Intelligent decision support system for antiviral treatment of newly diagnosed HIV infected person

Abstract

The invention belongs to the technical field of medical information, and particularly relates to a newly diagnosed HIV infected person antiviral treatment intelligent decision support system which comprises a multi-source data integration module, a medicine knowledge module, an individualized decision support module, an interactive output module, a knowledge optimization module and a safety and privacy protection module. The system replaces doctors to query and integrate patient data across multiple systems, clinical time is saved, key information omission is avoided, comprehensive data support is provided for treatment decisions, experience decisions are converted into systematic analysis and graded risk prompts are generated by means of an individualized decision support module, scheme deviation is effectively reduced, the medication risk is reduced, and the treatment efficiency is improved. The method assists the basic level in improving the treatment normalization, allows doctors to input new experience and new achievements summarized in practice into the system after double-blind auditing, and provides continuously iterative knowledge support for the scientificity of the antiviral treatment decision of the newly diagnosed HIV infected person.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of medical information technology, and particularly relates to an intelligent decision support system for antiviral therapy of newly diagnosed HIV infectors. BACKGROUND

[0002] Antiviral therapy of human immunodeficiency virus (HIV) infection is the core means to curb disease progression and reduce the risk of transmission, and the selection of the initial treatment plan for newly diagnosed infectors is the key starting point to determine long-term efficacy and safety. However, there are still significant bottlenecks in current clinical practice. The baseline clinical data of patients, such as CD4 cell count, viral load, liver and kidney function indicators, and previous medical history, such as drug allergy history and comorbidities, are scattered in multiple independent platforms such as electronic medical records, laboratory systems, and physical examination reports. These data lack an automatic integration mechanism, and doctors need to manually cross-platform query, filter, and summarize information, which not only occupies a lot of clinical time, but also is more likely to miss key details due to human negligence, posing a hidden danger to decision-making.

[0003] More importantly, newly diagnosed HIV infectors are often in the early stage of the disease, and their treatment plan needs to be closely combined with individual characteristics such as liver and kidney function, allergy history, and comorbidities. For example, for patients with renal dysfunction, if the abnormal creatinine clearance rate is not identified in time, the anti-HIV drugs with nephrotoxicity may be misselected or the dosage is not adjusted, thereby causing adverse reactions. The judgment of these complex associations highly depends on the personal experience and knowledge reserve of doctors in the traditional decision-making mode, and it is difficult to ensure the consistency of drug conflict judgment in different medical scenarios. In particular, due to lack of experience, such problems are more prominent in primary medical institutions, directly leading to a higher risk of medication safety for newly diagnosed HIV patients. SUMMARY

[0004] In view of the above deficiencies in the prior art, the present application provides an intelligent decision support system for antiviral therapy of newly diagnosed HIV infectors to solve the problems in the background art.

[0005] In order to solve the above technical problems, the present application adopts the following technical solutions: an intelligent decision support system for antiviral therapy of newly diagnosed HIV infectors, comprising: a multi-source data integration module for interfacing with multiple data source systems, obtaining and integrating structured data and unstructured data of the multiple data source systems to form a standardized patient data file; a drug knowledge module for storing pharmacological properties, drug interaction rules, drug resistance mutation site association data, and contraindication information of anti-HIV drugs, and supporting dynamic updating; An individualized decision support module, connected with the multi-source data integration module and the drug knowledge module respectively, is used to receive patient data archives, call information of the drug knowledge module, combine patient individual factors, and generate drug conflict prompts; An interactive output module, connected with the multi-source data integration module and the individualized decision support module respectively, is used to receive and display patient data archives and drug conflict prompts, and is provided with a new HIV drug rule input interface; A knowledge optimization module, connected with the interactive output module and the drug knowledge module respectively, is used to receive new HIV drug rules input by the interactive output module, and update the new HIV drug rules that pass the audit to the drug knowledge module.

[0006] Further, the multi-source data integration module includes a data cleaning unit, a standardization unit, and a structured conversion unit; The data cleaning unit is used to pre-process structured data and unstructured data obtained from multiple data source systems, and the pre-processing includes removing duplicate data and correcting abnormal values; The standardization unit is used to map the pre-processed structured data to a unified data standard, and the unified data standard includes field naming and numerical unit; The structured conversion unit uses natural language processing technology to analyze the pre-processed unstructured data, extracts key information through entity recognition and relationship extraction, and converts it into structured labels conforming to the unified data standard, the structured labels are bound to the unique identification of the patient, and the key information includes allergens in drug allergy history, comorbidity types, and previous antiviral treatment history.

[0007] Further, the data source system at least includes an electronic medical record system, a laboratory information system, and a physical examination system.

[0008] Further, the structured data includes routine blood biochemical indicators and demographic data, and the unstructured data includes patient self-reported medical history, imaging report description, and doctor's handwritten medical history notes.

[0009] Further, the routine blood biochemical indicators include CD4 cell count, HIV viral load, γ-glutamyltransferase, amylase, lipase, total bilirubin, albumin, and blood glucose; and the demographic data includes age, gender, ethnicity, body mass index, and smoking history.

[0010] Further, the drug interaction rules in the drug knowledge module include the conflict relationship between anti-HIV drugs and other therapeutic drugs in metabolic pathways and side effects, and the drug resistance mutation site association data includes the corresponding relationship between HIV virus mutation sites and drug sensitivity.

[0011] Further, the individualized decision support module comprises a patient feature extraction unit, a knowledge base query unit, a conflict analysis and determination unit, and a risk prompt generation unit. The patient feature extraction unit is configured to extract patient individuality factors from a patient data archive, wherein the patient individuality factors at least include liver and kidney function indicators, drug allergy history, a list of combined drugs, HIV virus genotype, and CD4 cell count. The knowledge base query unit is configured to initiate a query to the drug knowledge module based on the patient individuality factors, and screen out matched drug interaction rules, contraindication data, allergen cross-reaction data, and drug resistance sensitivity data. The conflict analysis and determination unit is configured to compare and analyze the patient individuality factors with the screened drug rules and data, and determine the adaptability of the drug to the liver and kidney function, the conflict of the drug with the allergy history, and the interaction risk between the combined drugs. The risk prompt generation unit is configured to divide the conflict analysis results into three levels of disable, use with caution, and monitor use according to a preset threshold based on the conflict analysis results, and generate a drug conflict prompt.

[0012] Further, the interactive output module displays the patient data archive and the drug conflict prompt in a visual interface, and highlights the drug conflict prompt through color coding.

[0013] Further, the knowledge optimization module comprises a rule input unit, a rule review unit, a structured processing unit, and an update unit. The rule input unit is configured to receive new HIV drug rules input by the interactive output module. The rule review unit adopts a double-blind review mechanism to professionally review the new HIV drug rules, and generates review opinions. The structured processing unit is configured to convert the new HIV drug rules that pass the review into a structured format recognizable by the drug knowledge module. The update unit is configured to update the structured new HIV drug rules to the drug knowledge module.

[0014] Further, the system further comprises a security and privacy protection module connected with the multi-source data integration module, the drug knowledge module, the individualized decision support module, the interactive output module, and the knowledge optimization module, wherein the security and privacy protection module is configured to perform full-process encryption processing and access log recording on the patient data.

[0015] Compared with the prior art, the present application has the following beneficial effects: 1. Through the data cleaning unit, standardization unit and structured conversion unit of the multi-source data integration module, the structured data and unstructured data scattered in the independent platforms such as electronic medical record system, laboratory information system and physical examination system are removed repeated data, abnormal values are corrected, field naming and numerical unit are unified, and unstructured data is converted into standardized structured label through natural language processing technology, and finally complete patient data archives are formed, which replaces the traditional doctor cross-platform query and integration, not only saves clinical time, but also avoids missing of key information caused by human negligence, and provides comprehensive and consistent data support for individualized decision-making; 2. Through the individualized decision support module, the core features such as liver and kidney function, allergic history and combined medication of the patient are extracted, and combined with the dynamic rules of the drug knowledge module, the multi-dimensional conflict analysis is used to generate the hierarchical prompts of forbidden use, cautious use and monitoring use, the traditional decision-making mode depending on personal experience is converted into a systematic and rule-based analysis process, which effectively reduces the scheme deviation caused by experience difference and reduces the risk of medication errors, especially suitable for improving the treatment standardization of primary medical institutions; 3. The drug knowledge module supports dynamic updating, and through the "entry-double-blind review-structuring-updating" process of the knowledge optimization module, the new drug rules are quickly included in the system after professional verification, and the adaptability of the system to clinical progress is improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The composition schematic diagram of the intelligent decision support system for antiviral therapy of newly diagnosed HIV infected persons of the present application; Figure 2 The composition schematic diagram of the multi-source data integration module in the intelligent decision support system for antiviral therapy of newly diagnosed HIV infected persons of the present application; Figure 3 The composition schematic diagram of the individualized decision support module in the intelligent decision support system for antiviral therapy of newly diagnosed HIV infected persons of the present application; Figure 4 The composition schematic diagram of the knowledge optimization module in the intelligent decision support system for antiviral therapy of newly diagnosed HIV infected persons of the present application. DETAILED DESCRIPTION

[0017] In order for those skilled in the art to better understand the present application, the technical solutions of the present application are further described below in combination with the drawings and examples.

[0018] Among them, the drawings are only used for example description, and the representation is only a schematic diagram, not a physical diagram, and cannot be understood as a limitation of the patent; in order to better illustrate the embodiments of the present application, some components of the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some known structures and their descriptions in the drawings can be omitted.

[0019] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it is understood that if the terms "upper", "lower", "left", "right", "inner", "outer" and the like indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration and cannot be understood as a limitation of the present patent, for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0020] In the description of the present application, unless otherwise explicitly specified and limited, if the term "connection" and the like indicating the connection relationship between components appears, the term should be interpreted broadly, for example, it can be a fixed connection, or a detachable connection, or an integral; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium; it can be a communication inside two components or an interaction relationship between two components. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0021] Embodiment one: As shown in the present application, specifically, the intelligent decision support system for antiviral treatment of newly diagnosed HIV infected persons comprises: Figures 1-4 A multi-source data integration module for connecting multiple data source systems, acquiring and integrating structured data and unstructured data of the multiple data source systems, forming a standardized patient data file, wherein the data source systems at least include an electronic medical record system, a laboratory information system and a physical examination system; A drug knowledge module for storing pharmacological properties of anti-HIV drugs, drug interaction rules, drug resistance mutation site correlation data and contraindication information, and supporting dynamic updating; An individualized decision support module connected with the multi-source data integration module and the drug knowledge module respectively, for receiving patient data files, calling information of the drug knowledge module, combining patient individual factors, and generating drug conflict prompts; An interactive output module connected with the multi-source data integration module and the individualized decision support module respectively, for receiving and displaying patient data files and drug conflict prompts, and provided with a new HIV drug rule input interface; A knowledge optimization module connected with the interactive output module and the drug knowledge module respectively, for receiving new HIV drug rules input by the interactive output module, and updating the new HIV drug rules that pass the audit to the drug knowledge module; ​A security and privacy protection module is connected with the multi-source data integration module, the drug knowledge module, the individualized decision support module, the interactive output module and the knowledge optimization module, and is used for full-process encryption processing and access log recording of patient data.

[0022] In the embodiments, the intelligent decision support system for antiviral treatment of newly diagnosed HIV infected persons realizes intelligent support for treatment decision of the newly diagnosed HIV infected persons through a closed loop process of data integration, drug knowledge support, drug conflict prompt, interactive feedback, knowledge iteration and security protection: the multi-source data integration module automatically connects with the electronic medical record system, the laboratory information system and the physical examination system, ensures that the system can obtain complete diagnosis and treatment information of the patient, avoids one-sidedness of the decision due to incomplete data sources, improves comprehensiveness of the scheme, and solves the problems of low efficiency and easy omission of information in manual integration by cleaning, standardizing and structuring the conversion, integrating the structured data and the unstructured data into a complete standardized patient file; the individualized decision support module extracts individual characteristics such as liver and kidney function and allergy history based on patient data, calls information such as dynamic drug rules and drug resistance association of the drug knowledge module, generates a hierarchical drug conflict prompt through multi-dimensional analysis, and breaks through the limitations of traditional decision-making relying on experience and poor consistency; the interactive output module displays data and prompts in a visual interface and provides a new rule input interface, facilitating doctors to efficiently obtain information and feedback clinical experience; the knowledge optimization module updates the new rules to the drug knowledge module through auditing, solving the lag problem of the static knowledge base; the security and privacy protection module runs through the whole process, encrypts and records the patient data, eliminates the risk of sensitive information leakage, and shortens the time for the doctor to make a treatment decision for the newly diagnosed HIV infected person, improves the drug safety, and provides efficient, scientific and safe intelligent support for the treatment of the newly diagnosed HIV infected person.

[0023] The multi-source data integration module includes a data cleaning unit, a standardization unit and a structured conversion unit. The data cleaning unit is configured to pre-process the structured data and the unstructured data obtained from the plurality of data source systems, and the pre-processing includes eliminating duplicate data and correcting abnormal values. The standardization unit is configured to map the pre-processed structured data to a unified data standard, and the unified data standard includes field naming and numerical unit. The structured conversion unit uses natural language processing technology to analyze the pre-processed unstructured data, extracts key information through entity recognition and relationship extraction, and converts the key information into structured labels conforming to the unified data standard, the structured labels are bound to the medical record number of the unique identification of the patient, and the key information includes allergens in drug allergy history, types of comorbidities and previous antiviral treatment history.

[0024] In the embodiments, the three-level processing mechanism of data cleaning, standardization and structured conversion effectively solves the problems of heterogeneous structured data and non-structured data, reduces the omissions and errors of manual integration, ensures the integrity and consistency of patient data archives, and provides a reliable data basis for subsequent individualized decision-making.

[0025] The structured data includes routine blood biochemical indicators and demographic data, and the non-structured data includes patient self-reported medical history, imaging report description and doctor's handwritten medical history notes.

[0026] In the embodiments, by distinguishing the structured and non-structured data types and targeted processing, the accuracy of structured data is preserved, and the implicit information obtained from patient self-reports or doctor's experience is mined by analyzing non-structured data, ensuring the comprehensiveness of patient feature extraction.

[0027] The routine blood biochemical indicators include CD4 cell count, HIV viral load, gamma-glutamyl transferase, amylase, lipase, total bilirubin, albumin and blood glucose, and the demographic data includes age, gender, ethnicity and body mass index, smoking history.

[0028] In the first assessment of newly diagnosed HIV infected patients, the clinic is more accustomed to giving priority to core indicators directly related to viral control and immune status, such as CD4 cell count, HIV viral load and total bilirubin, while gamma-glutamyl transferase, amylase and lipase are often classified as secondary indicators. Especially when the patient has no obvious symptoms, such as abdominal pain and jaundice, gamma-glutamyl transferase is often mistaken as only reflecting alcohol consumption or biliary obstruction, and its value as an early signal of drug-induced liver damage is ignored. Amylase and lipase are often limited to the exclusion of pancreatitis in patients with abdominal pain, and their role in predicting the risk of drug-induced pancreatic damage in asymptomatic patients is ignored. Therefore, the routine blood biochemical indicators including gamma-glutamyl transferase, amylase and lipase reflect the early risk warning of drug toxicity target organs such as liver and gallbladder and pancreas, especially for the underlying problems of asymptomatic patients, which can help doctors avoid high-risk drugs and adjust treatment plans to reduce the incidence of adverse reactions from the source, and make up for the blank of monitoring drug toxicity target organs that is easily ignored in traditional evaluation.

[0029] The drug interaction rules in the drug knowledge module include the conflict relationship between anti-HIV drugs and other therapeutic drugs in metabolic pathways and side effects, such as the competition relationship between anti-HIV drugs and antihypertensive drugs in CYP3A4 enzyme metabolism. The drug resistance mutation site association data includes the corresponding relationship between HIV virus mutation sites and drug sensitivity, such as the corresponding relationship between HIV K103N mutation and efavirenz sensitivity reduction, which provides accurate risk warning basis for doctors and reduces the risk of adverse drug reactions and treatment failure.

[0030] The individualized decision support module comprises a patient characteristic extraction unit, a knowledge base query unit, a conflict analysis and determination unit, and a risk prompt generation unit. The patient characteristic extraction unit is configured to extract patient individuality factors from a patient data archive, the patient individuality factors comprising at least liver and kidney function indicators, drug allergy history, a list of combined drugs, HIV virus genotype, and CD4 cell count. The knowledge base query unit is configured to initiate a query to a drug knowledge module based on the patient individuality factors, and screen matched drug interaction rules, contraindication data, allergen cross-reaction data, and drug resistance sensitivity data, such as screening drug contraindication rules for patients with renal insufficiency. The conflict analysis and determination unit is configured to compare and analyze the patient individuality factors with the screened drug rules and data, and determine the adaptability of the drug to the liver and kidney function of the patient, the conflict of the drug with the allergy history, and the interaction risk between the combined drugs, such as the fact that tenofovir has a significant toxicity risk when the patient's creatinine clearance rate is 25 mL / min, because the kidney metabolism burden exceeds the patient's tolerance range. The risk prompt generation unit is configured to divide the conflict analysis results into three levels of disable, use with caution, and monitor use according to a preset threshold based on the conflict analysis results, and generate drug conflict prompts, such as determining high-risk drugs metabolized by the kidney as "disable" when the creatinine clearance rate is less than 30 mL / min.

[0031] In an embodiment, through the standardized decision support process of the four-unit cooperation, the traditional subjective judgment relying on experience is converted into systematic and traceable objective analysis, the problem of low consistency in decision-making in different medical scenarios is solved, and the hierarchical prompts of risk disable, use with caution, and monitor use more directly assist doctors to avoid high-risk solutions, especially improving the decision-making standardization of primary medical institutions.

[0032] The interaction output module displays the patient data archive and the drug conflict prompts in a visual interface, and highlights the drug conflict prompts through color coding, such as using red for disable prompts, yellow for use with caution prompts, and blue for monitor use prompts.

[0033] In an embodiment, the visual interface and color coding reduce the information acquisition cost of doctors, directly locate high-risk information through visual impact, help quickly focus on core conflicts, avoid missing key conflict prompts in massive data, shorten information extraction time, and adapt to the fast-paced decision-making needs of clinics.

[0034] The knowledge optimization module comprises a rule input unit, a rule review unit, a structured processing unit, and an update unit. The rule input unit is configured to receive new HIV drug rules input by the interactive output module; The rule auditing unit is configured to generate auditing opinions by adopting a double-blind auditing mechanism to professionally audit the new HIV drug rules; The structured processing unit is configured to convert the new HIV drug rules that pass the auditing into a structured format recognizable by the drug knowledge module; The updating unit is configured to update the structured new HIV drug rules to the drug knowledge module.

[0035] In the embodiments, through the closed-loop process of inputting, double-blind auditing, structuring, and updating, the clinician is allowed to input new experiences and new achievements summarized in practice into the system, and the new rules that pass the assessment of at least two independent HIV treatment experts are updated to the drug knowledge base module, which ensures the timeliness and reliability of the drug knowledge and realizes the dynamic synchronization of the system and the clinical practice, thereby providing continuous iterative knowledge support for the scientificity of the antiviral treatment decision for new HIV infected persons and avoiding the lag of the drug knowledge module.

[0036] Although the embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

[0037] The above is only an embodiment of the present application, and the circuit and electronic components and modules are all prior art, and those skilled in the art can implement them without further description. The content protected by the present application does not involve the improvement of software and methods. The specific structures and characteristics of the known scheme and other common knowledge are not described in detail here. Those skilled in the art know all the common technical knowledge in the field of the present application before the filing date or the priority date, can obtain all the prior art in the field, and have the ability to apply conventional experimental means before the date. Those skilled in the art can improve and implement the present scheme based on their own ability under the guidance of the present application. Some typical known structures or known methods should not be an obstacle for those skilled in the art to implement the present application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present application, and these should be considered as the protection scope of the present application, which will not affect the effect and practicality of the patent.

Claims

1. An intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients, characterized in that: include: The multi-source data integration module is used to connect to multiple data source systems, acquire and integrate structured and unstructured data from multiple data source systems, and form standardized patient data archives. The drug knowledge module stores the pharmacological properties of anti-HIV drugs, drug interaction rules, data on drug resistance mutation sites, and contraindications information, and supports dynamic updates. The personalized decision support module is connected to the multi-source data integration module and the drug knowledge module, respectively. It is used to receive patient data files, call the information of the drug knowledge module, and generate drug conflict prompts by combining the patient's individual factors. The interactive output module is connected to the multi-source data integration module and the individualized decision support module respectively. It is used to receive and display patient data files and drug conflict prompts, and has a new HIV drug rule input interface. The knowledge optimization module is connected to both the interactive output module and the drug knowledge module. It is used to receive new HIV drug rules entered by the interactive output module and update the approved new HIV drug rules to the drug knowledge module.

2. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, The multi-source data integration module includes a data cleaning unit, a standardization unit, and a structured transformation unit; The data cleaning unit is used to preprocess structured and unstructured data obtained from multiple data source systems. The preprocessing includes removing duplicate data and correcting outliers. The standardization unit is used to map preprocessed structured data to a unified data standard, which includes field naming and numerical units; The structured conversion unit uses natural language processing technology to parse the preprocessed unstructured data, extracts key information through entity recognition and relation extraction, and converts it into structured tags that conform to unified data standards. The structured tags are bound to the patient's unique identifier, enabling association and storage with the standardized structured data. The key information includes allergens, comorbidity types, and previous antiviral treatment history in the history of drug allergies.

3. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, The data source system includes at least an electronic medical record system, a laboratory information system, and a physical examination system.

4. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 3, characterized in that, The structured data includes routine blood biochemistry indicators and demographic data, while the unstructured data includes patient self-reported medical history, imaging report descriptions, and doctors' handwritten medical notes.

5. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 4, characterized in that, The routine blood biochemical indicators include CD4 cell count, HIV viral load, gamma-glutamyl transferase, amylase, lipase, total bilirubin, albumin, and blood glucose; the demographic data include age, sex, ethnicity, body mass index, and smoking history.

6. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, The drug interaction rules in the drug knowledge module include the conflict relationships between anti-HIV drugs and other therapeutic drugs in terms of metabolic pathways and toxic side effects, and the drug resistance mutation site association data includes the correspondence between HIV virus mutation sites and drug sensitivity.

7. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, The individualized decision support module includes a patient feature extraction unit, a knowledge base query unit, a conflict analysis and judgment unit, and a risk warning generation unit. The patient feature extraction unit is used to extract individual patient factors from patient data files. These individual patient factors include at least liver and kidney function indicators, drug allergy history, list of concomitant medications, HIV genotype, and CD4 cell count. The knowledge base query unit is used to initiate queries to the drug knowledge module based on individual patient factors, and filter out matching drug interaction rules, contraindication data, allergen cross-reaction data, and drug resistance sensitivity data. The conflict analysis and determination unit is used to compare and analyze the patient's individual factors with the selected drug rules and data to determine the compatibility of the drug with the patient's liver and kidney function, the conflict between the drug and the history of allergies, and the risk of interaction between concomitant medications. The risk warning generation unit is used to divide the conflict analysis results into three levels—prohibited, used with caution, and monitored—according to a preset threshold, and generate drug conflict warnings.

8. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, The interactive output module displays patient data files and drug conflict alerts in a visual interface, and highlights drug conflict alerts using color coding.

9. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, The knowledge optimization module includes a rule input unit, a rule review unit, a structured processing unit, and an update unit; The rule input unit is used to receive new HIV drug rules input by the interactive output module; The rule review unit employs a double-blind review mechanism to conduct professional reviews of the new HIV drug rules and generate review opinions. The structured processing unit is used to convert the approved new HIV drug rules into a structured format that the drug knowledge module can recognize; The update unit is used to update the structured new HIV drug rules to the drug knowledge module.

10. The intelligent decision support system for antiretroviral treatment of newly diagnosed HIV-infected patients as described in claim 1, characterized in that, It also includes a security and privacy protection module, which is connected to the multi-source data integration module, drug knowledge module, personalized decision support module, interactive output module and knowledge optimization module. The security and privacy protection module is used to perform full-process encryption processing and access log recording of patient data.

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