An intelligent diagnostic analysis method and system

Abstract

The application discloses an intelligent diagnosis analysis method and system, and relates to the technical field of intelligent medical information processing.The method comprises the following steps: determining whether a diagnosis conclusion can be obtained according to disease information of a patient, a relationship between the disease information and diseases, and a diagnosis analysis model; if yes, obtaining a recommended diagnosis conclusion according to the disease information of the patient, the relationship between the disease information and the diseases, and the diagnosis analysis model; if no, obtaining a possible disease range of the patient according to the disease information of the patient, obtaining a recommended diagnosis scheme according to the possible disease range of the patient and a relationship between diseases and diagnosis schemes, and diagnosing the patient by using the recommended diagnosis scheme; and the next judgment is performed, which can help medical personnel and the patient to quickly and accurately obtain a scientific diagnosis conclusion, avoids the phenomenon that the diagnosis conclusion is inaccurate and the diagnosis scheme is unreasonable, and effectively improves the accuracy and timeliness of diagnosis.

Description

[0001] This application is a divisional application of a patent application entitled "An Intelligent Diagnostic Analysis System and Its Usage Method", the original application was filed on July 12, 2021, and the application number is 202110787847.9. Technical Field

[0002] This invention relates to the field of intelligent medical information processing technology, and in particular to an intelligent diagnostic analysis method and system. Background Technology

[0003] Diagnosis refers to the conclusion a doctor draws based on patient and disease information regarding their health status. The accuracy of the diagnosis directly impacts effective treatment, the rational allocation of medical resources, and patient safety and health; it is crucial for ensuring medical quality, controlling unreasonable medical costs, and protecting patient rights. During the diagnostic process, developing an appropriate diagnostic plan based on the patient's condition is essential for prompt diagnosis and targeted treatment. Common methods for obtaining patient information include history taking, physical examination, blood tests, and urine tests. With the advancement of hospital equipment, chest X-rays, electrocardiograms, and ultrasound examinations are now considered standard methods for obtaining information by most doctors. While the emergence and widespread use of various diagnostic methods can provide more information in the early stages, they can sometimes place unnecessary burdens on patients. Furthermore, inappropriate diagnostic methods can lead to unnecessary risks and harm to patients, delaying timely treatment. Therefore, the appropriateness of the diagnostic plan is also related to effective treatment, the rational allocation of medical resources, and patient safety and health.

[0004] During the diagnostic process, physicians should analyze the patient's possible diseases based on their clinical condition and individualized information, formulate relevant diagnostic plans according to diagnostic needs, obtain relevant information, and provide accurate diagnostic conclusions. Simultaneously, they should consider factors such as the applicability, contraindications, precautions, adverse reactions, time, cost, potential safety hazards, and usage limitations of various diagnostic methods involved in the diagnostic plan, selecting appropriate diagnostic methods or combinations thereof, excluding diagnostic methods that pose serious harm to the patient, and fully assessing the risks and weighing the necessity of diagnostic methods that pose potential risks to the patient. Furthermore, the patient's medical insurance conditions and financial capacity, as well as the suitability of the diagnostic plan, should be considered. Only by comprehensively considering the above factors can the optimal diagnostic goal be achieved, quickly and accurately determining the patient's disease condition and fully protecting the patient's rights. However, in actual diagnostic processes, due to insufficient understanding of the specific content of disease diagnosis by medical personnel or human error, inaccurate diagnostic conclusions and unreasonable diagnostic plans may occur. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent diagnostic analysis method and system that can help medical personnel and patients quickly and accurately arrive at scientific diagnostic conclusions, avoid inaccurate diagnostic conclusions and unreasonable diagnostic plans, and effectively improve the accuracy and timeliness of diagnosis.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] An intelligent diagnostic analysis method includes:

[0008] Obtain the patient's disease information;

[0009] Based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model, it is determined whether a diagnostic conclusion can be drawn; the diagnostic conclusion includes the likelihood / probability of the disease, its severity, and its urgency.

[0010] If so, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model;

[0011] If not, then based on the patient's disease information, the possible range of the patient's diseases is obtained, and based on the possible range of the patient's diseases and the relationship between the diseases and the diagnostic plan, a recommended diagnostic plan is obtained; the patient is diagnosed using the recommended diagnostic plan to obtain new disease information, and the new disease information is fused with the patient's disease information to obtain fused disease information. The fused disease information is used as the patient's disease information for the next iteration, and the process returns to the step of "determining whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model".

[0012] An intelligent diagnostic analysis system includes:

[0013] Information acquisition unit, used to acquire the patient's disease information;

[0014] The analysis unit is used to determine whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and a diagnostic analysis model. The diagnostic conclusion includes the likelihood / probability, severity, and urgency of the disease. If yes, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model. If no, the possible range of the patient's disease is obtained based on the patient's disease information, and a recommended diagnostic plan is obtained based on the possible range of the patient's disease and the relationship between the disease and the diagnostic plan. The patient is diagnosed using the recommended diagnostic plan to obtain new disease information, and the new disease information is fused with the patient's existing disease information to obtain fused disease information. The fused disease information is used as the patient's disease information for the next iteration, and the process returns to the step of "determining whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and a diagnostic analysis model".

[0015] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0016] This invention provides an intelligent diagnostic analysis method and system. Based on the patient's disease information, the relationship between the disease information and the disease, and a diagnostic analysis model, it determines whether a diagnostic conclusion can be drawn. If so, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model. If not, the possible range of the patient's disease is determined based on the patient's disease information, and a recommended diagnostic plan is obtained based on the possible range of the patient's disease and the relationship between the disease and the diagnostic plan. The recommended diagnostic plan is used to diagnose the patient, obtaining new disease information. This new disease information is then fused with the patient's existing disease information to obtain fused disease information for the next assessment. This method helps medical personnel and patients quickly and accurately arrive at scientific diagnostic conclusions, avoiding inaccurate diagnostic conclusions and unreasonable diagnostic plans, effectively improving the accuracy and timeliness of diagnosis. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the method flow for an intelligent diagnostic analysis method provided in Embodiment 1 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The purpose of this invention is to provide an intelligent diagnostic analysis method and system that can help medical personnel and patients quickly and accurately arrive at scientific diagnostic conclusions, avoid inaccurate diagnostic conclusions and unreasonable diagnostic plans, and effectively improve the accuracy and timeliness of diagnosis.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Example 1

[0023] like Figure 1 As shown, an intelligent diagnostic analysis method in this embodiment includes:

[0024] S1: Obtain the patient's disease information.

[0025] S2: Determine whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model; the diagnostic conclusion includes the possibility / probability of the disease, its severity, and its urgency.

[0026] S3: If so, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model.

[0027] S4: If not, then based on the patient's disease information, obtain the possible range of the patient's diseases, and based on the possible range of the patient's diseases and the relationship between the diseases and the diagnostic plan, obtain a recommended diagnostic plan; use the recommended diagnostic plan to diagnose the patient, obtain new disease information for the patient, and fuse the new disease information with the patient's disease information to obtain fused disease information. Use the fused disease information as the patient's disease information for the next iteration, and return to the step of "determining whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model".

[0028] Diagnosis is a process by which doctors, based on a patient's clinical condition and individualized information, directly provide a diagnostic conclusion or, through a diagnostic plan, continuously gather new disease information until a diagnosis can be made. Simultaneously, when obtaining new disease information through a diagnostic plan, it is necessary to consider factors such as the applicability, contraindications, precautions, adverse reactions, time, cost, potential safety hazards, and usage limitations of various diagnostic methods. Appropriate diagnostic methods or combinations thereof are selected to form a diagnostic plan. Diagnostic methods may include: examination, inquiry, observation, testing, monitoring, laboratory testing, experiments, and surgery (to obtain diagnostic tissue), etc.

[0029] This embodiment has a pre-built diagnostic database, which includes: the relationship between disease information and disease, the relationship between disease and diagnostic plan, and relevant rules on the rationality and compliance of diagnostic plan.

[0030] (a) The relationship between disease information and disease:

[0031] In this embodiment, the relationship between disease information and disease includes: possible diseases corresponding to different disease information, and disease information required to diagnose a disease.

[0032] The relationship between disease information and disease is determined by comprehensively considering the balance between the possibility / probability of the disease, its severity, urgency, differences in treatment plans, and treatment costs. This is because the more information obtained, the higher the accuracy of the diagnosis, but the higher the cost; conversely, the less information obtained, the lower the accuracy of the diagnosis. Therefore, the same disease information can correspond to multiple different diseases or combinations of diseases, each with different accuracy, risks, and diagnostic / treatment costs (including time and financial costs). Different diagnostic strategies and analytical methods can be determined under different circumstances by pre-configuring or configuring different parameters through big data analysis.

[0033] The diagnostic strategy and analysis methods can include: setting analytical items such as the likelihood / probability of the disease, its severity, urgency, differences in treatment plans, and treatment costs; comprehensively considering each analytical item; and finding a suitable balance among them to achieve the best overall effect. Comprehensive consideration of each analytical item can be achieved by balancing different elements of each item through pre-set parameters, or by modifying and iterating the pre-set parameters according to actual conditions to ensure the balance of different elements of each analytical item under actual circumstances. The setting, modification, and iteration of relevant parameters can be derived by experts based on experience or by artificial intelligence based on big data analysis.

[0034] For example, disease information may correspond to multiple possible diseases or disease ranges. While it's possible to continuously acquire more disease information through various diagnostic methods to increase the accuracy of diagnosis and rule out impossible diseases, the cost of acquiring more information increases significantly, the probability of acquisition decreases, and new risks may arise, such as delayed diagnosis leading to missed optimal treatment opportunities. Therefore, simply maximizing the acquisition of more disease information for a more accurate diagnosis without considering other analytical aspects is unrealistic in actual medical practice. Therefore, empirical treatment should be developed based on limited disease information, taking into full account the time dimension, and comprehensively considering factors such as the possibility / probability of the disease, its severity, urgency, differences in treatment plans, and treatment costs. This empirical treatment essentially involves establishing interim conclusions and measures for possible diseases (especially severe cases) before a definitive diagnosis. Simultaneously, new disease information (such as new examination / test results, new symptoms, new sensations, etc.) is continuously acquired during treatment based on these interim conclusions. By incorporating and analyzing this new information into existing data, new conclusions can be drawn, allowing for timely adjustments to the diagnostic conclusions and treatment plans. When a conclusion can be reached based on the patient's disease information using different diagnostic models and different balance parameters, it is considered that a diagnostic analysis result can be given.

[0035] (II) Relationship between disease and diagnostic protocol:

[0036] In this embodiment, the relationship between disease and diagnostic plan includes: the appropriate diagnostic plan required for different diseases or disease ranges.

[0037] The relationship between disease and diagnostic plan is determined by comprehensively considering the balance between the probability / severity of the disease, its severity, urgency, differences in treatment plans, and treatment costs. In other words, just as with the relationship between disease information and the disease, the relationship between disease and diagnostic plan also needs to comprehensively consider the probability / severity of the disease, its severity, urgency, differences in treatment plans, and treatment costs, and find a suitable balance among these factors to achieve the best overall effect.

[0038] A diagnostic plan can be a static set of one or more plans, or a dynamic diagnostic path. A static plan is a fixed diagnostic approach that adopts corresponding fixed diagnostic methods or combinations thereof for various clinical conditions of the patient. A dynamic plan is developed step-by-step during the diagnostic process, continuously adjusting and modifying the next step or adjusting existing plans based on new information obtained from previous steps. It can also be based on different stages of disease development and progression, patient information, manifestations or combinations thereof, disease progress / changes during treatment / treatment attempts, and new disease information obtained, selecting different diagnostic paths or recommending new diagnostic plans based on this information. In this embodiment, the scope, number, and intensity of problems to be addressed at each step of the diagnostic plan can be set.

[0039] A diagnostic plan is a scheme to arrive at a diagnosis by acquiring new disease information through various diagnostic methods, based on the patient's disease information, corresponding diseases or combinations of diseases, and individual patient circumstances. The establishment of a diagnostic plan can involve obtaining a large number of diagnostic examples, collecting combinations of elements from various dimensions, creating a dataset, assessing for common characteristics, and then using various relevant statistical methods and big data monitoring to establish diagnostic plans for combinations of elements with common characteristics.

[0040] (III) Relevant rules regarding the rationality and compliance of diagnostic plans:

[0041] The rules governing the rationality and compliance of diagnostic protocols include: rules concerning applicability, contraindications, precautions, interactions, allergies, timing, methods of administration / use, dosage, adverse reactions, preparation / protection measures, suitability / comfort / compliance, medical insurance / benefits, economic control, and administrative rules. Other rules related to the rationality and compliance of diagnostic protocols may also be included.

[0042] Applicability-related rules and elements may include: the necessity, level, degree, ranking, and effectiveness of the diagnostic protocol's applicability; applicability evaluation for patients / operators / doctors / pharmacists / nurses / caregivers; the rationality and compliance of the applicable population; the rationality and compliance of the applicable purpose; the rationality and compliance of the indications / contraindications / applicable conditions; and various factors affecting applicability and their relationship with applicability.

[0043] The relevant rules and elements of contraindications may include: the reasons for the contraindication of the diagnostic protocol, the relationship between the various factors that may affect the contraindication and the contraindication, the guidance and tips / warnings for the relevant patients / operators / doctors / nurses / pharmacists / caregivers, the consequences of the above-mentioned contraindications, the level, degree, order, and incidence of the corresponding contraindications, the contraindication-related evaluations for patients / operators / doctors / pharmacists / nurses / caregivers, and the discovery and remedial measures for the above-mentioned contraindications.

[0044] The relevant rules and elements requiring caution / attention may include: the reasons why the diagnostic plan requires caution or attention; the relationship between various factors that may affect caution or attention and caution or attention; guidance / warning information for relevant patients / operators / doctors / nurses / pharmacists / caregivers; the level, degree, order, probability of occurrence, degree of danger of caution / attention; the possible consequences of the above situations; the evaluation of caution or attention by patients / operators / doctors / pharmacists / nurses / caregivers; and the discovery and remedial measures for the relevant consequences of the above situations.

[0045] The rules of interaction refer to the rules governing the interaction or mutual influence between the diagnostic methods (drugs, medical devices, examinations, surgeries, etc.) contained in the diagnostic protocol itself, or between the diagnostic protocol and other drugs, surgeries, medical examinations and tests, diets, health products, cosmetics, medical devices, procedures, etc. In other words, if there is an interaction or mutual influence, the diagnostic protocol should be avoided or carefully selected, and special attention should be paid to monitoring and emergency care during the process.

[0046] The relevant rules and elements of interaction may include: situations where the diagnostic protocol may interact / influence each other with other factors or items such as drugs / medical devices / examinations / surgery / chemotherapy / radiotherapy / phototherapy / thermotherapy / magnetic therapy / electrotherapy / electromagnetic therapy / sound therapy / immunotherapy / gene therapy / operations / physical therapy / rehabilitation / health care / exercise / psychological intervention / health products / health foods / cosmetics / diet, etc., when they coexist / are being carried out simultaneously; the influence of the respective sites of action / pathways / methods, intervals / dosages / times, etc., of the diagnostic protocol and related drugs / medical devices / examinations / surgery / chemotherapy / radiotherapy / phototherapy / thermotherapy / magnetic therapy / electrotherapy / electromagnetic therapy / sound therapy / immunotherapy / gene therapy / operations / physical therapy / rehabilitation / health care / exercise / psychological intervention / health products / health foods / cosmetics / diet on the above-mentioned interactions / influences; and the potential influence of other factors that may affect the above-mentioned interactions / influences on the interactions.

[0047] Other factors that may influence the above interactions / mutual influences include: patient genetic factors, lifestyle factors, dietary factors, disease / treatment history factors, family medical history, allergies, work / study / exercise / activity factors, environmental factors, physical / psychological / learning / sleep / exercise / emotion / metabolism / vision / hearing / intelligence / attention / appetite / immunity / growth / development / memory / fertility status / level factors, age-related factors, physiological function-related factors, fertility status-related factors, sexual life-related factors, and other factors and items that may enhance / reduce / alter the above interactions / mutual influences, as well as the specific ways, results, and extent of these factors and items affecting the interactions / mutual influences.

[0048] The relevant rules for interaction may also include: the reasons why diagnostic protocols interact / influence each other; guidance / warning information for relevant patients / operators / doctors / nurses / pharmacists / caregivers; the possible consequences of interaction / influence in the above situations; the level, degree, order, probability of occurrence, degree of danger / benefit of interaction / influence; the evaluation of interaction / influence by patients / operators / doctors / pharmacists / nurses / caregivers; and the discovery and remedial measures for interaction / influence in the above situations.

[0049] Allergy-related rules and elements can include: the physicochemical properties and mechanisms of action of drugs, medical devices, examinations, surgeries, etc., that may cause allergic reactions. Specific elements include: ingredients, raw materials, electrical, magnetic, light, heat, radiation, irritancy, size, weight, heavy metals, toxicity, odor, shape, specifications, packaging, materials, additives, preservatives, antifreeze, consumable materials, etc., and related information such as concentration, content, strength, and cost-effectiveness.

[0050] Allergy-related rules may also include: the patient's constitution, age, gender, height, weight, developmental information, fertility information, sexual activity information, genetic information, the climate / air quality / humidity / season / temperature of the environment, the patient's disease / symptoms / indicators / feelings / emotions, the patient's work / study / rest / exercise / nutritional status / dietary status / sleep schedule / immune status, the patient's history of treatment with drugs / surgery / chemotherapy / radiotherapy / phototherapy / thermotherapy / magnetic therapy / electrotherapy / sound therapy / immunotherapy / gene therapy, etc., or information that may be related to allergies, such as the patient's family medical history / disease history / allergy history / hereditary disease history / regional epidemiological history / smoking history, etc.

[0051] Allergy-related rules may also include: the possible manifestations and consequences of allergic reactions under different conditions, the causes and mechanisms of allergies, the relationship between various factors that may affect allergies and allergies, relevant guidance / warning information for patients / operators / doctors / nurses / pharmacists / caregivers, the level, severity, order, and probability of allergic reactions, the evaluation of relevant allergic reactions by patients / operators / doctors / pharmacists / nurses / caregivers, and the discovery and remedial measures for relevant allergic reactions in the above situations.

[0052] This embodiment can also set conditions for requiring an allergy test. If the patient's condition meets the relevant conditions, they must first undergo an allergy test and the result must be negative before they can pass.

[0053] Time-related rules and their elements can include: start time, duration, interval time, discontinuity time, treatment cycle, frequency, course of treatment, treatment cycle, treatment interval, number of courses of treatment, onset time, expiration time, and end time of medications / medical devices / examinations / surgeries, etc. Time-related rules can be defined and stored using different time-related element attribute types, including: time element attributes related to natural rhythms (such as year, month, day, daytime / nighttime, morning / morning / noon / afternoon / evening / night, season, solar term, lunar year / month / day, etc.), time element attributes related to time of day (such as hour, minute, time of day, etc.), time element attributes related to personal daily routines (such as waking up, before / during / after meals, before bedtime), and time element attributes related to specific conditions / symptoms / indicators / psychological states / physiological states / feelings (such as when body temperature exceeds a certain value, when in pain, when tired, when dizzy, when feeling palpitations, when nauseous, when creatinine clearance exceeds a certain value, when blood pressure is below a certain value, etc.). Time-related attributes include: when the rate exceeds a certain value, when the patient is depressed, feels fear, or is excited; treatment-related time-related attributes (e.g., the day before an examination, 3 hours after a surgery, when changing a medication, after a physical therapy session); time-related attributes related to the patient's age / developmental stage (e.g., 2 weeks after birth, after the eruption of primary teeth, after puberty, 6 months after menopause, menopause); time-related attributes related to the patient's menstrual cycle (e.g., the first day of menstruation); time-related attributes related to the patient's fertility / sexual life (e.g., 24 hours after intercourse, 3 months before trying to conceive, 24 weeks of pregnancy, 3 days after delivery); and time-related attributes related to the patient's work / activities / exercise / study (e.g., before traveling by car, before traveling by boat, before performing high-altitude operations, after prolonged reading).

[0054] The relevant rules regarding time may also include: various factors that may affect time and their relationship with time; the possible consequences of not receiving a diagnosis within the reasonable time frame stipulated in the relevant rules and related remedial measures; the mutual conversion relationship between time and the actual information of relevant elements of the patient's diagnostic plan when different time attribute types need to be converted to each other; and the calculation and mutual conversion relationship between time and international standard time and time in various time zones.

[0055] The relevant rules and elements of the implementation / use methods may include: the route, site, time, distance, temperature, environment, conditions, equipment / consumables, operating methods, operator requirements, protective conditions / protective measures, and related precautions for the acceptance or use of drugs / medical devices / examinations / surgeries, etc. It may also include: the influence of factors such as treatment purpose, applicable population, corresponding indications / contraindications, corresponding health status assessment items, corresponding environment, performance, dosage, patient's drugs / medical devices / health products / diet / cosmetics / medical examinations and tests, patient's work / study / exercise related information, patient's sexual life / rest schedule / genetic information, etc., and the impact of these factors. It may also include: the possible consequences of not accepting or implementing the diagnostic plan in accordance with the correct method and related remedial measures.

[0056] Dosage-related rules and elements may include: specifications, packaging, dosage, quantity, strength, frequency, wavelength, concentration, method, scope of use, duration of use, and area of ​​use of related items such as drugs / medical devices / examinations / surgeries; information requiring unit / data conversion; the potential impact of dosage on purpose, target population, corresponding indications / indications, corresponding health status assessment items, corresponding environment, performance, usage, patient's medication / medical device / health product / diet / cosmetics / medical examinations and tests, patient's work / study / exercise-related information, patient's sexual life / rest schedule / genetic information, and the resulting impact; information on single dose / maximum dose, number of doses / frequency in a single day or other time unit, total dose / maximum dose in a single treatment course, total number of treatment courses, total dose, and maximum dose; and the potential consequences of not adhering to the correct dosage when receiving or implementing a diagnostic protocol and related remedial measures.

[0057] The relevant rules and elements of adverse reactions may include: the relationship between the method / dosage / time / frequency / interval / protective measures of drugs / medical devices / examinations / surgeries and possible adverse reactions, as well as the relationship between various factors that may affect adverse reactions and adverse reactions. It may also include: the symptoms / indicator signals / manifestations / feelings / severity / harm of adverse reactions, the management of adverse reactions, the prevention methods of adverse reactions, and the remedial measures after the occurrence of adverse reactions.

[0058] The relevant rules and elements of preparation / protection measures may include: physical, chemical, pharmaceutical, food, health products, rehabilitation, biological products, psychological intervention, humanistic and other types of preparation / protection / intervention / remedy / recovery measures taken to avoid or reduce the potential risks and harms of the diagnostic program; and may also include: the possible consequences and risk levels of failure to take preparation / protection measures in accordance with the relevant rules, as well as relevant remedial measures.

[0059] The relevant rules and elements of suitability / comfort / compliance may include: the comfort, convenience, difficulty, compliance difficulty, aesthetics, wearing weight, shape, size, volume, floor space, taste, smell, feel, temperature, hardness, irritation, portability, and storage convenience of drugs / medical devices / examinations / surgeries, etc., as well as methods for adjusting suitability / comfort / compliance, including various auxiliary conditions, auxiliary measures, auxiliary methods, and auxiliary operations.

[0060] The relevant rules for suitability / comfort / compliance may also include: setting the weights and combined calculation methods for each item related to suitability / comfort / compliance based on the patient's age, gender, physical condition, sensitivity, tolerance to electrical stimulation / magnetic field / pressure / pain, taste preferences, motor ability, physical strength, work / study / exercise / activity / travel patterns and characteristics, rest time, aesthetic requirements, learning ability, operational ability, executive ability, etc., as well as setting the goals for suitability / comfort / compliance of the patient's diagnostic plan.

[0061] The rules related to suitability / comfort / compliance may also include: when the target setting is not met, the relevant doctors, nurses, pharmacists, patients, and caregivers should be prompted to adjust the corresponding suitability / comfort / compliance methods.

[0062] The relevant rules and elements of medical insurance / benefits may include: whether the diagnostic plan falls within the scope and conditions of medical benefits / medical insurance or commercial insurance, the target group, ratio, calculation method, amount, etc.; it may also include: relevant restrictive clauses such as restrictions on hospitals / departments / doctors / nurses, etc.; and information such as price, single-session cost, daily cost, single-course cost, total cost, etc. Whether the diagnostic plan falls within the scope and conditions of medical benefits / medical insurance or commercial insurance includes: population, diagnosis, symptoms, syndrome, symptom type, surgery, test, examination, procedure, item, rehabilitation, psychotherapy, physical therapy, health care, nursing, caregiver, region, medical institution, department, doctor, pharmacy, testing center, physical examination center, treatment center, rehabilitation center, method, dosage, etc., and combinations of these elements. Relevant restrictive clauses include violations, examination methods, and penalties for violations.

[0063] The rules and elements of economic management can include: cost limits for a single diagnostic procedure, cost per day, cost per course of treatment, total cost, average cost per procedure, average cost per patient, average cost per day, and average cost per month; cost limits for different hospitals / departments / doctors / nurses; cost limits for different diseases / different diagnostic procedures / different projects / patients, etc.; and can also include: pre-setting economic indicators related to the diagnostic procedure based on the patient's actual economic capacity and budget.

[0064] Administrative management rules and their elements may include: regional regulations / requirements for diagnostic protocols, hospital level regulations / requirements, hospital nature regulations / requirements, departmental regulations / requirements, physician regulations / requirements, operator regulations / requirements, patient regulations / requirements, disease-related regulations / requirements, key monitoring regulations / requirements, abnormal warning regulations / requirements, bidding results, pricing policies, number of days, monetary amounts, DRGs (Disease Related Groups), and clinical pathway regulations / requirements. Patient-related regulations / requirements include: medical insurance, out-of-pocket payments, work-related injuries, new rural cooperative medical schemes, cadre insurance, chronic diseases, mobility impairments, the elderly, and the disabled. Monetary amount-related regulations / requirements include: compliance with single-disease payment, total prepayment, cost ratio control, single-item procurement / examination amount limits, and category procurement / examination amount limits, etc.

[0065] The diagnostic database includes various diagnostic-related elements and the relationship between diagnostic and disease information, such as conditions, requirements, attributes, and values ​​for diagnostic methods like examinations, tests, and surgeries. These include the examination time, method, precautions, required drugs / medical devices, manufacturer, specifications, dosage form, daily frequency, dosage per day, maximum dosage per day, method of use, and route of administration. It also includes: patient's basic information, population information, genetic information, disease information, medical history information, medication history information, medical device usage history information, operation items, symptoms, indicators, feelings, physical condition, operation subjects, operation methods, operation goals, physiological development information, marital and reproductive information, physiological condition, psychological / intellectual condition, life / work / study / exercise / recreation information, environmental information, drug / medical device / health product / cosmetic usage information, medical insurance information, medical expense payment ability / willingness, medical institution information, medical personnel information, medical devices / consumables / equipment / instruments / drugs / implants required for the diagnostic plan, cost of the diagnostic plan, care, risk assessment, staging, stages, anesthesia, respiration, hemostasis, infection prevention, pain, emergency measures, indicator control, preparation and requirements, training, indications / indications, contraindications, related protection and first aid, nutritional support, doctor level and authority, auxiliary software, imaging and other elements of information.

[0066] In this embodiment, population information may include: specific age, gender, developmental status, marital status, fertility status, employment status, education status, exercise status, living conditions, physiological status, psychological status, genetic status, and other specific population information. Genetic-related information includes: genetic information, genetic variation / alteration information, genetic defect information, genetic disease history, family medical history, etc. Disease information includes: disease, diagnosis, symptoms, symptom type, syndrome, indicators, pulse, tongue diagnosis, etc. Medical history information includes: medical history, surgical history, radiotherapy history, chemotherapy history, psychotherapy history, physical therapy history, immunotherapy history, gene therapy history, etc. Medication history information includes: medication history, drug efficacy, adverse drug reactions, drug allergy history, drug tolerance, etc. Medical device usage history information includes: medical device usage history, medical device efficacy, adverse drug reactions, medical device tolerance, etc. The scope of services includes: surgery, examination, testing, detection, procedures, health care, rehabilitation, psychotherapy, radiotherapy, chemotherapy, physiotherapy, thermotherapy, phototherapy, magnetotherapy, electrotherapy, cryotherapy, electromagnetic therapy, sound therapy, immunotherapy, gene therapy, weight loss, fitness, plastic surgery, and cosmetic procedures. Physiological development information includes: growth and development status, physiological stages, and fertility. Marital and reproductive information includes: marital history, reproductive history, and sexual activity. Physiological condition includes: physical fitness, nutritional status, hearing, vision, taste, smell, touch, respiration, motor coordination, digestion, absorption, excretion, sexual function, and related abilities. Psychological / intellectual condition includes: mental illness, emotions, feelings, intelligence, attention, memory, perception, communication skills, and expression skills. Life / work / study / exercise / recreation information includes: diet, daily routines, sleep, work, study, entertainment, and exercise-related information. Environmental information includes: temperature, humidity, air pressure, season, altitude, air quality, topography, landforms, oxygen content, light intensity, ultraviolet radiation, electromagnetic waves, noise, epidemics, and vegetation. Medical insurance information includes: medical benefits, medical insurance, and commercial insurance. Medical institution information includes: institution level, specialty, attributes, region, and department. Medical personnel information includes: age, gender, position, professional title, professional qualifications, professional training, and protective measures.Medical devices include: active surgical instruments, passive surgical instruments, neurosurgical and cardiovascular surgical instruments, orthopedic surgical instruments, radiotherapy instruments, medical imaging instruments, medical diagnostic and monitoring instruments, respiratory / anesthesia and emergency instruments, physical therapy instruments, blood transfusion / dialysis and extracorporeal circulation instruments, medical device sterilization and disinfection instruments, active implantable devices, passive implantable devices, injection / nursing and protective instruments, patient carriers, ophthalmic instruments, dental instruments, obstetrics and gynecology / assisted reproductive and contraceptive devices, medical rehabilitation instruments, traditional Chinese medicine instruments, medical software, clinical testing instruments, surgical robots, nursing robots, and other products that fall under the category of medical devices or are similar to medical devices, as well as other products with similar functions, including components or accessories, consumables, etc. Implants include: grafts, cultures, gene vectors, implantable chips, implantable devices / devices, etc. Nutritional support includes: detection, content standards, and supplementation dosages of water, calories, protein, trace elements, sugars, salts, fats, carbohydrates, minerals, amino acids, and vitamins.

[0067] The rules in the diagnostic database also include situations where various elements need to be combined to take effect. The relevant rules can be defined by comprehensive conditions after combining multiple elements according to multi-level AND / OR / NOT relationships and relationships defined by relevant formulas. They can also include situations where the relevant rules are related to the time dimension.

[0068] The diagnostic database in this embodiment is based on various diagnostic and treatment standards, guidelines, industry standards, textbooks, clinical treatment pathways, drug instructions, medical equipment user manuals, medical device user manuals, surgical operation specifications, examination / testing specifications, prescription sets, pharmacopoeias, expert consensus, expert experience, meeting minutes and consensus within medical consortia / hospitals / departments, papers, monographs, inventions, scientific inferences, experimental reports, test reports, data analysis reports, testing reports, inspection reports, approval documents, relevant regulations, relevant guidelines, relevant policies, relevant systems, relevant catalogs, relevant literature, relevant price regulations, relevant price catalogs, relevant bidding results, relevant price policies, relevant insurance payment terms, relevant insurance payment agreements, relevant bidding results, relevant procurement catalogs, and relevant doctors / nurses. The database can include evaluations, test results, monitoring reports, safety reports, other literature, and other professional and authoritative research findings. It can also be based on evidence-based medicine methods or probabilistic inferences from existing data. Databases can be built from sources requiring manual setting of various weights, levels, and rankings; databases based on information restructuring, information analysis, and big data analysis; databases built through artificial intelligence and deep learning; databases derived from data mining and analysis; and databases with rules and indicators manually set after data statistical analysis and artificial intelligence deep learning. It can also include relevant information and rules continuously accumulated and refined by clinicians and pharmacists during disease treatment. Diagnostic databases can also be built using a combination of the above methods. Diagnostic databases can be updated by version or in real-time based on actual data. Diagnostic databases can be relational or non-relational; they can be tabular, graph, or knowledge graph databases; and the relevant data can be structured or unstructured.

[0069] This embodiment pre-constructs a diagnostic analysis model. The diagnostic analysis model is used to provide diagnostic conclusions, including the likelihood / probability of the disease, its severity, urgency, differences in treatment plans, and treatment costs.

[0070] In this embodiment, the diagnostic analysis model includes both single-disease diagnostic analysis models and complex / mixed disease diagnostic analysis models. For complex / mixed diseases, it is necessary to comprehensively consider the impact of complex / mixed diseases on disease information and various parameters, which greatly increases the complexity of the diagnostic analysis model.

[0071] Diagnostic analysis models can be developed by combining various dimensions of elements from a large number of diagnostic instances, creating a dataset, assessing for common characteristics, and then using various statistical methods and big data monitoring to build diagnostic analysis models for combinations of elements with common characteristics. The establishment of diagnostic analysis models can be based on evidence-based medicine methods, probabilistic inferences from existing data, or manually set weights, levels, and rankings. They can also be based on information restructuring / information analysis / big data analysis, artificial intelligence deep learning, data mining analysis, statistical analysis / artificial intelligence deep learning followed by manual setting, or continuously accumulated and refined by clinicians and pharmacists in the process of disease diagnosis and treatment. The parameters of the diagnostic analysis model can be manually set by experts, obtained through data statistics / analysis, information restructuring / big data analysis, or artificial intelligence deep learning / optimization, and can be continuously accumulated and optimized during use.

[0072] In S1, the patient's disease information may include: basic patient information; indicators, parameters, states, or conditions related to various physiological / psychological / learning / work / physical fitness / sleep / exercise / emotion / metabolism / vision / hearing / intelligence / attention / appetite / immunity / growth and development / memory / fertility / genetics; information obtained through inquiry, examination, testing, experimentation, surgery, assessment, evaluation, and observation of the disease / symptoms / indicators; information related to the patient's growth and development / fertility / contraception / assisted reproduction / psychological needs / learning needs / work needs / exercise needs / entertainment needs / nutritional needs / calorie needs; information on the patient's surgery / operation / examination / testing / monitoring / assessment / evaluation / analysis / prediction / physical therapy / thermotherapy / phototherapy / magnetic therapy / electrotherapy / rehabilitation / health care / immunotherapy / gene therapy; and information related to the patient's medications / medical devices / health products / cosmetics.

[0073] Sources for obtaining patient disease information may include: patient-reported symptoms, indicators, feelings, status, and physiological functions; patient-reported status and needs regarding learning, work, life, and exercise; medical orders issued by medical personnel; patient's medical records, electronic medical records, diagnostic reports, examination results, test results, monitoring results, and assessment reports; information obtained from patient personal information databases; information obtained through methods such as inquiry, examination, testing, experimentation, surgery, assessment, evaluation, and observation; pre-set information such as time, season, hour, interval, and cycle; and pre-set methods for monitoring changes in various physiological and pathological indicators.

[0074] In this embodiment, diagnostic analysis results are given based on the patient's disease information, the relationship between the disease information and the disease, and a diagnostic analysis model. These results include: determining whether a diagnostic conclusion can be drawn based on existing information, evaluating the correctness of existing diagnostic conclusions, recommending a diagnostic conclusion, and recommending a diagnostic plan for continued diagnosis. For example, this embodiment performs diagnostic analysis on existing patient disease information to determine whether a diagnostic conclusion can be drawn based on the existing information. If a diagnostic conclusion can be drawn, a recommended diagnostic conclusion can be given based on the existing information. If a diagnostic conclusion cannot be drawn, continued diagnosis can be suggested, and a recommended diagnostic plan suitable for the patient can be given based on the existing information. More patient disease information can be obtained through the diagnostic plan for the next diagnostic analysis. Furthermore, diagnostic analysis of existing information can determine the correctness of existing diagnostic conclusions and can also be used to recommend, review, and evaluate diagnostic conclusions.

[0075] The diagnostic analysis results of this embodiment can be used to assist in reminding, warning, restricting, prohibiting, assisting, and guiding patients / doctors / nurses / caregivers in making diagnoses; they can also be used as the data basis for patients / doctors / nurses / caregivers to discuss diagnoses with recommending / managing / guiding / consulting positions or secondary recommending / managing / guiding / consulting centers, as well as for senior positions to reply / automatically reply; they can also serve as the basis for evaluating the professional level / professional standardization / performance evaluation / performance appraisal of doctors / nurses / caregivers, etc.; they can also serve as the basis for vending machines / e-commerce / pharmacies / medical institutions to sell or refuse to sell; they can also be connected with various information systems / information platforms / various vending machines / smart wearable devices / smart home devices / smart medical devices / medical devices / remote control medical devices, as configurable control items that trigger related operations or processes in the system or related devices, such as automatic vending, smart reminders, smart start, smart shutdown, etc.; they can also serve as the basis for medical insurance management agencies / medical insurance companies / health administration departments / credit rating agencies / grading review agencies / judicial agencies to evaluate, assess, supervise, enforce, manage, and adjudicate medical institutions / pharmacies. This data serves as the basis for business activities related to medical device retail stores; it can also be used as the basis for the treatment, rehabilitation, health care, and examination of patients managed by guardians, family doctors, family pharmacists, health managers, and home caregivers; it can also be used in the diagnostic aspects of patient health management / disease management in intelligent health management systems / disease management systems; it can also be used as the data basis for the selection, evaluation, design, research and development, and sales of relevant entities such as research institutions, researchers, manufacturers, sales companies, user institutions, purchasing institutions, patients, doctors, nurses, pharmacists, caregivers, and salespersons; it can also be used as the data basis for medical insurance management agencies, medical insurance companies, price management departments, health administration departments, and medical institutions when formulating surgical-related policies, regulations, payment scope, payment ratio, payment amount, and scope of use; it can also set the feedback objects and applications for the above-mentioned analysis items and their analysis results, and according to the needs of patients, feed back the analysis results of each analysis item to different departments and positions, and set the processing permissions and processes of each department and position, as well as the processing time limits and processing requirements of each link, and can also generate various analysis result reports according to management needs. The application of rationality analysis for each analytical item can be a pre-event / in-event recommendation model or a post-event review model. Based on the above analyses and evaluation results, a standardized evaluation report for diagnostic analysis is issued and provided to medical administration personnel, medical insurance institutions, medical insurance management personnel, and distribution sector management personnel, highlighting potential violations and illegal activities, and providing support for further standardizing diagnostic analysis.

[0076] In S3, if a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model, then a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model.

[0077] Specifically, based on the different analysis results of at least one of the following analysis items under different disease / patient information conditions: the probability / severity of the patient having different diseases, the severity, the urgency, the differences in treatment plans, and the treatment costs, corresponding levels / scores are set. During diagnostic analysis, based on the level / score of the diagnostic conclusion in each analysis item and the diagnostic analysis model, the comprehensive level / score of different diagnostic conclusions under the patient's disease information conditions is calculated to obtain the recommended diagnostic conclusion. This can be used to give a comprehensive recommendation result or a comprehensive priority ranking of diagnostic conclusions and to complete the comparison between different diagnostic conclusions.

[0078] The basis for recommending a diagnostic conclusion can be that the overall level / score of the diagnostic conclusion reaches a pre-set threshold, or that the overall level / score of the diagnostic conclusion is optimal and the difference between the overall level / score of other diagnostic conclusions is greater than a pre-set threshold, or that the overall level / score of the diagnostic conclusion, combined with the level / score of the individual analysis items selected for the actual situation, meets the set standard.

[0079] This embodiment can also combine treatments for multiple possible diagnostic conclusions, saving medical resources. For example, if the treatment plans for multiple possible diagnostic conclusions are very similar and their severity and urgency are not significant, they can be combined for treatment.

[0080] In S4, if it is determined that a diagnosis cannot be reached based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model, then the possible range of the patient's disease is obtained based on the patient's disease information. Based on the possible range of the patient's disease and the relationship between the disease and the diagnostic plan, a recommended diagnostic plan is obtained. The patient is diagnosed using the recommended diagnostic plan to obtain new disease information. The new disease information is then fused with the patient's existing disease information to obtain fused disease information. This fused disease information is used as the patient's disease information for the next iteration, and the process returns to the step of "determining whether a diagnosis can be reached based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model".

[0081] This includes determining the possible range of diseases a patient might have based on their disease information, specifically: determining the possible range of diseases a patient might have based on their disease information and the relationship between the disease information and the disease.

[0082] Alternatively, a diagnostic analysis model can be used to analyze the possible range of a patient's diseases. In this case, the possible range of a patient's diseases is obtained based on the patient's disease information. Specifically, this includes setting a level / score for each analysis item based on the different analysis results of at least one of the following analysis items under different disease / patient information conditions: the probability / severity of the patient having different diseases, the severity, the urgency, the differences in treatment plans, and the treatment costs. When analyzing the possible range of a patient's diseases, the comprehensive level / score of the possible range of a patient's diseases under the given disease / patient information conditions can be calculated based on the level / score of each analysis item and the set diagnostic analysis model. This allows for the provision of a recommended possible range of a patient's diseases based on pre-set parameters, and the subsequent use of this recommended possible range of a patient's diseases to determine the recommended diagnostic plan.

[0083] Specifically, recommended diagnostic plans are derived based on the patient's possible disease range and the relationship between the disease and the diagnostic plan. This includes: obtaining a recommended list of diagnostic plans based on the patient's possible disease range and the relationship between the disease and the diagnostic plan; optimizing the recommended list of diagnostic plans using relevant rules on the rationality and compliance of diagnostic plans to obtain recommended diagnostic plans, thereby recommending a suitable diagnostic plan for the patient.

[0084] The relevant rules for the rationality and compliance of diagnostic protocols include: rules related to applicability, rules related to contraindications, rules related to caution / caution, rules related to interactions, rules related to allergies, rules related to timing, rules related to implementation / usage methods, rules related to dosage, rules related to adverse reactions, rules related to preparation / protection measures, rules related to suitability / comfort / compliance, rules related to medical insurance / benefits, rules related to economic control, and rules related to administrative management. The rules related to adverse reactions include: the relationship between the method / dosage / time / frequency / interval / protective measures of drugs / medical devices / examinations / surgeries and possible adverse reactions; the relationship between various factors that may affect adverse reactions and adverse reactions; and the symptoms / indicator signals / manifestations / feelings / severity / harm of adverse reactions, the management of adverse reactions, methods for preventing adverse reactions, and remedial measures after the occurrence of adverse reactions. The rules related to preparation / protection measures include: physical, chemical, drug, food, health products, rehabilitation, biological products, psychological intervention, and humanistic preparation / protection / intervention / remediation / recovery measures taken to avoid or reduce the potential risks and harms of the diagnostic program; and the possible consequences and risk levels and related remedial measures when preparation / protection measures are not taken in accordance with the relevant rules.

[0085] This embodiment analyzes the patient's disease information to determine the possible range of diseases, and then analyzes the possible range of diseases to determine the necessary diagnostic plan. The diagnostic plan obtained at this stage is only a preliminary analysis. There may be many diagnostic plans obtained through the preliminary analysis, including some that are not suitable for the patient's actual situation. These plans need to be compared with the relevant rules on the rationality and compliance of diagnostic plans in the diagnostic database to exclude diagnostic plans that pose a serious risk to the patient. Diagnostic plans that pose a potential risk to the patient need to have their risks fully assessed and their necessity weighed before a truly suitable diagnostic plan for the patient can be obtained.

[0086] Intelligent diagnostic plan recommendations require balancing multiple factors such as accuracy, risk, and cost. This balancing can be achieved through pre-set parameters, which can also be modified and iterated based on actual conditions to ensure balance under different circumstances. The parameters for balancing accuracy, risk, and cost can also be derived through big data analysis.

[0087] In this embodiment, after obtaining the recommended diagnostic conclusion, the method further includes: evaluating the recommended diagnostic conclusion based on new disease information obtained during the treatment process or based on the actual treatment effect after treatment based on the recommended diagnostic conclusion, to obtain an evaluation result used to characterize whether the recommended diagnostic conclusion is correct. Analyzing and evaluating the original diagnostic analysis results through new disease information or actual treatment effects allows for adjustments and corrections to the diagnostic analysis results at any time, ensuring the accuracy of the diagnostic analysis results. Furthermore, it allows for re-analysis based on new disease information or actual treatment effects to obtain new diagnostic analysis results.

[0088] In this embodiment, through big data analysis, regression verification and correction of treatment results, diagnostic conclusions, and diagnostic plans can be performed to analyze and evaluate the diagnostic analysis model. By balancing the analysis items such as the possibility / probability of the disease, severity, urgency, differences in treatment plans, and treatment costs, and by improving and adjusting the pre-set parameters, the diagnostic analysis model can be made more effective.

[0089] In this embodiment, after obtaining the recommended diagnostic plan, the method further includes: reminding the patient / operator / doctor / nurse / pharmacist / caregiver of various situations, risks and their detection / detection methods that may occur when the patient carries out the recommended diagnostic plan or after the recommended diagnostic plan is carried out, the relevant preparation and response methods, and issuing warnings, prompts or initiating remedial measures when a situation is discovered.

[0090] In this embodiment, the database unit used to store the diagnostic database also stores a diagnostic multi-dimensional element attribute dictionary, used to process the matching / comparison of information acquired from different sources, different data structures, different descriptions, and different data standards with relevant information and rules in the database unit. The diagnostic multi-dimensional element attribute dictionary contains at least one of the following: a standard dictionary, an antonym correspondence dictionary, and a fuzzy matching dictionary for each diagnostic-related element attribute. It includes data such as antonyms, structures, combinations, and mutual correspondences of the element attributes involved in each of the above items. Matching / comparison can be performed by comparing the acquired raw information with the antonym correspondence dictionary for each item in the diagnostic multi-dimensional attribute dictionary; it can also be performed by first converting the acquired raw information to correspond to each standard dictionary before comparing it with the diagnostic database; or it can be performed by fuzzy matching comparison of the acquired raw information with each dictionary in the diagnostic database; or it can be a combination of the above methods. The diagnostic multi-dimensional element attribute dictionary can be established separately or included in the diagnostic database.

[0091] In this embodiment, in addition to using a diagnostic multi-dimensional element attribute dictionary for matching, the information obtained can also be matched / compared with relevant information and rules in the database unit through speech recognition technology, semantic recognition technology, translation of different languages, OCR recognition technology, virtual reality technology, augmented reality technology, gesture recognition technology, etc.

[0092] In this embodiment, the database unit also stores a patient personal information database, which includes relevant patient information to provide or supplement relevant patient information during diagnostic analysis. The patient personal information database includes relevant patient information, including: basic patient information, genetic information, family health information such as family medical history, medical history, allergy history, regional epidemiological history, medication history, surgical history, medical device use history, learning situation, work situation, exercise situation, family situation, living environment, hobbies, compliance, tolerance, medical insurance information, as well as information on physiological / psychological / learning / work / physical fitness / sleep / exercise / emotion / metabolism / vision / hearing / intelligence / attention / diet / immunity / growth and development / memory / fertility status and daily routine, etc.

[0093] In this embodiment, the database unit also stores a database of medical personnel's personal information. This database includes relevant information about the medical personnel, which is used to provide or supplement this information during diagnostic analysis. The database includes information about the medical personnel, such as their education, major, specialty, professional title, position, practicing hospital, department, and their habits in prescribing medical orders. These habits include relevant experience, medical order writing habits, and patterns in commonly used medical orders.

[0094] The methods for acquiring data for patient personal information databases and medical personnel personal information databases include obtaining or accessing data from other information systems, devices, or databases and then analyzing it, or manually entering data, or continuously acquiring and analyzing new data during the use of relevant information in the database, or a combination of the above methods.

[0095] Sources for obtaining relevant patient information can include patient personal information databases, health records, family or clan member health records, medical orders, medical records, medication records, prescriptions, electronic medical records, medical institution information systems, pharmacy / medical device store information systems, medical records, treatment records, assessment reports, consultation records, survey records, daily routine records / plans, dietary records / plans, shopping records / plans, medication records / plans, treatment records / plans, exercise records / plans, work records / plans, study records / plans, rehabilitation records / plans, health care records / plans, laboratory / examination reports, surgical plans / records, health management plans, bills, clinical treatment pathways, examination / test results, surgical plans / records, and genetic testing results. Information can also be obtained from patients / doctors / nurses. This information can be obtained from the patient's / caregiver's usage / prescription / recommendation records, or from various wearable devices, sensors, electronic devices, electronic positioning systems, weather forecasting systems, electronic temperature and humidity / barometric pressure detection devices, smart speakers, smart home systems, smart monitoring / monitoring systems, smart glasses, smart toilets, smart floors, smart scales, smart detection / analysis devices, electronic infusion systems, surgical robots, facial recognition analysis, fingerprint recognition, voice recognition, gait recognition, positioning systems, social platforms, etc. It can also be obtained through big data analysis of the patient's life, study, work, exercise, travel, social activities, shopping, diet, rest, entertainment, etc., or from the analysis of the patient's race / family / region / age / marital / fertility information. Missing information can be provided or supplemented by patients / doctors / nurses / caregivers. Alternatively, highly relevant information can be proactively prompted to patients / doctors / nurses / caregivers for observation, monitoring, examination, inquiry, analysis, confirmation, and recording of relevant situations or acquisition of relevant indicators / performance / feelings / symptoms / physiological changes. Relevance is determined by grading or ranking various relevant elements according to their importance to the rationality and compliance of the diagnostic plan in terms of effectiveness, safety, economy, appropriateness / comfort / compliance. Information of higher importance can be designated as essential and cannot proceed to the next step without completion. Assessment reports include: physiological, psychological, economic, credit, and motor ability. Intelligent detection / analysis equipment includes: odor, image, sound, pulse, X-ray, CT, MRI, ultrasound, EEG, mass spectrometry, tongue diagnosis analysis, fundus examination, gastroscopy, colonoscopy, catheterization, minimally invasive endoscopy, heart rate, blood oxygen saturation, blood pressure, blood glucose, blood lipids, body temperature, blood tests, urine tests, stool tests, pulse measurement / analysis equipment, etc.

[0096] In this embodiment, the identification, confirmation, login, and electronic signature of patients, medical personnel, and related roles, as well as the storage, transmission, and application of personal information, medical orders, and various analysis results, can be encrypted using various methods to prevent the theft of related identities / permissions or information leakage. The encryption algorithms include symmetric and / or asymmetric encryption algorithms, such as encryption algorithms for large integer factorization, discrete logarithm problems, and elliptic curve cryptography, specifically blockchain technology. Encryption hardware can employ keys, dongles, encrypted hard drives, etc., and can also be combined with user device hardware, network addresses, etc., or a combination of the above methods.

[0097] In this embodiment, data transmission methods can include data cable, wired network, wireless transmission, radio frequency identification, magnetic card reading / writing, mobile hard drive, NFC, barcode, QR code, etc. Wireless transmission methods include: infrared, Bluetooth, Wi-Fi, microwave, visible light waves, telecommunications wireless networks, ultrasound / sound waves, radio waves, etc.

[0098] The intelligent diagnostic analysis method in this embodiment can be used as a standalone device, or it can be used by users through external hardware such as mobile hard drives, boxes, or cards. It can also be installed on a local server to support local users, or on a private cloud server to support private cloud users, or on the Internet to provide services to Internet users.

[0099] The intelligent diagnostic analysis method in this embodiment can comprehensively analyze, remind, and recommend diagnostic methods throughout the entire process, considering safety, effectiveness, economy, and appropriateness. It can effectively avoid problems such as unreasonable or untimely diagnoses caused by insufficient professional skills of medical personnel, individual patient differences, or human error, ensuring that patients receive appropriate diagnosis and treatment. For example, when different medical institutions, clinics, health centers, health stations, departments, doctors, nurses, and laboratory technicians within the jurisdiction of health administrative departments at all levels conduct diagnoses, the system can recommend, review, and comment on diagnostic conclusions and plans. This can be done by a system installed locally or by a system installed on an internet / LAN server. The system can complete this automatically, or it can complete a pre-recommendation, and the relevant results can be confirmed / adjusted by the recommending personnel before outputting the official recommendation results. The diagnostic analysis results can be used for managing the patient's diagnostic process, as a basis for adjusting medical orders, and for evaluating the medical quality / work performance / professional competence / ethics / service quality / efficiency of different medical institutions, departments, doctors, nurses, pharmacists, and laboratory technicians. For example, a data service center can be established to provide diagnostic conclusions and recommended treatment plans to users such as medical institutions, clinics, health centers, health stations, medical insurance institutions, insurance companies, doctors, pharmacists, nurses, laboratory technicians, patients, and caregivers based on intelligent diagnostic analysis methods. The data service center can assist users in generating different versions of diagnostic databases that are personalized and configured according to their attributes, scale, business, region, economic situation, management system, and usage habits. It can also customize different data transmission, storage, output, and application processes / modes / formats according to different user business processes, and can connect to different systems, networks, devices, and institutions.

[0100] This embodiment first obtains the patient's disease information. Based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model, it determines whether a diagnostic conclusion can be drawn. If so, a recommended diagnostic conclusion is obtained; otherwise, a recommended diagnostic plan is obtained. Based on technical data such as treatment guidelines, a comprehensive analysis and recommendation is made regarding the safety, effectiveness, economy, and appropriateness of the entire diagnostic process. This helps medical personnel and patients to quickly and accurately arrive at scientific diagnostic conclusions, effectively improving the accuracy and timeliness of diagnosis. It addresses the risks associated with inaccurate or untimely diagnostic conclusions, as well as unreasonable or non-compliant diagnostic plans in terms of effectiveness, safety, economy, and appropriateness. It avoids the possibility of inaccurate diagnostic conclusions or unreasonable diagnostic plans due to insufficient understanding of the specific content of the disease diagnosis by medical personnel, insufficient grasp of individualized patient information, or human error during the actual diagnostic process. It is an important method to ensure the accuracy of patient diagnostic results and the rationality of diagnostic plans, and will gradually play a significant role in ensuring accurate and reasonable patient diagnoses.

[0101] Example 2

[0102] This embodiment provides an intelligent diagnostic analysis system, including:

[0103] Information acquisition unit, used to acquire the patient's disease information;

[0104] The analysis unit is used to determine whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and a diagnostic analysis model. The diagnostic conclusion includes the likelihood / probability, severity, and urgency of the disease. If yes, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model. If no, the possible range of the patient's disease is obtained based on the patient's disease information, and a recommended diagnostic plan is obtained based on the possible range of the patient's disease and the relationship between the disease and the diagnostic plan. The patient is diagnosed using the recommended diagnostic plan to obtain new disease information, and the new disease information is fused with the patient's existing disease information to obtain fused disease information. The fused disease information is used as the patient's disease information for the next iteration, and the process returns to the step of "determining whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and a diagnostic analysis model".

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An intelligent diagnostic analysis method, characterized in that, include: Obtain the patient's disease information; Determine whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model. The diagnostic conclusions include the likelihood / probability of the disease, its severity, and its urgency. If so, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model; If not, then based on the patient's disease information, the possible range of the patient's diseases is obtained, and based on the possible range of the patient's diseases and the relationship between the diseases and the diagnostic plan, a recommended diagnostic plan is obtained; the patient is diagnosed using the recommended diagnostic plan to obtain new disease information, and the new disease information is fused with the patient's disease information to obtain fused disease information. The fused disease information is used as the patient's disease information for the next iteration, and the process returns to the step of "determining whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model".

2. The intelligent diagnostic analysis method according to claim 1, characterized in that, The relationship between disease information and disease includes: possible diseases corresponding to different disease information and the disease information required for diagnosis; the relationship between disease and diagnostic plan includes: the appropriate diagnostic plan required for different diseases or disease ranges; The relationship between disease information and disease, as well as the relationship between disease and diagnostic protocols, is determined by comprehensively considering the balance between the likelihood / probability of the disease, its severity, urgency, differences in treatment protocols, and treatment costs.

3. The intelligent diagnostic analysis method according to claim 1, characterized in that, The recommended diagnostic conclusion is derived based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model. Specifically, this includes: Based on the different analysis results of at least one of the following analysis items under different disease information conditions: the probability / severity, severity, urgency, differences in treatment plans, and treatment costs of patients suffering from different diseases, corresponding levels / scores are set. When conducting diagnostic analysis, based on the diagnostic conclusions derived from the diagnostic analysis and the levels / scores corresponding to each of the aforementioned analysis items, as well as the diagnostic analysis model, the comprehensive level / score of different diagnostic conclusions under the patient's disease information conditions is calculated to obtain the recommended diagnostic conclusion.

4. The intelligent diagnostic analysis method according to claim 1, characterized in that, After receiving the recommended diagnosis, the following is also included: Based on new disease information obtained during the treatment process or based on the actual treatment effect after treatment based on the recommended diagnostic conclusion, the recommended diagnostic conclusion is evaluated to obtain an evaluation result used to characterize whether the recommended diagnostic conclusion is correct.

5. The intelligent diagnostic analysis method according to claim 1, characterized in that, Based on the patient's disease information, the possible range of diseases for the patient is determined, specifically including: The possible range of diseases a patient may have is determined based on the patient's disease information and the relationship between that information and the disease itself.

6. The intelligent diagnostic analysis method according to claim 1, characterized in that, Based on the patient's disease information, the possible range of diseases for the patient is determined, specifically including: Based on the different analysis results of at least one of the following analysis items under different disease information conditions: the probability / severity, severity, urgency, treatment plan differences, and treatment cost of a patient having different diseases, corresponding levels / scores are set respectively. When analyzing the possible range of diseases of a patient, based on the level / scores corresponding to each of the analysis items and the diagnostic analysis model, the comprehensive level / score of different possible ranges of diseases under the conditions of the patient's disease information is calculated to obtain the possible range of diseases of the patient.

7. The intelligent diagnostic analysis method according to claim 1, characterized in that, The recommended diagnostic plan is derived based on the patient's possible disease range and the relationship between the disease and the diagnostic approach, specifically including: A list of recommended diagnostic options is generated based on the patient's possible range of diseases and the relationship between the diseases and the diagnostic options. The recommended list of diagnostic protocols is optimized using relevant rules regarding the rationality and compliance of diagnostic protocols to obtain recommended diagnostic protocols. These relevant rules include: rules regarding applicability, contraindications, precautions / cautions, interactions, allergies, timing, methods of administration / use, dosage, adverse reactions, preparation / protective measures, suitability / comfort / compliance, medical insurance / benefits, economic management, and administrative management.

8. The intelligent diagnostic analysis method according to claim 7, characterized in that, The relevant rules for adverse reactions include: the relationship between the method / dosage / time / frequency / interval / protective measures of drugs / medical devices / examinations / surgeries and possible adverse reactions; the relationship between various factors that may affect adverse reactions and adverse reactions; and the symptoms / indicator signals / manifestations / feelings / severity / harm of adverse reactions, the management of adverse reactions, the prevention methods of adverse reactions, and the remedial measures after the occurrence of adverse reactions. The relevant rules for preparation / protection measures include: physical, chemical, pharmaceutical, food, health products, rehabilitation, biological products, psychological intervention, and humanistic preparation / protection / intervention / remediation / recovery measures taken to avoid or reduce the potential risks and harms of the diagnostic program, as well as the possible consequences, risk levels, and relevant remedial measures that may occur if preparation / protection measures are not taken in accordance with the relevant rules.

9. The intelligent diagnostic analysis method according to claim 1, characterized in that, After receiving the recommended diagnostic plan, the following is also included: Remind patients / operators / doctors / nurses / pharmacists / caregivers of the various situations and risks that may arise when or after a recommended diagnostic protocol is implemented, the methods for detecting / finding these risks, the necessary preparations and coping strategies, and how to issue warnings, reminders, or initiate remedial measures when such situations are discovered.

10. An intelligent diagnostic analysis system, characterized in that, include: Information acquisition unit, used to acquire the patient's disease information; The analysis unit is used to determine whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model; the diagnostic conclusion includes the likelihood / probability of the disease, its severity, and its urgency. If yes, a recommended diagnostic conclusion is obtained based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model; if no, the possible range of the patient's disease is obtained based on the patient's disease information, and a recommended diagnostic plan is obtained based on the possible range of the patient's disease and the relationship between the disease and the diagnostic plan; the patient is diagnosed using the recommended diagnostic plan to obtain new disease information, and the new disease information is fused with the patient's disease information to obtain fused disease information. The fused disease information is used as the patient's disease information for the next iteration, and the process returns to the step of "determining whether a diagnostic conclusion can be drawn based on the patient's disease information, the relationship between the disease information and the disease, and the diagnostic analysis model".

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