Clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors

Abstract

The clinical decision support system comprises a data layer, a data processing layer, a decision reasoning layer and an interaction layer, the data layer is internally provided with an electronic medical record database, a medical knowledge base, an image database and an inspection database, and the data processing layer is used for calling data in the data layer. The decision reasoning layer obtains the patient tumor key indexes extracted after processing of the data processing layer, carries out preliminary diagnosis and differential diagnosis on tumors according to the patient tumor key indexes, and recommends personalized treatment schemes and risk assessment; the interaction layer finally realizes diagnosis and treatment risk prompt according to preliminary decision-making suggestions of the decision-making reasoning layer in combination with hospital admission records and laboratory inspection results, can provide accurate diagnosis, reasonable treatment schemes and personalized treatment suggestions, and assists doctors in scientifically making decisions and formulating more suitable treatment schemes, so that the life quality of liver cancer patients is improved, and the treatment efficiency is improved. The medical quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of auxiliary medical technology, and in particular to a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors. Background Art

[0004] From a medical perspective, a tumor is a new organism formed by the abnormal proliferation of local tissue cells under the action of multiple tumorigenic factors. According to the biological characteristics of tumor cells and the degree of harm to the body, they can be divided into two categories: benign tumors and malignant tumors. Malignant tumors, commonly known as cancer, have the characteristics of rapid growth, easy infiltration and metastasis, and significant systemic symptoms. For example, bone cancer and esophageal cancer have a high recurrence rate after surgical resection, which causes great harm to the body.

[0005] The causes of tumors are complex and diverse, and can be classified into two categories: internal and external. Internal factors mainly include the patient's genetic background, physical condition, mental state, and the induction of other diseases; external factors involve living environment, living habits, dietary characteristics, occupational exposure, and drug use. The diagnosis of tumors currently relies mainly on the comprehensive application of multiple methods such as routine examinations, cytological examinations, biopsy, and surgical specimen examinations.

[0006] Given the complexity of tumor occurrence and development and the serious threat to patients' lives, tumor inspection, diagnosis and treatment require extremely high professionalism and technical skills. Therefore, it has become an urgent task to strengthen the construction of tumor prevention and treatment systems, improve medical service capabilities, and promote innovation in diagnosis and treatment technologies. Summary of the invention

[0007] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0008] Therefore, the purpose of the present invention is to provide a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors, which can provide accurate diagnosis, reasonable treatment plans and personalized treatment suggestions, help doctors make scientific decisions and formulate more appropriate treatment plans, so as to improve the quality of life of liver cancer patients and improve the quality of medical care.

[0009] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0010] A clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors, comprising:

[0011] Data layer, with built-in electronic medical record database, medical knowledge base, imaging database and inspection database;

[0012] The data processing layer is used to retrieve the data in the data layer and extract the key indicators of the patient's tumor after preprocessing the data;

[0013] The decision-making reasoning layer obtains the key indicators of the patient's tumor extracted after processing by the data processing layer, and makes a preliminary diagnosis and differential diagnosis of the tumor, recommends personalized treatment plans, and conducts risk assessment based on the key indicators of the patient's tumor.

[0014] The interaction layer, based on the preliminary decision opinions of the decision reasoning layer, combines the admission records and laboratory test results to ultimately achieve automatic evaluation of the Child-Pugh score, Carprini score, CNLC, ALBI, BCLC, and CNLC, and automatically generates diagnosis and treatment risk prompts in combination with the medical knowledge base.

[0015] As a preferred solution of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors described in the present invention, in step S1, the electronic medical record database stores the patient's basic information, medical history, symptoms and treatment records;

[0016] The medical knowledge base contains diagnostic criteria, treatment guidelines, drug information, clinical research data, and expert experience for tumor diseases;

[0017] The imaging database stores various imaging examination images and reports of patients.

[0018] As a preferred solution of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer described in the present invention, the data processing layer includes a data cleaning module, a data integration module and a feature extraction module;

[0019] The data cleaning module cleans, removes duplications, and standardizes data from different data sources; the data integration module integrates the standardized data into a unified format;

[0020] The feature extraction module extracts key features from patient data, including tumor marker levels and various imaging indicators for evaluation, providing a basis for decision-making reasoning.

[0021] As a preferred solution of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer described in the present invention, the decision reasoning layer includes a diagnosis reasoning engine, a treatment recommendation engine, and a risk assessment engine;

[0022] The diagnostic reasoning engine performs preliminary diagnosis and differential diagnosis of tumors based on the patient's symptoms, test results, and diagnostic criteria in the medical knowledge base;

[0023] The treatment recommendation engine recommends personalized treatment plans based on the patient's condition, physical condition and treatment guidelines, where the treatment plans include surgery, chemotherapy, radiotherapy and targeted therapy;

[0024] The risk assessment engine assesses a patient's treatment risk and risk of complications.

[0025] As a preferred solution of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors described in the present invention, the interactive layer includes a doctor's workstation interface, and the doctor's workstation interface includes patient basic information, page layout settings, surgical information, staging, evaluation, ICD10 diagnosis settings, test results, diagnosis and treatment risk prompts, and imaging / pathology / microscopy results modules.

[0026] As a preferred solution of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer described in the present invention, the staging module includes a BCLC staging assessment system, a CNLC staging assessment system, a TNM staging assessment system and an ALBI staging assessment system, and the above liver cancer tumor stagings are automatically generated through the patient's test data and imaging indicators.

[0027] As a preferred solution of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors described in the present invention, the evaluation module includes Child-Pugh evaluation and Carprini evaluation, and the above risk assessments are automatically generated through the patient's test data and imaging indicators.

[0028] Compared with the prior art, the present invention has the following beneficial effects: the present invention combines medical entity and relationship extraction algorithms to complete the construction of a CT / MRI image report medical record learning model, automatically extracts various types of imaging indicators for evaluation, and then combines admission records and laboratory test results to ultimately achieve automatic evaluation of Child-Pugh scores, Carprini scores, CNLC, ALBI, BCLC, and CNLC, and automatically generates diagnosis and treatment risk prompts in combination with a medical knowledge base, which can provide accurate diagnosis, reasonable treatment plans, and personalized treatment recommendations, helping doctors make scientific decisions and develop more appropriate treatment plans, thereby improving the quality of life of patients with liver cancer and improving the quality of medical care. Secondly, it avoids ineffective or inefficient treatments, reduces unnecessary medical costs, and at the same time assists doctors in optimizing the treatment sequence, improving the success rate of treatment, and further reducing medical costs. In addition, it can also help doctors process complex information, reduce their workload, and allow liver cancer specialists to have more time to focus on communicating and caring for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below in combination with the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0030] Figure 1 A framework diagram of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0031] Figure 2 This is a schematic diagram of the main interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0032] Figure 3 A schematic diagram of basic patient information displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0033] Figure 4 A schematic diagram of a patient's allergy history list information displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0034] Figure 5 A schematic diagram of patient diagnosis history information displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0035] Figure 6 A schematic diagram of patient surgical information displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0036] Figure 7 A schematic diagram of the patient's BCLC and CNLC staging information displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0037] Figure 8 A schematic diagram of a patient BCLC assessment pop-up window displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0038] Fig. 9 A schematic diagram of patient BCLC staging historical data displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0039] Fig.10 A schematic diagram of a patient CNLC assessment pop-up window displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0040] Fig.11A schematic diagram of historical data of CNLC staging of a patient displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0041] Fig.12 A schematic diagram of the TNM staging information of a patient displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0042] Fig.13 A schematic diagram of ALBI staging information of a patient displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0043] Fig.14 A schematic diagram of information related to Child-Pugh and Carprini assessment of a patient displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0044] Fig.15 A schematic diagram of a list of Child-Pugh assessment details of a patient displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0045] Fig.16 A schematic diagram of a patient's Carprini assessment details list displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0046] Fig.17 A schematic diagram of prompts for patient BCLC assessment items displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0047] Fig.18 A schematic diagram of prompts for CNLC evaluation items for patients displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors according to the present invention;

[0048] Fig.19 The present invention is a schematic diagram of prompts for Child-Pugh assessment items for patients displayed on an interface of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0050] Secondly, the present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0052] The present invention provides a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors, which can provide accurate diagnosis, reasonable treatment plans and personalized treatment suggestions, help doctors make scientific decisions and formulate more appropriate treatment plans, thereby improving the quality of life of liver cancer patients and improving the quality of medical care.

[0053] Figure 1 The schematic diagram of a clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to the present invention is shown in FIG. Figure 1 , a clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors in this embodiment includes a data layer, a data processing layer, a decision reasoning layer and an interaction layer.

[0054] The data layer has built-in electronic medical record database, medical knowledge base, imaging database and inspection database. Specifically, in this embodiment, the electronic medical record database stores the patient's basic information, medical history, symptoms and treatment records, the medical knowledge base contains the diagnostic criteria, treatment guidelines, drug information, clinical research data and expert experience of tumor diseases, and the imaging database stores various imaging examination images and reports of the patient.

[0055] The data processing layer is used to retrieve the data in the data layer, and extract the key indicators of the patient's tumor after pre-processing the data. Specifically, in this embodiment, the data processing layer includes a data cleaning module, a data integration module and a feature extraction module. The data cleaning module cleans, removes duplications and standardizes the data from different data sources; the data integration module integrates the standardized data into data in a unified format, and the feature extraction module extracts key features from the patient data, wherein the key features include tumor marker levels and various types of assessed imaging indicators, providing a basis for decision reasoning.

[0056] The decision reasoning layer obtains the key indicators of the patient's tumor extracted after processing by the data processing layer, and performs preliminary diagnosis and differential diagnosis of the tumor, recommends personalized treatment plans and risk assessment based on the key indicators of the patient's tumor. Specifically, in this embodiment, the decision reasoning layer includes a diagnostic reasoning engine, a treatment recommendation engine, and a risk assessment engine. The diagnostic reasoning engine performs preliminary diagnosis and differential diagnosis of the tumor based on the patient's symptoms, test results and diagnostic criteria in the medical knowledge base. The treatment recommendation engine recommends personalized treatment plans based on the patient's condition, physical condition and treatment guidelines, wherein the treatment plans include surgery, chemotherapy, radiotherapy and targeted therapy. The risk assessment engine assesses the patient's treatment risk and complication risk.

[0057] The interaction layer finally realizes the automatic evaluation of Child-Pugh score, Carprini score, CNLC, ALBI, BCLC, and CNLC based on the preliminary decision opinions of the decision reasoning layer, combined with the admission records and laboratory test results, and automatically generates diagnosis and treatment risk prompts in combination with the medical knowledge base. Specifically, in this embodiment, the interaction layer includes a doctor's workstation interface, and the doctor's workstation interface includes patient basic information, page layout settings, surgical information, staging, evaluation, ICD10 diagnosis settings, test results, diagnosis and treatment risk prompts, and imaging / pathology / microscopy results modules. The staging module includes a BCLC staging evaluation system, a CNLC staging evaluation system, a TNM staging evaluation system, and an ALBI staging evaluation system. The above liver cancer tumor staging is automatically generated through the patient's test data and imaging indicators. The evaluation module includes a Child-Pugh evaluation and a Carprini evaluation. The above risk assessments are automatically generated through the patient's test data and imaging indicators.

[0058] In order to introduce the workflow of the clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors in detail, Figure 2-Figure 19 Provide detailed explanation.

[0059] like Figure 2 The main interface of the clinical decision support system is shown in the figure. The page is divided into three areas: left, middle, and right. You can set the display content before and after surgery and in each area.

[0060] Left side: displays the patient's basic information, assessment stage and calculator. The BCLC, CNLC, TNM, ALBI, Child-Pugh score and Carprini score in the assessment stage can be automatically generated through the patient's examination and test data; middle: line chart of the patient's test focus indicators and the patient's historical medication list; right side: examination results and diagnosis and treatment risk prompts.

[0061] like Figure 3 As shown, the patient's basic information, allergy history and diagnosis history are displayed. The basic information includes name, gender, bed, age, diagnosis department and other information; if there is an allergy history, click it to pop up the allergy history list, displaying relevant drug skin test and time information, such as Figure 4 If there is a diagnosis history, click it to pop up the diagnosis information, including diagnosis type, diagnosis name, main diagnosis, suspected, infectious, outcome, outcome date, diagnosis department, diagnosis doctor, diagnosis date, etc. Figure 5 shown.

[0062] like Figure 6 As shown, after the patient undergoes surgery, the system will update the surgery information based on the connected electronic medical record and display relevant information, including surgery date, surgery name, preoperative diagnosis, postoperative diagnosis and other information.

[0063] like Figure 7 As shown, BCLC and CNLC staging information are displayed. The items associated with BCLC include PS score, vascular invasion, distant metastasis, number of tumors, and tumor size; the items associated with CNLC include PS score, extrahepatic metastasis, vascular invasion, number of tumors, and tumor size.

[0064] Click BCLC Details to enter the BCLC Assessment pop-up window. Fill in the relevant options to obtain the patient rating (including staging reference value, current staging value) and related treatment recommendations, such as Figure 8 As shown; click the historical curve button in the lower left corner to call up the historical data of BCLC staging, such as Fig. 9 shown.

[0065] Click CNLC Details to enter the CNLC Assessment pop-up window, fill in the relevant options to obtain the patient rating (including staging reference value, current staging value) and related treatment recommendations, such as Fig.10 As shown; click the historical curve button in the lower left corner to call up the historical data of CNLC installments, such as Fig.11 shown.

[0066] like Fig.12 As shown, it shows the TNM staging information. The items associated with TNM staging include T (Tumor) primary tumor, N (Node) regional lymph nodes, and M (Metastasis) distant metastasis.

[0067] like Fig.13 As shown, if the patient has test data, it will be automatically brought in and ALBI scoring will be performed and ALBI staging information will be displayed. ALBI-related items include total bilirubin, albumin and other information.

[0068] like Fig.14 As shown, Child-Pugh and Carprini assessment related information is displayed. Click on Child-Pugh assessment details to view related conclusions. Child-Pugh related items include hepatic encephalopathy, ascites assessment, bilirubin, albumin, and prothrombin. Fig.15 As shown; click on the Carprini assessment details to view the conclusion, where Carprini is associated with age, surgery, family history, constitution, malignant tumors, pregnancy or postpartum and other related items, such as Fig.16 shown.

[0069] The clinical decision support system for liver cancer tumor auxiliary diagnosis and treatment provided by the present invention combines medical entity and relationship extraction algorithms to complete the construction of CT / MRI image report medical record learning model. After connecting to HIS, LIS, PACS and other systems, it can automatically extract various types of imaging indicators for evaluation, and obtain admission records and laboratory test results. Doctors can view and read relevant information. Fig.17 , Fig.18 , Fig.19 As shown, click the relevant icon to display detailed information of the assessment item inspection data.

[0070] It should be understood that the various technical means mentioned above can be implemented separately in the form of hardware or software, or the integration of the two. Therefore, the method and apparatus of the present invention, as well as several constituent elements or aspects thereof, can be built into a physical medium, such as a program code (i.e., a series of instructions) carried by a floppy disk, CD-ROM, hard disk, or any other machine-readable storage medium. When this program is loaded onto a device such as a computer and executed, the device is converted into a tool for implementing the present invention.

[0071] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer tumors, characterized in that: include: Data layer, with built-in electronic medical record database, medical knowledge base, imaging database and inspection database; The data processing layer is used to retrieve the data in the data layer and extract the key indicators of the patient's tumor after preprocessing the data; The decision-making reasoning layer obtains the key indicators of the patient's tumor extracted after processing by the data processing layer, and makes a preliminary diagnosis and differential diagnosis of the tumor, recommends personalized treatment plans, and conducts risk assessment based on the key indicators of the patient's tumor. The interaction layer, based on the preliminary decision opinions of the decision reasoning layer, combines the admission records and laboratory test results to ultimately achieve automatic evaluation of the Child-Pugh score, Carprini score, CNLC, ALBI, BCLC, and CNLC, and automatically generates diagnosis and treatment risk prompts in combination with the medical knowledge base.

2. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to claim 1, characterized in that: In step S1, the electronic medical record database stores the patient's basic information, medical history, symptoms and treatment records; The medical knowledge base contains diagnostic criteria, treatment guidelines, drug information, clinical research data, and expert experience for tumor diseases; The imaging database stores various imaging examination images and reports of patients.

3. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to claim 1, characterized in that: The data processing layer includes a data cleaning module, a data integration module and a feature extraction module; The data cleaning module cleans, removes duplications, and standardizes data from different data sources; The data integration module integrates the standardized data into data in a unified format; The feature extraction module extracts key features from patient data, including tumor marker levels and various imaging indicators for evaluation, providing a basis for decision-making reasoning.

4. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to claim 1, characterized in that: The decision reasoning layer includes a diagnosis reasoning engine, a treatment recommendation engine, and a risk assessment engine; The diagnostic reasoning engine performs preliminary diagnosis and differential diagnosis of tumors based on the patient's symptoms, test results, and diagnostic criteria in the medical knowledge base; The treatment recommendation engine recommends personalized treatment plans based on the patient's condition, physical condition and treatment guidelines, where the treatment plans include surgery, chemotherapy, radiotherapy and targeted therapy; The risk assessment engine assesses a patient's treatment risk and risk of complications.

5. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to claim 1, characterized in that: The interactive layer includes a doctor workstation interface, which includes patient basic information, page layout settings, surgery information, staging, evaluation, ICD10 diagnosis settings, test results, diagnosis and treatment risk prompts, and imaging / pathology / microscopy results modules.

6. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to claim 5, characterized in that: The staging module includes the BCLC staging assessment system, the CNLC staging assessment system, the TNM staging assessment system and the ALBI staging assessment system. The above liver cancer tumor stagings are automatically generated through the patient's test data and imaging indicators.

7. A clinical decision support system for auxiliary diagnosis and treatment of liver cancer according to claim 5, characterized in that: The assessment modules include Child-Pugh assessment and Carprini assessment, and the above risk assessments are automatically generated through the patient's test data and imaging indicators.

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