Drug clinical evaluation method and system based on big data and artificial intelligence

Abstract

The invention belongs to the technical field of drug clinical evaluation, and particularly relates to a drug clinical evaluation method and system based on big data and artificial intelligence, and the method comprises the steps: S1, constructing a default evaluation template; constructing a user-defined index library; s2, constructing an evidence database; s3, establishing a mapping relationship between each drug evaluation index and the business field of each evidence database; s4, on the basis of the default evaluation template and the self-defined index library, establishing an evaluation index system of the medicine according to specific requirements of medicine evaluation; s5, based on the mapping relationship between each medicine evaluation index and the business field of each evidence database, obtaining the corresponding evaluation evidence of the medicine from the evidence database by using each medicine evaluation index of the evaluation index system; and S6, sending the evaluation index system of the to-be-evaluated medicine and the corresponding evaluation evidence to an evaluation end for evaluation, and obtaining a clinical evaluation result of the medicine. According to the method, the evaluation accuracy and efficiency can be improved, and the objectivity and consistency of evaluation results are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of drug clinical evaluation, and in particular relates to a drug clinical evaluation method and system based on big data and artificial intelligence. Background Art

[0002] Drug clinical evaluation plays a vital role in the modern medical system. It aims to ensure the safety, effectiveness, economy and suitability of drugs, and provide a scientific basis for drug registration, marketing, use and supervision. Through strict screening, this evaluation process can identify drugs with definite efficacy, high safety and reasonable economy, thus providing patients with better treatment options.

[0003] The results of drug evaluation play an important role throughout the entire life cycle of a drug. In the research and development stage, it provides a solid scientific basis for the development of new drugs; in the production stage, it guides the improvement of production processes and the improvement of quality control; in the circulation and use stage, it provides patients and medical institutions with reasonable medication recommendations to ensure the safety and effectiveness of medication; in the regulatory stage, it is an indispensable and important reference for drug registration, approval and supervision.

[0004] However, traditional drug evaluation methods, such as expert review and literature review, have many limitations. These methods are not only time-consuming and labor-intensive, but also easily affected by subjective factors, which leads to doubts about the accuracy and objectivity of the evaluation results. With the increasingly clear requirements of the National Health Commission for medical institutions in provinces and cities across the country to carry out comprehensive clinical drug evaluation, a more scientific, objective and efficient drug evaluation system is particularly important. However, my country has not yet formed a unified and specific standard for drug evaluation in medical institutions. When performing drug evaluation-related work, different provinces and cities often follow the standards of their respective regions, which leads to inconsistency and difficulty in comparing evaluation results. Major technical problems have also emerged: First, it is difficult to formulate drug evaluation standards. In medical institutions, clinical pharmacists often have to formulate evaluation standards based on their own experience and knowledge when conducting drug evaluations due to the lack of unified and standardized evaluation standards, which leads to subjectivity and inconsistency in evaluation results.

[0005] Second, it is difficult to collect and use evidence for drug evaluation. Drug evaluation requires a large amount of evidence, but this information is often scattered from different sources, which is very time-consuming and laborious to collect. At the same time, even if the information is collected, how to effectively use it for evaluation is also a challenge.

[0006] Third, the efficiency of drug evaluation system construction and data collection is low. The traditional drug evaluation process requires clinical pharmacists to spend a lot of time and energy to build an evaluation system and collect data, which not only affects the efficiency of the evaluation work, but may also lead to a decline in the quality of the evaluation results due to time constraints.

[0007] Therefore, how to further improve the drug clinical evaluation system, improve the accuracy and efficiency of the evaluation, and ensure the objectivity and consistency of the evaluation results has become a problem that needs to be solved urgently. Summary of the invention

[0008] In view of the above-mentioned deficiencies in the prior art, the present invention provides a drug clinical evaluation method based on big data and artificial intelligence, which can further improve the drug clinical evaluation system, improve the accuracy and efficiency of the evaluation, and ensure the objectivity and consistency of the evaluation results.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: A drug clinical evaluation method based on big data and artificial intelligence comprises the following steps: S1. Construct a default evaluation template; obtain authoritative drug evaluation reference data and perform content integration, data cleaning and structural standardization to obtain a default evaluation template; the default evaluation template includes drug evaluation indicators in multiple evaluation dimensions; Build a custom indicator library; disassemble the drug evaluation indicators of various evaluation guidelines and standards to form a custom indicator library for drugs; S2. Constructing an evidence database, wherein the evidence database stores evaluation evidence for evaluating various drug indicators; S3, acquiring each business field of the evidence database to form a field set, and establishing a mapping relationship between each drug evaluation index and the business field of each evidence database; the mapping relationship is used to automatically match the corresponding evaluation evidence according to the selected drug evaluation index; S4. Based on the default evaluation template and the custom indicator library, a drug evaluation indicator system is established according to the specific needs of drug evaluation; the evaluation indicator system includes various drug evaluation indicators used in drug clinical evaluation; S5. Based on the mapping relationship between each drug evaluation index and the business field of each evidence database, use each drug evaluation index of the evaluation index system to obtain the corresponding evaluation evidence of the drug from the evidence database; S6. Send the evaluation index system and corresponding evaluation evidence of the drug to be evaluated to the review end for review to obtain the final drug clinical evaluation results; Among them, S1 and S2 can be swapped in order.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. This method can improve the evaluation efficiency of drug clinical evaluation. By constructing a default evaluation template and a custom indicator library, this method provides a standardized and normalized framework for drug clinical evaluation. This makes the evaluation process more efficient because evaluators can quickly build an evaluation indicator system based on these templates and libraries without having to design evaluation indicators from scratch. At the same time, by establishing a mapping relationship between evaluation indicators and evaluation evidence, evaluators can quickly obtain the required evaluation evidence, further shortening the evaluation cycle.

[0011] In order to solve the problem of difficulty in formulating drug evaluation standards, this method provides a standardized drug evaluation template by introducing the "template management" function. These templates are built based on authoritative drug evaluation reference data to ensure the scientificity and objectivity of the evaluation standards. In order to solve the problem of difficulty in collecting and using drug evaluation evidence, this method constructs an "evaluation evidence" database and establishes a mapping relationship between each drug evaluation indicator and the business field of the evidence database, so that clinical pharmacists can easily and automatically match the corresponding evaluation evidence according to the selected evaluation indicator. In order to solve the problem of low efficiency in drug evaluation system construction and data collection. This method provides a one-stop service, integrating functions such as template management, evaluation evidence database, and mapping relationship establishment to form a complete drug evaluation system, which greatly improves work efficiency.

[0012] Through "template management" and "evaluation evidence", this method solves the problem that clinical pharmacists in medical institutions are difficult to formulate drug evaluation standards and have difficulty collecting and using evidence materials related to drug evaluation in their actual work. Through field evaluation, the above functions can greatly reduce the duration of their evaluation work and improve efficiency by up to 90%, achieving the universality, functional versatility and user convenience of drug clinical evaluation standards in a true sense. This method greatly improves the work efficiency of users. Through one-stop service, it scientifically and effectively solves the difficulties users face in building a drug evaluation system and collecting drug evaluation materials.

[0013] 2. By building a default evaluation template, authoritative drug evaluation reference data is integrated to ensure the scientificity and objectivity of the evaluation criteria. The establishment of a custom indicator library allows the evaluation indicators to be flexibly adjusted according to specific needs, enhancing the pertinence and practicality of the evaluation.

[0014] 3. The storage of the evidence database provides rich and comprehensive evidence for the evaluation, ensuring the adequacy and accuracy of the evaluation. By establishing a mapping relationship between each drug evaluation index and the business field of the evidence database, the automatic matching of evaluation evidence is achieved, greatly improving the efficiency of the evaluation. Using each drug evaluation index of the evaluation index system, the corresponding evaluation evidence of the drug is automatically obtained from the evidence database through the mapping relationship, avoiding the tedious process of manually collecting evidence and improving the efficiency and accuracy of the evaluation.

[0015] 4. This method can enhance the accuracy of evaluation. This method uses big data and artificial intelligence technology to obtain and integrate drug evaluation reference data from authoritative channels, ensuring the accuracy and reliability of evaluation information. In addition, by building an evidence database and accurately matching evaluation indicators with evaluation evidence, evaluators (through the review end) can conduct drug evaluation based on more comprehensive and objective evidence, thereby improving the accuracy and scientificity of the evaluation.

[0016] 5. This method can promote evaluation standardization. By building a standardized default evaluation template and a custom indicator library, this method helps promote the standardization process of drug clinical evaluation. This helps reduce subjectivity and differences in the evaluation process and improve the comparability and consistency of evaluation results.

[0017] In summary, this method can further improve the drug clinical evaluation system, improve the accuracy and efficiency of the evaluation, and ensure the objectivity and consistency of the evaluation results.

[0018] Preferably, in S1, the evaluation dimensions include effectiveness, safety, economy, accessibility, adaptability, innovation, pharmaceutical properties, and other attributes.

[0019] These dimensions not only cover the core attributes of drugs, but also involve their comprehensive performance in the market and social environment, so as to evaluate the value of drugs more comprehensively and deeply. Together, these dimensions constitute a comprehensive and detailed drug clinical evaluation framework.

[0020] Preferably, in S1, the authoritative drug evaluation reference data includes policy guidance issued by relevant national competent authorities, drug evaluation standards formulated by leading national pharmaceutical experts, and drug evaluation standards formulated by health departments of provinces and cities across the country.

[0021] Such settings, these authoritative drug evaluation reference data have a significant effect on building default evaluation templates and ensuring the accuracy and authority of evaluations. The inclusion of these data not only improves the accuracy and reliability of evaluations, but also promotes the standardization and automation of evaluations.

[0022] Preferably, in S1, authoritative drug evaluation reference data is also updated at a frequency of once a month, and processed to update the default evaluation template.

[0023] With such a setting, as new drug evaluation reference data and evaluation guidelines emerge, this method supports continuous updating and optimization of the default evaluation template and custom indicator library. This enables the evaluation process to keep up with the latest scientific progress and evaluation standards, ensuring the timeliness and accuracy of the evaluation results.

[0024] Preferably, in S1, the content of data cleaning includes: data deduplication processing, merging similar data according to established principles, and comparing new and old data.

[0025] Such a setting can ensure the accuracy, consistency and completeness of the data after cleaning the original collected data, which is conducive to the subsequent structural specification work and provides strong support for building the default evaluation template and conducting subsequent evaluation work.

[0026] Preferably, in S2, the constructed evidence database includes multiple databases such as a drug instructions database, a drug evidence-based analysis database, and an adverse reaction database.

[0027] Such a setting, by integrating information from multiple databases, can provide comprehensive evidence support for the evaluation and avoid one-sided or subjective evaluation results. In this way, the comprehensiveness and objectivity of the evaluation can be improved, providing a more scientific and reliable basis for the clinical application and promotion of drugs.

[0028] Preferably, the evidence database includes full-chain data of the smart pharmacy industry, including global research and development, approval for marketing, rational drug use, adverse reactions, and evidence-based analysis.

[0029] With this setup, the full chain data of the smart pharmacy industry included in the evidence database has a significant effect on improving the scientificity and accuracy of decision-making, optimizing rational drug use and patient management, strengthening drug safety monitoring and risk management, and promoting the development and application of evidence-based medicine. This comprehensive and in-depth data collection provides strong support for the development and application of the smart pharmacy industry.

[0030] Preferably, in S4, the process of building an evaluation index system includes: First, according to the specific needs of drug evaluation, select a suitable default evaluation template and screen the drug evaluation indicators in the default evaluation template; Afterwards, based on the missing drug evaluation indicators after screening, select the corresponding drug evaluation indicators in the custom indicator library; Finally, the drug evaluation indicators filtered by the default evaluation template are combined with the drug evaluation indicators selected in the custom indicator library to obtain the drug evaluation indicator system.

[0031] Such settings can, 1. Improve the comprehensiveness and accuracy of the evaluation. According to the specific needs of drug evaluation, select the appropriate default evaluation template to ensure the scientificity and authority of the basic framework and core indicators of the evaluation. This helps to avoid omissions and deviations in the evaluation process and improve the comprehensiveness and accuracy of the evaluation. Screen the drug evaluation indicators in the default evaluation template, remove unnecessary or duplicate indicators, and retain the core and key indicators. For the missing drug evaluation indicators after screening, select the corresponding indicators in the custom indicator library to supplement, ensuring the completeness and pertinence of the evaluation.

[0032] 2. It can enhance the flexibility and adaptability of the evaluation. The custom indicator library provides a wealth of evaluation indicators to choose from, which can be flexibly combined and adjusted according to different evaluation needs. This enables the evaluation indicator system to be customized according to different drug characteristics and evaluation objectives, enhancing the flexibility and adaptability of the evaluation. With the changes in drug research and development and market environment, the evaluation indicators may need to be updated and adjusted accordingly. By combining the custom indicator library with the default evaluation template, it is easy to dynamically update and optimize the evaluation indicators to ensure the timeliness and accuracy of the evaluation system.

[0033] 3. It can improve evaluation efficiency and operability. The process of building an evaluation index system simplifies the evaluation process and reduces unnecessary duplication of work by screening and combining evaluation indicators. This helps to improve evaluation efficiency, reduce evaluation costs, and provide strong support for the rapid launch and clinical application of drugs. In addition, the evaluation index system is presented in a structured and standardized form, making the evaluation process clearer, more intuitive, and easier to operate. This helps evaluators quickly understand and grasp the evaluation requirements and improve the accuracy and consistency of the evaluation.

[0034] Preferably, in S6, the evaluation index system and corresponding evaluation evidence of the drug to be evaluated are sent to multiple review terminals for review, and after the review results of the multiple review terminals are processed, the final drug clinical evaluation result is obtained.

[0035] Such a setting can, 1. Improve the objectivity and fairness of the evaluation. By sending the evaluation index system and evaluation evidence to multiple review parties, it can ensure that the evaluation process is supervised by multiple parties and reduce the bias or subjectivity that may exist in a single review party. The diversity and comprehensiveness of the review results will help to form a more objective and fair evaluation conclusion.

[0036] 2. It can enhance the comprehensiveness and accuracy of the evaluation. Multiple review terminals evaluate drugs from different angles and levels, which can fully cover multiple dimensions such as drug safety, effectiveness, economy, innovation, suitability and accessibility. This comprehensive evaluation helps to form a comprehensive understanding of drugs and provide a scientific basis for the rational use of drugs.

[0037] The present invention also provides a drug clinical evaluation system based on big data and artificial intelligence, which is used for the above-mentioned drug clinical evaluation method based on big data and artificial intelligence, including a cloud end, a local end, and a review end; The cloud is used to obtain authoritative drug evaluation reference data; the cloud is also used to store default evaluation templates, custom indicator libraries and evidence databases; the default evaluation templates include drug evaluation indicators of multiple evaluation dimensions; the custom indicator library is obtained by disassembling drug evaluation indicators of various evaluation guidelines and standards; the evidence database stores evaluation evidence for evaluating various drug indicators; the cloud is also used to store the mapping relationship between each drug evaluation indicator and the business field of each evidence database; the mapping relationship is used to automatically match the corresponding evaluation evidence according to the selected drug evaluation indicator; The review end is used to build a drug evaluation index system based on the default evaluation template and the custom index library according to the specific needs of drug evaluation; the evaluation index system includes various drug evaluation indicators used in drug clinical evaluation; The cloud is also used to obtain the corresponding evaluation evidence of the drug from the evidence database using the evaluation index system of each drug based on the mapping relationship between each drug evaluation index and the business field of each evidence database; and send the evaluation index system of the drug to be evaluated and the corresponding evaluation evidence to the review end for review; The review end is used to conduct clinical evaluation of drugs based on the evaluation index system and corresponding evaluation evidence received for the drugs to be evaluated.

[0038] Such a setting, using this system, can further improve the drug clinical evaluation system, improve the accuracy and efficiency of the evaluation, and ensure the objectivity and consistency of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which: Figure 1 This is a flow chart of the drug clinical evaluation method based on big data and artificial intelligence of the present invention; Figure 2 Schematic diagram of drug evaluation indicators of other attributes in the default evaluation template in the embodiment; Figure 3 Schematic diagram of drug evaluation indicators of other attributes in the evaluation indicator system exemplified in the embodiment; Figure 4 This is a framework diagram of the drug clinical evaluation system based on big data and artificial intelligence of the present invention; Figure 5 This is a schematic diagram of the home page interface in the evaluation index system used as an example in the embodiment; Figure 6 This is a schematic diagram of a function selection interface in an evaluation index system exemplified in the embodiment; Figure 7 This is a schematic diagram of an interface for selecting drugs in the evaluation index system exemplified in the embodiment; Figure 8 A schematic diagram of an evaluation template interface in an evaluation index system exemplified in the embodiment; Fig. 9 Schematic diagram of the drug clinical evaluation result interface in the evaluation index system exemplified in the embodiment. DETAILED DESCRIPTION

[0040] The following is a further detailed description through specific implementation methods: Example: like Figure 1 As shown, this embodiment discloses a drug clinical evaluation method based on big data and artificial intelligence, comprising the following steps: S1. Construct a default evaluation template; obtain authoritative drug evaluation reference data and perform content integration, data cleaning and structural standardization to obtain a default evaluation template.

[0041] The contents of data cleaning include: data deduplication, merging similar data according to established principles, and comparing old and new data. In this way, after cleaning the original collected data, the accuracy, consistency and integrity of the data can be guaranteed, which is conducive to the subsequent structural specification work and provides strong support for the construction of the default evaluation template and subsequent evaluation work.

[0042] The authoritative drug evaluation reference data include policy guidance issued by relevant national authorities, drug evaluation standards formulated by national top pharmaceutical experts, and drug evaluation standards formulated by provincial and municipal health departments across the country. In this way, these authoritative drug evaluation reference data have a significant effect on building a default evaluation template and ensuring the accuracy and authority of the evaluation. The inclusion of these data not only improves the accuracy and reliability of the evaluation, but also promotes the standardization and automation of the evaluation. In specific implementation, the authoritative drug evaluation reference data is updated at a monthly update frequency, and processed to update the default evaluation template. With such a setting, with the emergence of new drug evaluation reference data and evaluation guidelines, this method supports continuous updating and optimization of the default evaluation template and custom indicator library. This enables the evaluation process to keep up with the latest scientific progress and evaluation standards, ensuring the timeliness and accuracy of the evaluation results.

[0043] The default evaluation template includes drug evaluation indicators in multiple evaluation dimensions. In specific implementation, the evaluation dimensions include effectiveness, safety, economy, accessibility, adaptability, innovation, pharmaceutical properties, and other attributes. These dimensions not only cover the core attributes of the drug, but also involve its comprehensive performance in the market and social environment, so as to evaluate the value of the drug more comprehensively and in-depth. These dimensions together constitute a comprehensive and detailed drug clinical evaluation framework.

[0044] Build a custom indicator library; disassemble the drug evaluation indicators of various evaluation guidelines and standards to form a custom indicator library for drugs.

[0045] S2. Construct an evidence database, in which evaluation evidence for evaluating various drug indicators is stored.

[0046] In specific implementation, the evidence database constructed includes multiple databases such as the drug instructions database, drug evidence-based analysis database, and adverse reaction database. In this way, by integrating the information of multiple databases, comprehensive evidence support can be provided for the evaluation, avoiding one-sided or subjective evaluation results. In this way, the comprehensiveness and objectivity of the evaluation can be improved, providing a more scientific and reliable basis for the clinical application and promotion of drugs.

[0047] The evidence database contains data on the entire chain of the smart pharmacy industry, including global research and development, approval for marketing, rational use of drugs, adverse reactions, and evidence-based analysis. In this way, the data on the entire chain of the smart pharmacy industry included in the evidence database has a significant effect on improving the scientificity and accuracy of decision-making, optimizing rational use of drugs and patient management, strengthening drug safety monitoring and risk management, and promoting the development and application of evidence-based medicine. This comprehensive and in-depth data collection provides strong support for the development and application of the smart pharmacy industry.

[0048] S3. Acquire each business field of the evidence database to form a field set, and establish a mapping relationship between each drug evaluation indicator and the business field of each evidence database; the mapping relationship is used to automatically match the corresponding evaluation evidence according to the selected drug evaluation indicator.

[0049] In this way, all drug evaluation indicators are linked to the corresponding databases in pairs, the business fields include retrieval and precise matching, and different databases show different mapping relationships based on different businesses. Finally, when the user formulates any drug evaluation indicator under the system, the background can automatically recommend the corresponding evaluation evidence, which is highly directional and accurate.

[0050] S4. Based on the default evaluation template and the custom indicator library, a drug evaluation indicator system is established according to the specific needs of drug evaluation; the evaluation indicator system includes various drug evaluation indicators used in drug clinical evaluation.

[0051] In specific implementation, the process of building an evaluation index system includes: First, according to the specific needs of drug evaluation, select a suitable default evaluation template and filter the drug evaluation indicators in the default evaluation template. Figure 2 As shown, the figure shows the schematic diagram of the drug evaluation indicators of other attributes in the two default evaluation templates. By browsing the drug evaluation indicators contained in each default evaluation template, a suitable default evaluation template can be selected relatively quickly and accurately.

[0052] Afterwards, based on the missing drug evaluation indicators after screening, select the corresponding drug evaluation indicators in the custom indicator library; Finally, the drug evaluation indexes selected by the default evaluation template are combined with the drug evaluation indexes selected in the custom index library to obtain the drug evaluation index system. Figure 3 As shown, the figure is a schematic diagram of drug evaluation indicators of other attributes in an example evaluation indicator system.

[0053] In this way, the comprehensiveness and accuracy of the evaluation can be improved. According to the specific needs of drug evaluation, select a suitable default evaluation template to ensure the scientificity and authority of the basic framework and core indicators of the evaluation. This helps to avoid omissions and deviations in the evaluation process and improve the comprehensiveness and accuracy of the evaluation. Screen the drug evaluation indicators in the default evaluation template, remove unnecessary or repeated indicators, and retain the core and key indicators. For the missing drug evaluation indicators after screening, select the corresponding indicators in the custom indicator library to supplement, ensuring the completeness and pertinence of the evaluation. In addition, the flexibility and adaptability of the evaluation can be enhanced. The custom indicator library provides a wealth of evaluation indicators for selection, which can be flexibly combined and adjusted according to different evaluation needs. This enables the evaluation indicator system to be customized according to different drug characteristics and evaluation objectives, enhancing the flexibility and adaptability of the evaluation. With the changes in drug research and development and market environment, the evaluation indicators may need to be updated and adjusted accordingly. Through the combined use of the custom indicator library and the default evaluation template, the dynamic update and optimization of the evaluation indicators can be easily realized to ensure the timeliness and accuracy of the evaluation system.

[0054] Moreover, it can also improve the evaluation efficiency and operability. The process of building an evaluation index system simplifies the evaluation process and reduces unnecessary duplication of work by screening and combining evaluation indicators. This helps to improve evaluation efficiency, reduce evaluation costs, and provide strong support for the rapid launch and clinical application of drugs. In addition, the evaluation index system is presented in a structured and standardized form, making the evaluation process clearer, more intuitive, and easier to operate. This helps evaluators quickly understand and grasp the evaluation requirements and improve the accuracy and consistency of the evaluation.

[0055] S5. Based on the mapping relationship between each drug evaluation index and the business field of each evidence database, each drug evaluation index of the evaluation index system is used to obtain corresponding evaluation evidence of the drug from the evidence database.

[0056] S6. Send the evaluation index system and corresponding evaluation evidence of the drug to be evaluated to the review end for review to obtain the final drug clinical evaluation results.

[0057] In specific implementation, the evaluation index system and corresponding evaluation evidence of the drug to be evaluated are sent to multiple review terminals for review, and the review results of multiple review terminals are processed to obtain the final drug clinical evaluation results. In specific implementation, the specific method of processing the review results of multiple review terminals can be based on familiar data processing methods by those skilled in the art, such as removing the highest score / lowest score and then calculating the average, etc., which will not be repeated here.

[0058] In this way, the objectivity and fairness of the evaluation can be improved. By sending the evaluation index system and evaluation evidence to multiple review ends, it can ensure that the evaluation process is supervised by multiple parties and reduce the bias or subjectivity that may exist in a single review end. The diversity and comprehensiveness of the review results help to form a more objective and fair evaluation conclusion. In addition, it can also enhance the comprehensiveness and accuracy of the evaluation. Multiple review ends evaluate drugs from different angles and levels, which can fully cover multiple dimensions such as drug safety, effectiveness, economy, innovation, suitability and accessibility. This comprehensive evaluation helps to form a comprehensive understanding of drugs and provide a scientific basis for the rational use of drugs.

[0059] Among them, S1 and S2 can be swapped in order.

[0060] This method can improve the evaluation efficiency of drug clinical evaluation. By constructing default evaluation templates and custom indicator libraries, this method provides a standardized and normalized framework for drug clinical evaluation. This makes the evaluation process more efficient because evaluators can quickly build an evaluation indicator system based on these templates and libraries without having to design evaluation indicators from scratch. At the same time, by establishing a mapping relationship between evaluation indicators and evaluation evidence, evaluators can quickly obtain the required evaluation evidence, further shortening the evaluation cycle. For the problem of difficulty in formulating drug evaluation standards, this method provides a standardized drug evaluation template by introducing the "template management" function. These templates are built based on authoritative drug evaluation reference data to ensure the scientificity and objectivity of the evaluation standards. For the problem of difficulty in collecting and using drug evaluation evidence data, this method constructs an "evaluation evidence" database and establishes a mapping relationship between each drug evaluation indicator and the business field of the evidence database, so that clinical pharmacists can easily automatically match the corresponding evaluation evidence according to the selected evaluation indicator. For the problem of low efficiency in drug evaluation system construction and data collection. This method provides a one-stop service, integrating functions such as template management, evaluation evidence database, and mapping relationship establishment to form a complete drug evaluation system, which greatly improves work efficiency. Through "template management" and "evaluation evidence", this method solves the problem that clinical pharmacists in medical institutions are difficult to formulate drug evaluation standards and have difficulty collecting and using evidence materials related to drug evaluation in their actual work. Through field evaluation, the above functions can greatly reduce the duration of their evaluation work and improve efficiency by up to 90%, achieving the universality, functional versatility and user convenience of drug clinical evaluation standards in a true sense. This method greatly improves the work efficiency of users. Through one-stop service, it scientifically and effectively solves the difficulties users face in building a drug evaluation system and collecting drug evaluation materials.

[0061] In addition, this method integrates authoritative drug evaluation reference data by constructing a default evaluation template, ensuring the scientificity and objectivity of the evaluation criteria. The establishment of a custom indicator library allows the evaluation indicators to be flexibly adjusted according to specific needs, enhancing the pertinence and practicality of the evaluation. In addition, the storage of the evidence database provides rich and comprehensive evidence information for the evaluation, ensuring the adequacy and accuracy of the evaluation. By establishing a mapping relationship between each drug evaluation indicator and the business field of the evidence database, the automatic matching of the evaluation evidence is realized, greatly improving the efficiency of the evaluation. Using each drug evaluation indicator of the evaluation indicator system, the corresponding evaluation evidence of the drug is automatically obtained from the evidence database through the mapping relationship, avoiding the tedious process of manually collecting evidence and improving the efficiency and accuracy of the evaluation. At the same time, this method can enhance the accuracy of the evaluation. This method uses big data and artificial intelligence technology to obtain and integrate drug evaluation reference data from authoritative channels, ensuring the accuracy and reliability of the evaluation information. In addition, by constructing an evidence database and accurately matching the evaluation indicators with the evaluation evidence, the evaluator (through the review end) can conduct drug evaluation based on more comprehensive and objective evidence, thereby improving the accuracy and scientificity of the evaluation. In addition, this method can promote evaluation standardization. By building a standardized default evaluation template and a custom indicator library, this method helps promote the standardization of drug clinical evaluation, which helps reduce subjectivity and differences in the evaluation process and improves the comparability and consistency of evaluation results.

[0062] This method can further improve the drug clinical evaluation system, improve the accuracy and efficiency of the evaluation, and ensure the objectivity and consistency of the evaluation results.

[0063] like Figure 4 As shown, the present invention also provides a drug clinical evaluation system based on big data and artificial intelligence, which is used for the above-mentioned drug clinical evaluation method based on big data and artificial intelligence, including a cloud, a local end, and a review end. In specific implementation, the cloud can be a cloud database, and the local end and the review end can select a personal smart terminal, such as a PC or a smart tablet.

[0064] The cloud is used to obtain authoritative drug evaluation reference data; the cloud is also used to store default evaluation templates, custom indicator libraries and evidence databases; the default evaluation templates include drug evaluation indicators of multiple evaluation dimensions; the custom indicator library is obtained by disassembling drug evaluation indicators of various evaluation guidelines and standards; the evidence database stores evaluation evidence for evaluating various drug indicators; the cloud is also used to store the mapping relationship between each drug evaluation indicator and the business field of each evidence database; the mapping relationship is used to automatically match the corresponding evaluation evidence according to the selected drug evaluation indicator; The review end is used to build a drug evaluation index system based on the default evaluation template and the custom index library according to the specific needs of drug evaluation; the evaluation index system includes various drug evaluation indicators used in drug clinical evaluation; The cloud is also used to obtain the corresponding evaluation evidence of the drug from the evidence database using the evaluation index system of each drug based on the mapping relationship between each drug evaluation index and the business field of each evidence database; and send the evaluation index system of the drug to be evaluated and the corresponding evaluation evidence to the review end for review; The review end is used to conduct clinical evaluation of drugs based on the evaluation index system and corresponding evaluation evidence received for the drugs to be evaluated.

[0065] As an example, Figures 5 to 9 A set of implementation example interface diagrams of a drug clinical evaluation system based on the framework of the present invention are shown. Figure 5 This is the home page interface of the drug clinical evaluation system in this example; Figure 6 This is the interface after entering the function selection page from the homepage of the drug clinical evaluation system in this example. The example system provides two service functions: "drug comprehensive evaluation" and "drug rapid evaluation". The "drug comprehensive evaluation" service function provides a relatively comprehensive drug evaluation result, and the "drug rapid evaluation" service function provides a faster drug evaluation result with slightly simplified evaluation items; Figure 7 An example interface of a list of selectable drugs that can provide evaluation and analysis in the drug clinical evaluation system of this example is shown; Figure 8 The example interface of providing selectable evaluation templates and presetting and creating evaluation templates in the drug clinical evaluation system of this example is shown; Fig. 9 The example interface of the drug clinical evaluation system of this example for the drug clinical evaluation results of the drug "Telmisartan Tablets" is shown. Through this group of implementation example interface diagrams of the drug clinical evaluation system, the operation process of the evaluation service function provided by the drug clinical evaluation system of the present invention for drug clinical evaluation is presented to a certain extent, as well as the user interface status presented by various functions and information in the process links.

[0066] It can be seen that the use of this drug clinical evaluation system can further improve the drug clinical evaluation system, and combined with the aforementioned technical content introduction and technical advantage description of this application scheme, it can be known that the drug clinical evaluation system of the present invention can effectively improve the accuracy and efficiency of the evaluation and ensure the objectivity and consistency of the evaluation results.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A drug clinical evaluation method based on big data and artificial intelligence, characterized in that: The following steps are involved: S1. Build a default evaluation template; Obtain authoritative drug evaluation reference data and perform content integration, data cleaning and structural standardization to obtain a default evaluation template; the default evaluation template includes drug evaluation indicators in multiple evaluation dimensions; Build a custom indicator library; disassemble the drug evaluation indicators of various evaluation guidelines and standards to form a custom indicator library for drugs; S2. constructing an evidence database, wherein the evidence database stores evaluation evidence for evaluating various drug indicators; S3, acquiring each business field of the evidence database to form a field set, and establishing a mapping relationship between each drug evaluation index and the business field of each evidence database; the mapping relationship is used to automatically match the corresponding evaluation evidence according to the selected drug evaluation index; S4. Based on the default evaluation template and the custom indicator library, a drug evaluation indicator system is established according to the specific needs of drug evaluation; the evaluation indicator system includes various drug evaluation indicators used in drug clinical evaluation; S5. Based on the mapping relationship between each drug evaluation index and the business field of each evidence database, use each drug evaluation index of the evaluation index system to obtain the corresponding evaluation evidence of the drug from the evidence database; S6. Send the evaluation index system and corresponding evaluation evidence of the drug to be evaluated to the review end for review to obtain the final drug clinical evaluation results; Among them, S1 and S2 can be swapped in order.

2. The drug clinical evaluation method based on big data and artificial intelligence as claimed in claim 1, characterized in that: In S1, the evaluation dimensions include effectiveness, safety, economy, accessibility, adaptability, innovation, pharmaceutical properties, and other attributes.

3. The drug clinical evaluation method based on big data and artificial intelligence as claimed in claim 2, characterized in that: In S1, the authoritative drug evaluation reference data include policy guidance issued by relevant national authorities, drug evaluation standards formulated by leading national pharmaceutical experts, and drug evaluation standards formulated by health departments of provinces and cities across the country.

4. The drug clinical evaluation method based on big data and artificial intelligence as claimed in claim 3, characterized in that: In S1, authoritative drug evaluation reference data are also updated at a monthly frequency and processed to update the default evaluation template.

5. The drug clinical evaluation method based on big data and artificial intelligence as claimed in claim 1, characterized in that: In S1, data cleaning includes: data deduplication, merging similar data according to established principles, and comparing new and old data.

6. The drug clinical evaluation method based on big data and artificial intelligence according to claim 1, characterized in that: In S2, the evidence database constructed includes multiple databases such as the drug instructions database, the drug evidence-based analysis database, and the adverse reaction database.

7. The drug clinical evaluation method based on big data and artificial intelligence according to claim 6, characterized in that: The evidence database contains full-chain data of the smart pharmacy industry, including global research and development, approval for marketing, rational use of drugs, adverse reactions, and evidence-based analysis.

8. The drug clinical evaluation method based on big data and artificial intelligence as claimed in claim 1, characterized in that: In S4, the process of building an evaluation index system includes: First, according to the specific needs of drug evaluation, select a suitable default evaluation template and screen the drug evaluation indicators in the default evaluation template; Afterwards, based on the missing drug evaluation indicators after screening, select the corresponding drug evaluation indicators in the custom indicator library; Finally, the drug evaluation indicators filtered by the default evaluation template are combined with the drug evaluation indicators selected in the custom indicator library to obtain the drug evaluation indicator system.

9. The drug clinical evaluation method based on big data and artificial intelligence according to claim 1, characterized in that: In S6, the evaluation index system and corresponding evaluation evidence of the drug to be evaluated are sent to multiple review ends for review, and after the review results of multiple review ends are processed, the final drug clinical evaluation result is obtained.

10. A drug clinical evaluation system based on big data and artificial intelligence, characterized by: The drug clinical evaluation method based on big data and artificial intelligence as described in claim 1, comprising a cloud end, a local end, and a review end; The cloud is used to obtain authoritative drug evaluation reference data; the cloud is also used to store default evaluation templates, custom indicator libraries and evidence databases; the default evaluation templates include drug evaluation indicators of multiple evaluation dimensions; the custom indicator library is obtained by disassembling drug evaluation indicators of various evaluation guidelines and standards; the evidence database stores evaluation evidence for evaluating various drug indicators; the cloud is also used to store the mapping relationship between each drug evaluation indicator and the business field of each evidence database; the mapping relationship is used to automatically match the corresponding evaluation evidence according to the selected drug evaluation indicator; The review end is used to build a drug evaluation index system based on the default evaluation template and the custom index library according to the specific needs of drug evaluation; the evaluation index system includes various drug evaluation indicators used in drug clinical evaluation; The cloud is also used to obtain the corresponding evaluation evidence of the drug from the evidence database using the evaluation index system of each drug based on the mapping relationship between each drug evaluation index and the business field of each evidence database; and send the evaluation index system of the drug to be evaluated and the corresponding evaluation evidence to the review end for review; The review end is used to conduct clinical evaluation of drugs based on the evaluation index system and corresponding evaluation evidence received for the drugs to be evaluated.

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