Intelligent hospital guide system based on cloud computing
Through multi-dimensional data collection and intelligent algorithm analysis, the misjudgment problem caused by the singleness of data in traditional medical guidance systems has been solved, and more accurate medical advice and resource optimization have been achieved.
Patent Information
- Application Number
- CN202510754187.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-06
- Publication Date
- 2025-09-12
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional medical guidance systems rely on a single data source, resulting in a single basis for judgment, a high misjudgment rate, slow system response, difficulty in generating personalized medical recommendations, and affecting the allocation of medical resources.
By integrating multi-dimensional data collection of symptom descriptions, physiological indicators, medical history and patient behavior logs, combining mathematical formulas to calculate matching degrees and behavior association scores, and designing data matching modules and disease tracing modules, multi-dimensional data fusion and intelligent algorithm analysis can be achieved.
It improves the accuracy of disease screening, provides more accurate medical guidance, and alleviates the uneven distribution of medical resources and the pressure of manual guidance.
Smart Images
Figure CN120636767A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent medical guidance technology, and in particular to an intelligent medical guidance system based on cloud computing. Background Art
[0002] In recent years, with the rapid development of cloud computing and big data technologies, the progress of medical informatization has continued to accelerate. In traditional medical services, patients often need to undergo manual guidance or simple rule matching to receive preliminary medical advice. This approach is easily restricted by information silos, incomplete data collection, and the limitations of manual judgment. The introduction of cloud computing platforms provides a new solution for the real-time collection, storage, and processing of medical data. It enables the efficient integration of multi-source data (such as patient symptom descriptions, physiological indicators, medical history information, and behavioral logs), providing solid data support for intelligent medical guidance.
[0003] Traditional systems rely heavily on patients' chief complaints and symptom descriptions, lacking real-time physiological parameters and behavioral data, making the basis for judgment relatively simple and prone to missing key information; existing methods often use preset simple matching rules, which are difficult to accurately reflect the complex relationship between symptoms and diseases, and have low accuracy in identifying potential diseases; traditional systems usually fail to fully utilize the distributed processing capabilities of cloud computing, resulting in slow system response and limited scalability when the data volume and request volume are large; due to the lack of comprehensive analysis of multi-dimensional data such as patient behavior logs, the existing medical guidance system has shortcomings in generating personalized and accurate medical recommendations, which may affect the rational allocation of medical resources. Summary of the Invention
[0004] In response to the shortcomings of existing technologies, the present invention proposes an intelligent medical guidance system based on cloud computing. By integrating symptom descriptions, physiological indicators, medical history, and patient behavior logs through a data acquisition port, the present invention realizes multi-dimensional real-time data collection, providing a comprehensive basis for subsequent analysis. A data matching module is designed to extract the patient's current symptoms from the typical symptom set in the disease database and calculate the matching degree using a mathematical formula. At the same time, the co-occurrence of patient behavior logs and symptoms is analyzed to calculate the behavioral association score. This dual assessment makes disease screening more accurate. Through multi-dimensional data fusion and intelligent algorithms, this solution can effectively reduce the risk of misjudgment caused by a single data source, provide patients with more accurate medical guidance, and thus alleviate the uneven distribution of medical resources and the pressure of manual medical guidance to a certain extent.
[0005] To achieve the above object, the technical solution of the present invention is as follows:
[0006] The intelligent medical guidance system based on cloud computing includes a data collection port, a data matching port and a disease tracing port. The data collection port is used to collect patients' symptom descriptions, medical history, basic physiological indicators, age, gender and patient behavior logs in real time through multi-source input; the data matching port is used to compare patients' symptoms with the disease database, calculate the symptom matching degree and screen potential diseases; the disease tracing port is used to locate the most likely disease or department and generate medical guidance suggestions.
[0007] A further improvement of the present invention is that the data acquisition port includes a symptom description acquisition module, a physiological indicator acquisition module, a medical history acquisition module and a patient behavior acquisition module; the symptom description acquisition module is used to collect the patient's chief complaint, symptom duration and severity; the physiological indicator acquisition module is used to collect the patient's real-time monitored body temperature, blood pressure, blood oxygen, age and gender; the medical history acquisition module is used to collect the patient's allergy history, chronic disease history, and surgical history; the patient behavior acquisition module is used to collect the patient's behavior log.
[0008] A further improvement of the present invention is that the data matching port includes a data extraction module, a matching degree calculation module and a threshold judgment module, wherein the data extraction module is used to extract the patient's current symptom set S = {S1, ..., S i ,...S n} and the typical symptom set D in the disease database j , j represents the disease number, i represents the patient's current symptom number, and the value of i is 1-n; the matching degree calculation module is used to calculate the matching degree between the patient's symptoms and each disease; the threshold judgment module is used to mark it as a potential disease when the matching degree exceeds the threshold.
[0009] A further improvement of the present invention is that the disease tracing port includes a behavior association search module and a comprehensive recommendation value calculation module, the behavior association search module is used to extract the patient behavior log data A m The number of co-occurrences with the patient's current symptom set S, and the patient's behavior log data A is calculated m The degree of compatibility with the patient's current symptom set S is used to obtain the behavioral association score B j The comprehensive recommendation value calculation module is used to calculate the recommendation value R according to the matching degree between the patient's current symptoms and the disease and the behavior association score. j The recommended values of each disease are arranged in descending order, and the corresponding departments of the top three diseases are recommended.
[0010] A further improvement of the present invention is that the matching degree calculation module calculates the matching degree between the patient's symptoms and each disease, and the matching degree calculation formula is:
[0011]
[0012] Among them, M j represents the matching degree of disease j, y i represents the weight of symptom i, N(S i ∩D j ) represents the intersection count of patient symptom i and typical symptoms of disease j, N(D j ) represents the total number of typical symptoms of disease j.
[0013] A further improvement of the present invention is that the behavior association score B is obtained j The specific steps include:
[0014] S11. Extract patient behavior log data A m The number of co-occurrences with the patient's current symptom set S, m represents the patient's behavior log data number, and the value of m is 1-M;
[0015] S12. Calculate patient behavior log data A m The compatibility with the patient's current symptom set S is calculated as:
[0016] S13. Calculate patient behavior log data A m Behavioral association score B with disease j j , the calculation formula is: Among them, I(A m ∈E j ) is the indicator function, when the patient behavior data A m When I(A m ∈E j )=1, otherwise I(A m ∈E j )=0.
[0017] A further improvement of the present invention is that the comprehensive recommendation value calculation module is used to calculate the recommendation value R according to the matching degree between the patient's current symptoms and the disease and the behavior association score. j , the recommended value R j The calculation formula is:
[0018] R j =αM j +βB j ;
[0019] Wherein, α and β are weight factors respectively, and α+β=1.
[0020] The technical effects of the present invention are as follows:
[0021] This invention integrates symptom descriptions, physiological indicators, medical history, and patient behavior logs through a data acquisition port, enabling multi-dimensional, real-time data collection and providing a comprehensive basis for subsequent analysis. A data matching module is designed to extract the patient's current symptoms from a set of typical symptoms in the disease database and calculate the degree of matching using a mathematical formula. Simultaneously, the co-occurrence of patient behavior logs and symptoms is analyzed to calculate a behavioral association score. This dual assessment enables more accurate disease screening. Through multi-dimensional data fusion and intelligent algorithms, this solution can effectively reduce the risk of misjudgment due to a single data source, providing patients with more accurate medical guidance, thereby alleviating the uneven distribution of medical resources and the pressure of manual medical guidance to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:
[0023] Figure 1 Schematic diagram of the structure of the cloud computing-based intelligent medical guidance system of the present invention. DETAILED DESCRIPTION
[0024] Example 1
[0025] The intelligent medical guidance system based on cloud computing includes a data collection port, a data matching port and a disease tracing port. The data collection port is used to collect patients' symptom descriptions, medical history, basic physiological indicators, age, gender and patient behavior logs in real time through multi-source input; the data matching port is used to compare patient symptoms with the disease database, calculate the symptom matching degree and screen potential diseases; the disease tracing port is used to locate the most likely disease or department and generate medical guidance suggestions.
[0026] In this embodiment, the data acquisition port includes a symptom description acquisition module, a physiological indicator acquisition module, a medical history acquisition module and a patient behavior acquisition module; the symptom description acquisition module is used to collect the patient's chief complaint, symptom duration and severity; the physiological indicator acquisition module is used to collect the patient's real-time monitored body temperature, blood pressure, blood oxygen, age and gender; the medical history acquisition module is used to collect the patient's allergy history, chronic disease history, and surgical history; the patient behavior acquisition module is used to collect the patient's behavior log.
[0027] Example 2
[0028] In this embodiment, the data matching port includes a data extraction module, a matching degree calculation module and a threshold judgment module. The data extraction module is used to extract the patient's current symptom set S = {S1, ..., S i ,...S n} and the typical symptom set D in the disease databasej , j represents the disease number, i represents the patient's current symptom number, and the value of i is 1-n; the matching degree calculation module is used to calculate the matching degree between the patient's symptoms and each disease; the threshold judgment module is used to mark it as a potential disease when the matching degree exceeds the threshold.
[0029] In this embodiment, the disease traceability port includes a behavior association search module and a comprehensive recommendation value calculation module. The behavior association search module is used to extract patient behavior log data A m The number of co-occurrences with the patient's current symptom set S, and the patient's behavior log data A is calculated m The degree of compatibility with the patient's current symptom set S is used to obtain the behavioral association score B j The comprehensive recommendation value calculation module is used to calculate the recommendation value R according to the matching degree between the patient's current symptoms and the disease and the behavior association score. j The recommended values of each disease are arranged in descending order, and the corresponding departments of the top three diseases are recommended.
[0030] Example 3
[0031] In this embodiment, the matching degree calculation module calculates the matching degree between the patient's symptoms and each disease. The matching degree calculation formula is:
[0032]
[0033] Among them, M j represents the matching degree of disease j, y i represents the weight of symptom i, N(S i ∩D j ) represents the intersection count of patient symptom i and typical symptoms of disease j, N(D j ) represents the total number of typical symptoms of disease j.
[0034] In this embodiment, the behavior association score B j The specific steps include:
[0035] S11. Extract patient behavior log data A m The number of co-occurrences with the patient's current symptom set S, m represents the patient's behavior log data number, and the value of m is 1-M;
[0036] S12. Calculate patient behavior log data A m The compatibility with the patient's current symptom set S is calculated as:
[0037] S13. Calculate patient behavior log data A m Behavioral association score B with disease j j , the calculation formula is: Among them, I(A m ∈E j ) is the indicator function, when the patient behavior data A m When I(A m ∈E j )=1, otherwise I(A m ∈E j )=0.
[0038] Example 4
[0039] In this embodiment, the comprehensive recommendation value calculation module is used to calculate the recommendation value R according to the matching degree between the patient's current symptoms and the disease and the behavior association score. j , the recommended value R j The calculation formula is:
[0040] R j =αM j +βB j ;
[0041] Wherein, α and β are weight factors respectively, and α+β=1.
[0042] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0043] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.
[0044] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.
[0045] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.
[0046] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0047] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0048] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.
[0049] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0050] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0051] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.
Claims
1. The intelligent medical guidance system based on cloud computing is characterized by: The system includes a data collection port, a data matching port and a disease tracing port. The data collection port is used to collect patients' symptom descriptions, medical history, basic physiological indicators, age, gender and patient behavior logs in real time through multi-source input; the data matching port is used to compare patients' symptoms with the disease database, calculate the symptom matching degree and screen potential diseases; the disease tracing port is used to locate the most likely disease or department and generate guidance suggestions.
2. The cloud computing-based intelligent medical guidance system according to claim 1, characterized in that: The data collection port includes a symptom description collection module, a physiological index collection module, a medical history collection module, and a patient behavior collection module; the symptom description collection module is used to collect the patient's main complaint, symptom duration, and severity; the physiological index collection module is used to collect the patient's real-time monitored body temperature, blood pressure, blood oxygen, age, and gender; The medical history collection module is used to collect the patient's allergy history, chronic disease history, and surgical history; the patient behavior collection module is used to collect the patient's behavior log.
3. The cloud computing-based intelligent medical guidance system according to claim 2, characterized in that: The data matching port includes a data extraction module, a matching degree calculation module and a threshold judgment module. The data extraction module is used to extract the patient's current symptom set S = {S1, ..., S i ,...S n } and the typical symptom set D in the disease database j , j represents the disease number, i represents the patient's current symptom number, and the value of i is 1-n; the matching degree calculation module is used to calculate the matching degree between the patient's symptoms and each disease; the threshold judgment module is used to mark it as a potential disease when the matching degree exceeds the threshold.
4. The cloud computing-based intelligent medical guidance system according to claim 3, characterized in that: The disease tracing port includes a behavior association search module and a comprehensive recommendation value calculation module. The behavior association search module is used to extract patient behavior log data A m The number of co-occurrences with the patient's current symptom set S, and the patient's behavior log data A is calculated m The degree of compatibility with the patient's current symptom set S is used to obtain the behavioral association score B j The comprehensive recommendation value calculation module is used to calculate the recommendation value R according to the matching degree between the patient's current symptoms and the disease and the behavior association score. j The recommended values of each disease are arranged in descending order, and the corresponding departments of the top three diseases are recommended.
5. The cloud computing-based intelligent medical guidance system according to claim 4, characterized in that: The matching degree calculation module calculates the matching degree between the patient's symptoms and each disease. The matching degree calculation formula is: Among them, M j represents the matching degree of disease j, y i represents the weight of symptom i, N(S i ∩D j ) represents the intersection count of patient symptom i and typical symptoms of disease j, N(D j ) represents the total number of typical symptoms of disease j.
6. The cloud computing-based intelligent medical guidance system according to claim 5, characterized in that: The obtained behavior association score B j The specific steps include: S11. Extract patient behavior log data A m The number of co-occurrences with the patient's current symptom set S, m represents the patient's behavior log data number, and the value of m is 1-M; S12. Calculate patient behavior log data A m The compatibility with the patient's current symptom set S is calculated as: S13. Calculate patient behavior log data A m Behavioral association score B with disease j j , the calculation formula is: Among them, I(A m ∈E j ) is the indicator function, when the patient behavior data A m When I(A m ∈E j )=1, otherwise I(A m ∈E j )=0.
7. The cloud computing-based intelligent medical guidance system according to claim 6, characterized in that: The comprehensive recommendation value calculation module is used to calculate the recommendation value R according to the matching degree between the patient's current symptoms and the disease and the behavior association score. j , the recommended value R j The calculation formula is: R j =αM j +βB j ; Wherein, α and β are weight factors respectively, and α+β=1.