatment scheme analysis matrix of the current patient, and obtaining an optimal screening hepatobiliary surgery treatment scheme of the current patient based on the screening priority value of all reference hepatobiliary surgery treatment schemes of the current patient.

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