WO2023155441A1 - Medical resource recommendation method and apparatus, device, and storage medium - Google Patents
Medical resource recommendation method and apparatus, device, and storage medium Download PDFInfo
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- WO2023155441A1 WO2023155441A1 PCT/CN2022/122991 CN2022122991W WO2023155441A1 WO 2023155441 A1 WO2023155441 A1 WO 2023155441A1 CN 2022122991 W CN2022122991 W CN 2022122991W WO 2023155441 A1 WO2023155441 A1 WO 2023155441A1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/338—Presentation of query results
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Definitions
- the present application relates to the technical field of artificial intelligence, and in particular to a medical resource recommendation method, device, electronic equipment, and computer-readable storage medium.
- a method for recommending medical resources includes:
- Obtain patient information to be analyzed and available medical resources use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- the present application also provides a device for recommending medical resources, the device comprising:
- the feature extraction module is used to obtain historical patient data sets and historical medical resource sets, extract historical patient information feature sets and historical patient visit data from the historical patient data sets, and extract historical medical resource feature sets from the historical medical resource sets ;
- the sample construction module is used to use the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set, and use the historical patient information feature set and the historical medical resource feature set
- the feature set and the associated features are used to construct a medical recommendation sample set for historical patients
- the model training module is used to train the pre-built medical resource recommendation model based on the historical patient medical recommendation sample set using the contrastive divergence algorithm to obtain the trained medical resource recommendation model;
- the recommendation module is used to obtain the patient information to be analyzed and the available medical resources, and use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and the available medical resources to obtain recommended medical resource data.
- the present application also provides an electronic device, the electronic device comprising:
- the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the medical resource recommendation method as follows:
- Obtain patient information to be analyzed and available medical resources use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- the present application also provides a computer-readable storage medium, at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is executed by a processor in an electronic device to implement medical resource recommendation as described below method:
- Obtain patient information to be analyzed and available medical resources use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- FIG. 1 is a schematic flowchart of a method for recommending medical resources provided by an embodiment of the present application
- FIG. 2 is a schematic diagram of a detailed implementation process of one of the steps in the medical resource recommendation method shown in FIG. 1;
- FIG. 3 is a schematic diagram of a detailed implementation process of another step in the method for recommending medical resources shown in FIG. 1;
- FIG. 4 is a functional block diagram of a medical resource recommendation device provided by an embodiment of the present application.
- FIG. 5 is a schematic structural diagram of an electronic device implementing the medical resource recommendation method provided by an embodiment of the present application.
- An embodiment of the present application provides a method for recommending medical resources.
- the subject of execution of the method for recommending medical resources includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application.
- the method for recommending medical resources can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform.
- the server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
- the server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (ContentDelivery Network) , CDN), and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.
- FIG. 1 it is a schematic flowchart of a method for recommending medical resources provided by an embodiment of the present application.
- the method for recommending medical resources includes:
- the historical patient data set includes: historical patient medical record data, and historical patient visit data.
- the historical patient medical record data includes the patient's basic information, disease type, symptom description, physical condition, previous treatment conditions, pathological type, etc.
- the basic information includes: name, age, weight, gender, etc.
- the types of diseases include: cardiovascular disease, lung disease, rheumatic immune system disease, urinary system disease, kidney disease, diabetes and endocrine system disease, ENT diseases, digestive system diseases, gynecological diseases, nervous system diseases, eye diseases, orthopedic diseases, etc.
- the description of the symptoms is the abnormal sensation of the patient himself, such as dizziness, vertigo, wheezing, coughing, etc.
- the ECOG score is an index to understand the patient's general health status and tolerance to treatment from the patient's physical strength, wherein, 0 points indicate that the activity ability is completely normal, and There was no difference in activity ability before the onset of the disease; 1 point means that you can walk around freely and engage in light
- the historical patient visit data includes the hospital data and doctor data that the historical patient actually visited finally, such as hospital name, hospital ranking, hospital grade, doctor name, doctor's department, doctor's current position, doctor's specialty, etc.
- the historical medical resource set includes hospital data disclosed by domestic and foreign hospital official websites and authoritative websites, and doctor data disclosed by domestic and foreign hospital official websites and authoritative websites.
- the hospital data may specifically include hospital rankings, whether the hospital participates in formulating disease-related diagnosis and treatment guidelines, whether the hospital has doctors working in disease-related professional academic institutions, the number of disease-related papers published by the hospital, and disease-related clinical trials conducted by the hospital.
- the number of doctors and the number of expert teams in the hospital; the doctor data can include the name of the doctor, the center/project to which the doctor belongs, the department to which the doctor belongs, the current position of the doctor, the academic title of the doctor, the clinical expertise of the doctor, whether the doctor participates in the formulation of disease-related diagnosis and treatment guidelines, whether the doctor Affiliations in disease-related associations and foundations, number of disease-related papers published by physicians, etc.
- the historical patient data set may include various data forms such as text and images. Therefore, after obtaining the historical patient data set, it is necessary to use existing text conversion technology (such as OCR recognition technologies, etc.), and convert historical patient data sets in various data forms into text form in order to extract historical patient information feature sets and historical patient visit data.
- existing text conversion technology such as OCR recognition technologies, etc.
- the feature set of historical patient information extracted from the historical patient data set in S1 includes:
- Nouns and noun phrases are extracted from the word segmentation according to the results of the part-of-speech tagging, and according to the nouns and noun phrases, statistically obtain historical patient information characteristic frequencies, and generate frequent pattern trees according to the historical patient information characteristic frequencies;
- the following algorithm can be used to calculate the point mutual information value PMI of each feature in the candidate historical patient information feature set:
- the historical patient represents the name of the historical patient
- the feature represents each feature in the candidate historical patient information feature set
- the hit (“historical patient") represents the number obtained by searching the historical patient
- hit ("feature") represents the search for the feature
- the number obtained; hit("historical patient” and “feature") indicates the number obtained by searching the feature and the historical patient; the higher the point mutual information value, the higher the degree of association between the historical patient and the feature, when the When the point mutual information value is lower than the preset standard threshold, filter out the corresponding historical patient information features; when the point mutual information value is higher than the preset standard threshold, keep the corresponding historical patient information features, and combine to obtain the Describe the feature set of historical patient information.
- the same method as that for extracting the feature set of historical patient information can be used to extract the historical patient visit data and the feature set of historical medical resources, which will not be repeated here.
- using the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set includes:
- the key words and relational words irrelevant to the historical patient visit data are deleted to obtain the associated features between the historical patient information feature set and the historical medical resource feature set.
- the embodiment of the present application deletes key words and relational words that are not related to the historical patient visit data, and deletes features with too much missing data in historical patient information features and historical medical resource features, so as to screen out key words and phrases that are key to medical resource recommendation.
- the characteristics of the function such as the pathological type of the patient, whether the doctor participates in the formulation of disease-related diagnosis and treatment guidelines, etc., the selected features that play a key role in the recommendation of medical resources are used as the difference between the historical patient information feature set and the historical medical resource feature set. interrelationship features.
- the embodiment of the present application before constructing the historical patient medical recommendation sample set, it is necessary to unify the representation forms of the features in the historical patient information feature set, the historical medical resource feature set, and the associated features, for example, in the Among the historical patient information feature set, the historical medical resource feature set, and the associated features, some of the time representations are "****year**month**day**hour**minute**second", and some The time representation form is "**** year/** month/** day ** hour ** minute ** second", then the embodiment of this application can unify the time representation form as "**** year* *Month**day**hour**minute**second"; for another example, some of the names of hospitals in the feature are abbreviated names of hospitals, and some are full names of hospitals, so all the names of hospitals can be unified as hospitals Full name.
- the medical resource recommendation model is constructed based on a multi-layer restricted Boltzmann machine (RBM for short).
- RBM is a probability generation model based on deep learning, comprising a visible layer and a hidden layer, the visible layer is composed of a plurality of visible layer neurons, and the hidden layer is composed of a plurality of hidden layers Composed of neurons, there is a connection between the visible layer and the hidden layer, but there is no connection between the units in the layer, and the multi-layer restricted Boltzmann machine network connection is determined by the weight value under self-orientation.
- the multilayer restricted Boltzmann machine may be composed of three RBMs.
- the S3 includes:
- 80% of the samples in the historical patient medical recommendation sample set may be divided into a training set, and the remaining 20% of the samples may be divided into a test set.
- the contrastive divergence algorithm (contrastive divergence, CD algorithm for short) described in the embodiment of the present application reconstructs the value of the neuron of the visible layer through the value of the neuron of the hidden layer, according to the initial value of the neuron of the visible layer
- the parameters of the medical resource recommendation model are adjusted according to the errors of the reconstructed values of neurons in the visible layer.
- the S32 includes:
- the activation probability of the hidden layer neurons can be calculated by the following formula:
- f() represents the sigmoid function
- h j represents the value of the jth neuron in the hidden layer
- v represents the value of the visible layer neuron
- ⁇ (w ij , b i , b c )
- c j represents the The bias parameter of the jth neuron in the hidden layer
- w ij represents the weight value between the jth hidden layer neuron and the i-th visible layer neuron
- b i represents the weight value of the i-th neuron in the visible layer
- v i represents the value of the ith neuron in the visible layer.
- the activation probability of neurons in the visible layer can be calculated by the following formula:
- f() represents the sigmoid function
- v i represents the value of the ith neuron in the visible layer
- h represents the value of the neuron in the hidden layer
- ⁇ (w ij , bi , c j )
- c j represents the hidden
- the bias parameter of the j-th neuron in the layer w ij represents the weight value between the hidden layer neuron j and the visible layer neuron i
- b i represents the bias parameter of the i-th neuron in the visible layer
- b j represents the value of the jth neuron of the visible layer.
- S328 is executed to obtain and output the trained medical resource recommendation model.
- contrastive divergence algorithm described in S325 to adjust the weight value, the bias parameter of the visible layer neuron and the bias parameter of the hidden layer neuron, including:
- Step A using the normalized sample set to initialize the visible layer neuron, the state vector of the hidden layer neuron, the weight value between the visible layer neuron and the hidden layer neuron ;
- Step B using the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer to perform K-step Gibbs sampling to obtain the state vector and hidden The state vector of neurons in the layer, the state vector of neurons in the visible layer corresponding to time t, and the state vector of neurons in the hidden layer;
- the Gibbs sampling (Gibbs sampling) described in the embodiment of the present application is an algorithm used in Markov Monte Carlo (MCMC) in statistics, which is used to extract from a certain multivariate probability distribution when it is difficult to sample directly. Approximately draws a sequence of samples.
- MCMC Markov Monte Carlo
- Step C using the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to the time t-1, the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to the time t, performing circular calculation on the normalized sample set to obtain a comparison error value;
- Step D using the comparison error value to adjust the weight value, the bias parameter of the neuron in the visible layer, and the bias parameter of the neuron in the hidden layer.
- the embodiment of the present application uses the medical resource recommendation model trained by the contrastive divergence algorithm, so that the update direction of the model parameters depends on the result of the previous iteration, so that the algorithm jumps out of the local optimum, speeds up the model convergence speed, and greatly reduces the model iteration.
- the number of times greatly improves the recommendation efficiency and improves the accuracy of medical resource recommendation.
- the available medical resources include hospital data published by domestic and foreign hospital official websites and authoritative websites, and doctor data published by domestic and foreign hospital official websites and authoritative websites.
- said S4 includes:
- the patient to be analyzed can enter the relevant case data through the label filter items related to the condition set on the user terminal to obtain the patient's information to be analyzed.
- the historical patient information feature set and the historical medical resource feature set are respectively extracted from the historical patient data set and the historical medical resource set; the historical patient information feature set and the historical patient information feature set are extracted using the historical patient data
- the associated features between the historical medical resource feature sets, using the historical patient information feature set, the historical medical resource feature set, and the associated features construct a historical patient medical recommendation sample set, which is conducive to improving the relationship between patients and recommended medical treatment.
- the matching degree of resources improves the accuracy of recommended medical resources; the medical resource recommendation model is obtained by using contrastive divergence algorithm training, which speeds up model convergence, reduces the number of model iterations, and improves the accuracy of medical resource recommendation. Therefore, the medical resource recommendation method proposed in this application can solve the problem of low accuracy in medical resource recommendation.
- FIG. 4 it is a functional block diagram of a medical resource recommendation device provided by an embodiment of the present application.
- the medical resource recommendation apparatus 100 described in this application may be installed in electronic equipment.
- the medical resource recommendation device 100 may include a feature extraction module 101 , a sample construction module 102 , a model training module 103 and a recommendation module 104 .
- the module described in this application can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.
- each module/unit is as follows:
- the feature extraction module 101 is configured to acquire historical patient data sets and historical medical resource sets, extract historical patient information feature sets and historical patient visit data from the historical patient data sets, and extract historical medical treatment data from the historical medical resource sets.
- resource feature set
- the sample construction module 102 is configured to use the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set, and use the historical patient information feature set, the The historical medical resource feature set and the associated features are used to construct a historical patient medical recommendation sample set;
- the model training module 103 is used to train the pre-built medical resource recommendation model based on the historical patient medical recommendation sample set by using the contrastive divergence algorithm, so as to obtain the trained medical resource recommendation model;
- the recommendation module 104 is used to obtain patient information to be analyzed and available medical resources, and use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources to obtain recommended medical resource data.
- each module described in the medical resource recommendation device 100 in the embodiment of the present application adopts the same technical means as the medical resource recommendation method described in the above-mentioned Fig. 1 to Fig. 3 , and can generate the same The technical effect will not be repeated here.
- FIG. 5 it is a schematic structural diagram of an electronic device implementing a method for recommending medical resources provided by an embodiment of the present application.
- the electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include computer programs stored in the memory 11 and operable on the processor 10, such as medical resource recommendation program.
- the processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or A combination of multiple central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors and various control chips, etc.
- the processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, by running or executing programs or modules stored in the memory 11 (for example, executing medical resource recommendation program, etc.), and call the data stored in the memory 11 to execute various functions of the electronic device and process data.
- Control Unit Control Unit
- the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. , the computer-readable storage medium may be non-volatile or volatile.
- the storage 11 may be an internal storage unit of the electronic device in some embodiments, such as a mobile hard disk of the electronic device.
- the memory 11 can also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk equipped on an electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD ) card, flash card (Flash Card), etc.
- the memory 11 may also include both an internal storage unit of the electronic device and an external storage device.
- the memory 11 can not only be used to store application software and various data installed in the electronic device, such as the code of the medical resource recommendation program, etc., but also can be used to temporarily store the data that has been output or will be output.
- the communication bus 12 may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (EISA for short) bus or the like.
- PCI peripheral component interconnect
- EISA extended industry standard architecture
- the bus can be divided into address bus, data bus, control bus and so on.
- the bus is configured to realize connection and communication between the memory 11 and at least one processor 10 and the like.
- the communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface.
- the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device and other electronic devices.
- the user interface may be a display (Display) or an input unit (such as a keyboard (Keyboard)).
- the user interface may also be a standard wired interface or a wireless interface.
- the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like.
- the display may also be properly referred to as a display screen or a display unit, and is used for displaying information processed in the electronic device and for displaying a visualized user interface.
- FIG. 5 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 5 does not constitute a limitation to the electronic device 1, and may include fewer or more components, or combinations of certain components, or different arrangements of components.
- the electronic device may also include a power supply (such as a battery) for supplying power to various components.
- the power supply may be logically connected to the at least one processor 10 through a power management device, so that Realize functions such as charge management, discharge management, and power consumption management.
- the power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.
- the electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be repeated here.
- the medical resource recommendation program stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:
- Obtain patient information to be analyzed and available medical resources use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- the integrated modules/units of the electronic device 1 are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium.
- the computer-readable storage medium may be volatile or non-volatile.
- the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory).
- the present application also provides a computer-readable storage medium, the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can realize:
- Obtain patient information to be analyzed and available medical resources use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- the disclosed devices, devices and methods can be implemented in other ways.
- the device embodiments described above are only illustrative.
- the division of the modules is only a logical function division, and there may be other division methods in actual implementation.
- modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
- each functional module in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
- the above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software function modules.
- Blockchain essentially a decentralized database, is a series of data blocks associated with each other using cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify its Validity of information (anti-counterfeiting) and generation of the next block.
- the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
- AI artificial intelligence
- the embodiments of the present application may acquire and process relevant data based on artificial intelligence technology.
- artificial intelligence is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .
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Abstract
A medical resource recommendation method, comprising: using historical patient treatment data to extract an association feature between a historical patient information feature set and a historical medical resource feature set; using the historical patient information feature set, the historical medical resource feature set, and the association feature to construct a historical patient medical recommendation sample set; training a pre-constructed medical resource recommendation model on the basis of the historical patient medical recommendation sample set to obtain a trained medical resource recommendation model; and using the trained medical resource recommendation model to perform matching analysis on patient information to be analyzed and available medical resources to obtain recommended medical resource data. In addition, the present application further relates to the blockchain technology, and the historical patient medical recommendation sample set may be stored in a node of a blockchain. Also provided are a medical resource recommendation apparatus, an electronic device, and a storage medium. The medical resource recommendation accuracy can be improved.
Description
本申请要求于2022年02月15日提交中国专利局、申请号为202210137916.6,发明名称为“医疗资源推荐方法、装置、设备及存储介质”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese patent application with the application number 202210137916.6 filed on February 15, 2022, and the title of the invention is "medical resource recommendation method, device, equipment and storage medium", the entire content of which is incorporated by reference in this application.
本申请涉及人工智能技术领域,尤其涉及一种医疗资源推荐方法、装置、电子设备及计算机可读存储介质。The present application relates to the technical field of artificial intelligence, and in particular to a medical resource recommendation method, device, electronic equipment, and computer-readable storage medium.
求医问诊是人们生活中常遇到的事情,选择合适的医院和医生至关重要,然而发明人意识到大多数患者由于医学知识的限制,对于自身的症状无法正确评估,同时互联网信息庞杂,患者无从分辨真伪,由于缺乏医疗专业知识,也很难找到擅长自己病情的医院和医生,从而降低医疗效率并浪费了医疗资源。通过配置医疗推荐系统,能够帮助病人和医生大大缩短就诊时间,节省人力物力。Seeking a doctor is a common thing in people's life. It is very important to choose the right hospital and doctor. However, the inventor realized that most patients cannot correctly evaluate their own symptoms due to the limitation of medical knowledge. At the same time, the Internet information is huge and complex. Patients have no way of distinguishing authenticity from falsehoods, and due to lack of medical expertise, it is also difficult to find hospitals and doctors who are good at their own conditions, thereby reducing medical efficiency and wasting medical resources. By configuring the medical recommendation system, it can help patients and doctors greatly shorten the time of seeing a doctor and save manpower and material resources.
现有的医疗推荐系统往往采用固定的搜索方式,或单纯使用医生和病人的历史交互信息作为输入进行相关医疗信息的推荐,这种方法推荐的医疗资源不够准确。Existing medical recommendation systems often use fixed search methods, or simply use historical interaction information between doctors and patients as input to recommend relevant medical information. The medical resources recommended by this method are not accurate enough.
发明内容Contents of the invention
为实现上述目的,本申请提供的一种医疗资源推荐方法,包括:In order to achieve the above purpose, a method for recommending medical resources provided by this application includes:
获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;
利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;
基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;
获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
本申请还提供一种医疗资源推荐装置,所述装置包括:The present application also provides a device for recommending medical resources, the device comprising:
特征提取模块,用于获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;The feature extraction module is used to obtain historical patient data sets and historical medical resource sets, extract historical patient information feature sets and historical patient visit data from the historical patient data sets, and extract historical medical resource feature sets from the historical medical resource sets ;
样本构建模块,用于利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;The sample construction module is used to use the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set, and use the historical patient information feature set and the historical medical resource feature set The feature set and the associated features are used to construct a medical recommendation sample set for historical patients;
模型训练模块,用于基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;The model training module is used to train the pre-built medical resource recommendation model based on the historical patient medical recommendation sample set using the contrastive divergence algorithm to obtain the trained medical resource recommendation model;
推荐模块,用于获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。The recommendation module is used to obtain the patient information to be analyzed and the available medical resources, and use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and the available medical resources to obtain recommended medical resource data.
本申请还提供一种电子设备,所述电子设备包括:The present application also provides an electronic device, the electronic device comprising:
至少一个处理器;以及,at least one processor; and,
与所述至少一个处理器通信连接的存储器;其中,a memory communicatively coupled to the at least one processor; wherein,
所述存储器存储有可被所述至少一个处理器执行的计算机程序,所述计算机程序被所述至少一个处理器执行,以使所述至少一个处理器能够执行如下所述的医疗资源推荐方法:The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the medical resource recommendation method as follows:
获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;
利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;
基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;
获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
本申请还提供一种计算机可读存储介质,所述计算机可读存储介质中存储有至少一个计算机程序,所述至少一个计算机程序被电子设备中的处理器执行以实现如下所述的医疗资源推荐方法:The present application also provides a computer-readable storage medium, at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is executed by a processor in an electronic device to implement medical resource recommendation as described below method:
获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;
利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;
基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;
获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
图1为本申请一实施例提供的医疗资源推荐方法的流程示意图;FIG. 1 is a schematic flowchart of a method for recommending medical resources provided by an embodiment of the present application;
图2为图1所示医疗资源推荐方法中其中一个步骤的详细实施流程示意图;FIG. 2 is a schematic diagram of a detailed implementation process of one of the steps in the medical resource recommendation method shown in FIG. 1;
图3为图1所示医疗资源推荐方法中其中另一个步骤的详细实施流程示意图;FIG. 3 is a schematic diagram of a detailed implementation process of another step in the method for recommending medical resources shown in FIG. 1;
图4为本申请一实施例提供的医疗资源推荐装置的功能模块图;FIG. 4 is a functional block diagram of a medical resource recommendation device provided by an embodiment of the present application;
图5为本申请一实施例提供的实现所述医疗资源推荐方法的电子设备的结构示意图。FIG. 5 is a schematic structural diagram of an electronic device implementing the medical resource recommendation method provided by an embodiment of the present application.
本申请目的的实现、功能特点及优点将结合实施例,参照附图做进一步说明。The realization, functional features and advantages of the present application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.
应当理解,此处所描述的具体实施例仅仅用以解释本申请,并不用于限定本申请。It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.
本申请实施例提供一种医疗资源推荐方法。所述医疗资源推荐方法的执行主体包括但不限于服务端、终端等能够被配置为执行本申请实施例提供的该方法的电子设备中的至少一种。换言之,所述医疗资源推荐方法可以由安装在终端设备或服务端设备的软件或硬件来执行,所述软件可以是区块链平台。所述服务端包括但不限于:单台服务器、服务器集群、云端服务器或云端服务器集群等。所述服务器可以是独立的服务器,也可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、内容分发网络(ContentDelivery Network,CDN)、以及大数据和人工智能平台等基础云计算服务的云服务器。An embodiment of the present application provides a method for recommending medical resources. The subject of execution of the method for recommending medical resources includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for recommending medical resources can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (ContentDelivery Network) , CDN), and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.
参照图1所示,为本申请一实施例提供的医疗资源推荐方法的流程示意图。在本实施例中,所述医疗资源推荐方法包括:Referring to FIG. 1 , it is a schematic flowchart of a method for recommending medical resources provided by an embodiment of the present application. In this embodiment, the method for recommending medical resources includes:
S1、获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集。S1. Acquire historical patient data sets and historical medical resource sets, extract historical patient information feature sets and historical patient visit data from the historical patient data sets, and extract historical medical resource feature sets from the historical medical resource sets.
本申请其中一个实施例中,所述历史患者数据集包括:历史患者病历数据、历史患者就诊数据。In one embodiment of the present application, the historical patient data set includes: historical patient medical record data, and historical patient visit data.
本申请实施例中,所述历史患者病历数据包括患者的基本信息、疾病类型、症状描述、体力状况、既往治疗情况、病理类型等。其中,所述基本信息包括:姓名、年龄、体重、性别等;所述疾病类型包括:心血管疾病、肺部疾病、风湿免疫系统疾病、泌尿系统疾病、肾病、糖尿病与内分泌系统疾病、耳鼻喉科疾病、消化系统疾病、妇科疾病、神经系统疾病、眼科疾病、骨科疾病等;所述症状描述是患者本人的异常感觉,如头晕、目眩、气喘、咳嗽等;所述体力状况可以通过美国东部肿瘤协作组(EasternCooperative Oncology Group,ECOG)评分标准来判定,所述ECOG评分是从患者的体力来了解其一般健康状况和 对治疗耐受能力的指标,其中,0分表示活动能力完全正常,与起病前活动能力无任何差异;1分表示能自由走动及从事轻体力活动,包括一般家务或办公室工作,但不能从事较重的体力活动;2分表示能自由走动及生活自理,但已丧失工作能力,日间不少于一半时间可以起床活动;3分表示生活仅能部分自理,日间一半以上时间卧床或坐轮椅;4分表示卧床不起,生活不能自理;所述既往治疗情况是患者曾经治疗情况,包括是否接受过手术治疗等。In the embodiment of the present application, the historical patient medical record data includes the patient's basic information, disease type, symptom description, physical condition, previous treatment conditions, pathological type, etc. Among them, the basic information includes: name, age, weight, gender, etc.; the types of diseases include: cardiovascular disease, lung disease, rheumatic immune system disease, urinary system disease, kidney disease, diabetes and endocrine system disease, ENT diseases, digestive system diseases, gynecological diseases, nervous system diseases, eye diseases, orthopedic diseases, etc.; the description of the symptoms is the abnormal sensation of the patient himself, such as dizziness, vertigo, wheezing, coughing, etc.; According to the Eastern Cooperative Oncology Group (ECOG) score standard, the ECOG score is an index to understand the patient's general health status and tolerance to treatment from the patient's physical strength, wherein, 0 points indicate that the activity ability is completely normal, and There was no difference in activity ability before the onset of the disease; 1 point means that you can walk around freely and engage in light physical activities, including general housework or office work, but you cannot engage in heavy physical activities; 2 points means you can move around freely and take care of yourself, but you have lost Ability to work, able to get up and move about at least half of the time during the day; 3 points means that they can only take care of themselves partially, and more than half of the time during the day is bedridden or in a wheelchair; 4 points means that they are bedridden and unable to take care of themselves; the previous treatment status is The patient's previous treatment conditions, including whether they have received surgical treatment, etc.
进一步地,所述历史患者就诊数据包括历史患者最终实际就诊的医院数据和医生数据等,如医院名称,医院排名、医院等级、医生姓名、医生所属科室、医生现任职务、医生擅长方向等。Further, the historical patient visit data includes the hospital data and doctor data that the historical patient actually visited finally, such as hospital name, hospital ranking, hospital grade, doctor name, doctor's department, doctor's current position, doctor's specialty, etc.
进一步地,所述历史医疗资源集包括国内外医院官网以及权威网站公开的医院数据以及国内外医院官网以及权威网站公开的医生数据。Further, the historical medical resource set includes hospital data disclosed by domestic and foreign hospital official websites and authoritative websites, and doctor data disclosed by domestic and foreign hospital official websites and authoritative websites.
其中,所述医院数据具体可以包括医院排名、医院是否参与制定疾病相关的诊疗指南、医院是否有医生任职于疾病相关的专业学术机构、医院发表的疾病相关论文数、医院开展的疾病相关临床试验数以及医院的专家团队数量;所述医生数据可以包括医生姓名、医生所属中心/项目、医生所属科室、医生现任职务、医生学术头衔、医生临床专长、医生是否参与制定疾病相关诊疗指南、医生是否任职于疾病相关协会和基金会以及医生发表的疾病相关论文数等。Among them, the hospital data may specifically include hospital rankings, whether the hospital participates in formulating disease-related diagnosis and treatment guidelines, whether the hospital has doctors working in disease-related professional academic institutions, the number of disease-related papers published by the hospital, and disease-related clinical trials conducted by the hospital. The number of doctors and the number of expert teams in the hospital; the doctor data can include the name of the doctor, the center/project to which the doctor belongs, the department to which the doctor belongs, the current position of the doctor, the academic title of the doctor, the clinical expertise of the doctor, whether the doctor participates in the formulation of disease-related diagnosis and treatment guidelines, whether the doctor Affiliations in disease-related associations and foundations, number of disease-related papers published by physicians, etc.
本申请其中一个实施例中,所述历史患者数据集可包括文本、图像等多种数据形式,因此,在获取到所述历史患者数据集后,需要利用现有的文本转换技术(如OCR识别技术等),将各种数据形式的历史患者数据集统一转换为文本形式,以便提取历史患者信息特征集以及历史患者就诊数据。In one of the embodiments of the present application, the historical patient data set may include various data forms such as text and images. Therefore, after obtaining the historical patient data set, it is necessary to use existing text conversion technology (such as OCR recognition technologies, etc.), and convert historical patient data sets in various data forms into text form in order to extract historical patient information feature sets and historical patient visit data.
详细地,S1中所述从所述历史患者数据集中提取历史患者信息特征集,包括:In detail, the feature set of historical patient information extracted from the historical patient data set in S1 includes:
将所述历史患者数据集统一转换为文本格式,得到历史患者文本数据集;Converting the historical patient data set into a text format uniformly to obtain the historical patient text data set;
对所述历史患者文本数据集进行分词及词性标注,得到分词及词性标注的结果;Carrying out word segmentation and part-of-speech tagging on the historical patient text data set to obtain the results of word segmentation and part-of-speech tagging;
根据所述词性标注的结果从所述分词中提取名词及名词短语,并根据所述名词及名词短语,统计得到历史患者信息特征频率,根据所述历史患者信息特征频率生成频繁模式树;Nouns and noun phrases are extracted from the word segmentation according to the results of the part-of-speech tagging, and according to the nouns and noun phrases, statistically obtain historical patient information characteristic frequencies, and generate frequent pattern trees according to the historical patient information characteristic frequencies;
识别所述频繁模式树中的特征,得到候选历史患者信息特征集;identifying features in the frequent pattern tree to obtain a feature set of candidate historical patient information;
计算所述候选历史患者信息特征集中各个特征的点互信息值,并从所述候选历史患者信息特征集中过滤掉点互信息值小于预设的标准阈值的历史患者信息特征,得到历史患者信息特征集。Calculate the point mutual information value of each feature in the candidate historical patient information feature set, and filter out the historical patient information features whose point mutual information value is less than a preset standard threshold from the candidate historical patient information feature set to obtain the historical patient information feature set.
本申请其中一个实施例中,可以利用下述算法计算所述候选历史患者信息特征集中各个特征的点互信息值PMI:In one of the embodiments of the present application, the following algorithm can be used to calculate the point mutual information value PMI of each feature in the candidate historical patient information feature set:
其中,历史患者表示历史患者姓名,特征表示候选历史患者信息特征集中各个特征;所述hit(“历史患者”)表示搜索所述历史患者得到的数量;hit(“特征”)表示搜索所述特征得到的数量;hit("历史患者“and”特征“)表示搜索所述特征和所述历史患者得到的数量;所述点互信息值越高,代表历史患者和特征关联程度越高,当所述点互信息值低于预设的标准阈值时,过滤掉对应的历史患者信息特征;当所述点互信息值高于预设的标准阈值时,保留对应的历史患者信息特征,组合得到所述历史患者信息特征集。Among them, the historical patient represents the name of the historical patient, and the feature represents each feature in the candidate historical patient information feature set; the hit ("historical patient") represents the number obtained by searching the historical patient; hit ("feature") represents the search for the feature The number obtained; hit("historical patient" and "feature") indicates the number obtained by searching the feature and the historical patient; the higher the point mutual information value, the higher the degree of association between the historical patient and the feature, when the When the point mutual information value is lower than the preset standard threshold, filter out the corresponding historical patient information features; when the point mutual information value is higher than the preset standard threshold, keep the corresponding historical patient information features, and combine to obtain the Describe the feature set of historical patient information.
进一步,本申请实施例可以采取与上述提取历史患者信息特征集相同的方法提取所述历史患者就诊数据以及所述历史医疗资源特征集,这里不再赘述。Further, in this embodiment of the present application, the same method as that for extracting the feature set of historical patient information can be used to extract the historical patient visit data and the feature set of historical medical resources, which will not be repeated here.
S2、利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集。S2. Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, and use the historical patient information feature set, the historical medical resource feature set and all Based on the above association features, a medical recommendation sample set for historical patients is constructed.
详细地,S2中所述利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,包括:Specifically, in S2, using the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set includes:
对所述历史患者信息特征集中每个历史患者信息特征及所述历史医疗资源特征集中每个历史医疗资源特征进行分词,从所述分词结果中提取关键词汇及关系词汇;Segmenting each historical patient information feature in the historical patient information feature set and each historical medical resource feature in the historical medical resource feature set, and extracting key words and relational words from the word segmentation results;
删除与所述历史患者就诊数据无关的所述关键词汇及关系词汇,得到所述历史患者信息特征集与所述历史医疗资源特征集之间的关联特征。The key words and relational words irrelevant to the historical patient visit data are deleted to obtain the associated features between the historical patient information feature set and the historical medical resource feature set.
具体地,本申请实施例删除与所述历史患者就诊数据无关的关键词汇及关系词汇,删除历史患者信息特征及历史医疗资源特征中缺失数据过多的特征,从而筛选出对医疗资源推荐起关键作用的特征,例如患者的病理类型、医生是否参与制定疾病相关诊疗指南等,将筛选出的对医疗资源推荐起关键作用的特征作为所述历史患者信息特征集与所述历史医疗资源特征集之间的关联特征。Specifically, the embodiment of the present application deletes key words and relational words that are not related to the historical patient visit data, and deletes features with too much missing data in historical patient information features and historical medical resource features, so as to screen out key words and phrases that are key to medical resource recommendation. The characteristics of the function, such as the pathological type of the patient, whether the doctor participates in the formulation of disease-related diagnosis and treatment guidelines, etc., the selected features that play a key role in the recommendation of medical resources are used as the difference between the historical patient information feature set and the historical medical resource feature set. interrelationship features.
进一步,本申请实施例中在构建历史患者医疗推荐样本集之前,需要统一所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征中特征的表示形式,例如,在所 述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征中,有的时间表示形式为“****年**月**日**时**分**秒”,有的时间表示形式为“****年/**月/**日**时**分**秒”,那么本申请实施例可将时间表示形式都统一为“****年**月**日**时**分**秒”;又如,在所述特征中医院名称有的为医院名称简称,有的为医院名称全称,那么可将医院名称全部都统一为医院名称全称。Further, in the embodiment of the present application, before constructing the historical patient medical recommendation sample set, it is necessary to unify the representation forms of the features in the historical patient information feature set, the historical medical resource feature set, and the associated features, for example, in the Among the historical patient information feature set, the historical medical resource feature set, and the associated features, some of the time representations are "****year**month**day**hour**minute**second", and some The time representation form is "**** year/** month/** day ** hour ** minute ** second", then the embodiment of this application can unify the time representation form as "**** year* *Month**day**hour**minute**second"; for another example, some of the names of hospitals in the feature are abbreviated names of hospitals, and some are full names of hospitals, so all the names of hospitals can be unified as hospitals Full name.
S3、基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型。S3. Based on the historical patient medical recommendation sample set, use the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain a trained medical resource recommendation model.
本申请实施中,所述医疗资源推荐模型是基于多层受限玻尔兹曼机(restricted Boltzmann machine,简称RBM)构建。其中,所述RBM是一种基于深度学习的概率生成模型,包含一个可视层和一个隐藏层,所述可视层由多个可视层神经元组成,所述隐藏层由多个隐藏层神经元组成,所述可视层与隐藏层之间存在连接,但是层内的单元间不存在连接,多层受限玻尔兹曼机网络连接是通过自定向下的权重值来确定的。本申请其中一个实施例中,所述多层受限玻尔兹曼机可以由3个RBM构成。In the implementation of this application, the medical resource recommendation model is constructed based on a multi-layer restricted Boltzmann machine (RBM for short). Wherein, the RBM is a probability generation model based on deep learning, comprising a visible layer and a hidden layer, the visible layer is composed of a plurality of visible layer neurons, and the hidden layer is composed of a plurality of hidden layers Composed of neurons, there is a connection between the visible layer and the hidden layer, but there is no connection between the units in the layer, and the multi-layer restricted Boltzmann machine network connection is determined by the weight value under self-orientation. In one of the embodiments of the present application, the multilayer restricted Boltzmann machine may be composed of three RBMs.
详细地,参阅图2所示,所述S3包括:In detail, referring to Figure 2, the S3 includes:
S31、将所述历史患者医疗推荐样本集划分为训练集和测试集;S31. Divide the historical patient medical recommendation sample set into a training set and a test set;
S32、根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型;S32. According to the training set, use the contrastive divergence algorithm to adjust the parameters of the medical resource recommendation model, and iteratively train the medical resource recommendation model to obtain a trained medical resource recommendation model;
S33、利用所述测试集对所述经过训练的医疗资源推荐模型进行测试和调整,得到训练完成的医疗资源推荐模型。S33. Use the test set to test and adjust the trained medical resource recommendation model to obtain a trained medical resource recommendation model.
本申请其中一个实施例,可将所述历史患者医疗推荐样本集中80%的样本划分为训练集,剩余20%的样本划分为测试集。In one embodiment of the present application, 80% of the samples in the historical patient medical recommendation sample set may be divided into a training set, and the remaining 20% of the samples may be divided into a test set.
本申请实施例中所述对比散度算法(contrastive divergence,简称CD算法)通过所述隐藏层的神经元的值重构所述可视层神经元的值,根据可视层神经元的初始值与所述可视层神经元的重构值的误差来调整医疗资源推荐模型的参数。The contrastive divergence algorithm (contrastive divergence, CD algorithm for short) described in the embodiment of the present application reconstructs the value of the neuron of the visible layer through the value of the neuron of the hidden layer, according to the initial value of the neuron of the visible layer The parameters of the medical resource recommendation model are adjusted according to the errors of the reconstructed values of neurons in the visible layer.
进一步地,参阅图3所示,所述S32包括:Further, referring to Fig. 3, the S32 includes:
S321、将所述训练集中的所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征转化为特征向量,并对所述特征向量进行均值、方差、白化处理,并对所述处理后的特征向量按照从小到大的顺序均匀归一到0、1之间,得到归一化的样本集;S321. Convert the historical patient information feature set, the historical medical resource feature set, and the associated features in the training set into feature vectors, and perform mean value, variance, and whitening processing on the feature vectors, and calculate all The processed eigenvectors are evenly normalized to between 0 and 1 in order of small to large, to obtain a normalized sample set;
S322、初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值,及所述可视层神经元的偏置参数和隐藏层神经元的偏置参数;S322. Initialize the weight value between the neurons in the visible layer and the neurons in the hidden layer in the medical resource recommendation model, and the bias parameters of the neurons in the visible layer and the bias parameters of the neurons in the hidden layer;
S323、利用所述归一化的样本集、所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数,对所述可视层神经元和所述隐藏层神经元进行循环迭代,并计算所述隐藏层神经元的激活概率;S323. Using the normalized sample set, the weight value, the bias parameter of the neuron in the visible layer, and the bias parameter of the neuron in the hidden layer, calculate the neuron in the visible layer and the neuron in the hidden layer. The hidden layer neuron is looped and iterated, and the activation probability of the hidden layer neuron is calculated;
本申请其中一个实施例可通过如下公式计算所述隐藏层神经元的激活概率:In one embodiment of the present application, the activation probability of the hidden layer neurons can be calculated by the following formula:
其中,f()表示sigmoid函数,h
j表示隐藏层第j个神经元的值,v表示可视层神经元的值,θ=(w
ij,b
i,b
c),c
j表示所述隐藏层第j个神经元的偏置参数,w
ij表示连接第j个隐藏层神经元和第i个可视层神经元之间的权重值,b
i表示可视层第i个神经元的偏置参数,v
i表示所述可视层第i个神经元的值。
Among them, f() represents the sigmoid function, h j represents the value of the jth neuron in the hidden layer, v represents the value of the visible layer neuron, θ=(w ij , b i , b c ), c j represents the The bias parameter of the jth neuron in the hidden layer, w ij represents the weight value between the jth hidden layer neuron and the i-th visible layer neuron, b i represents the weight value of the i-th neuron in the visible layer Bias parameter, v i represents the value of the ith neuron in the visible layer.
S324、利用所述循环迭代后的隐藏层神经元,反向循环迭代所述可视层神经元,并计算所述可视层神经元的激活概率;S324. Using the hidden layer neurons after the loop iteration, iterate the visible layer neurons in a reverse loop, and calculate the activation probability of the visible layer neurons;
本申请其中一个实施例可通过如下公式计算所述可视层神经元的激活概率:In one embodiment of the present application, the activation probability of neurons in the visible layer can be calculated by the following formula:
其中f()表示sigmoid函数,v
i表示可视层第i个神经元的值,h表示隐藏层神经元的值,θ=(w
ij,b
i,c
j),c
j表示所述隐藏层第j个神经元的偏置参数,w
ij表示连接隐藏层神经元j和可视层神经元i之间的权重值,b
i表示可视层第i个神经元的偏置参数,b
j表示所述可视层第j个神经元的值。
Where f() represents the sigmoid function, v i represents the value of the ith neuron in the visible layer, h represents the value of the neuron in the hidden layer, θ=(w ij , bi , c j ), c j represents the hidden The bias parameter of the j-th neuron in the layer, w ij represents the weight value between the hidden layer neuron j and the visible layer neuron i, b i represents the bias parameter of the i-th neuron in the visible layer, b j represents the value of the jth neuron of the visible layer.
S325、根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数进行调整,利用调整后的所述权重值及所述偏置参数,重新计算所述可视层神经元的激活概率,根据所述可视层神经元的激活概率的最大值对应的医疗资源推荐模型的输出结果作为预测推荐医疗资源;S325. According to the activation probability of the neuron in the hidden layer and the activation probability of the neuron in the visible layer, use a contrastive divergence algorithm to calculate the weight value, the bias parameter of the neuron in the visible layer, and the hidden adjust the bias parameters of the neurons in the visible layer, use the adjusted weight value and the bias parameters to recalculate the activation probability of the neurons in the visible layer, and according to the activation probability of the neurons in the visible layer The output of the medical resource recommendation model corresponding to the maximum value is used as the predicted recommended medical resource;
S326、利用损失函数计算所述预测推荐医疗资源与所述历史患者就诊数据之间的损失值,并判断所述损失值是否小于预设的损失阈值;S326. Using a loss function to calculate the loss value between the predicted and recommended medical resources and the historical patient visit data, and determine whether the loss value is less than a preset loss threshold;
当所述损失值大于或者等于预设的损失阈值时,执行S327,调整所述医疗资源推荐模型的参数,并返回所述S322对应的步骤;When the loss value is greater than or equal to the preset loss threshold, execute S327, adjust the parameters of the medical resource recommendation model, and return to the step corresponding to S322;
当所述损失值小于预设的损失阈值时,执行S328,得到并输出训练完成的医疗资源推荐模型。When the loss value is less than the preset loss threshold, S328 is executed to obtain and output the trained medical resource recommendation model.
进一步地,S325中所述利用对比散度算法对所述权重值、所述可视层神经元的偏置参 数及所述隐藏层神经元的偏置参数进行调整,包括:Further, the use of contrastive divergence algorithm described in S325 to adjust the weight value, the bias parameter of the visible layer neuron and the bias parameter of the hidden layer neuron, including:
步骤A、利用所述归一化的样本集初始化所述可视层神经元、所述隐藏层神经元的状态向量、所述可视层神经元和所述隐藏层神经元之间的权重值;Step A, using the normalized sample set to initialize the visible layer neuron, the state vector of the hidden layer neuron, the weight value between the visible layer neuron and the hidden layer neuron ;
步骤B、利用所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,执行K步吉布斯采样,得到t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量;Step B, using the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer to perform K-step Gibbs sampling to obtain the state vector and hidden The state vector of neurons in the layer, the state vector of neurons in the visible layer corresponding to time t, and the state vector of neurons in the hidden layer;
本申请实施例中所述吉布斯采样(Gibbs sampling)是统计学中用于马尔科夫蒙特卡洛(MCMC)的一种算法,用于在难以直接采样时从某一多变量概率分布中近似抽取样本序列。The Gibbs sampling (Gibbs sampling) described in the embodiment of the present application is an algorithm used in Markov Monte Carlo (MCMC) in statistics, which is used to extract from a certain multivariate probability distribution when it is difficult to sample directly. Approximately draws a sequence of samples.
步骤C、利用所述t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量,对所述归一化的样本集进行循环计算得到对比误差值;Step C, using the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to the time t-1, the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to the time t, performing circular calculation on the normalized sample set to obtain a comparison error value;
步骤D、利用所述对比误差值对所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数进行调整。Step D, using the comparison error value to adjust the weight value, the bias parameter of the neuron in the visible layer, and the bias parameter of the neuron in the hidden layer.
本申请实施例利用对比散度算法训练得到的医疗资源推荐模型,使得模型参数的更新方向依赖于上一次迭代的结果,使得算法跳出局部最优,加快了模型收敛速度,较大地降低了模型迭代次数,较大地提高了推荐效率,提升了医疗资源推荐准确性。The embodiment of the present application uses the medical resource recommendation model trained by the contrastive divergence algorithm, so that the update direction of the model parameters depends on the result of the previous iteration, so that the algorithm jumps out of the local optimum, speeds up the model convergence speed, and greatly reduces the model iteration. The number of times greatly improves the recommendation efficiency and improves the accuracy of medical resource recommendation.
S4、获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。S4. Obtain patient information to be analyzed and available medical resources, and use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources to obtain recommended medical resource data.
本申请实施例中,所述可用医疗资源包括国内外医院官网以及权威网站公开的医院数据以及国内外医院官网以及权威网站公开的医生数据。In the embodiment of the present application, the available medical resources include hospital data published by domestic and foreign hospital official websites and authoritative websites, and doctor data published by domestic and foreign hospital official websites and authoritative websites.
详细地,所述S4,包括:In detail, said S4 includes:
获取用户端上传的待分析患者信息及可用医疗资源;Obtain patient information to be analyzed and available medical resources uploaded by the client;
从所述待分析患者信息中提取待分析患者信息特征及从所述可用医疗资源中提取可用医疗资源特征;extracting the characteristics of the patient information to be analyzed from the patient information to be analyzed and the characteristics of available medical resources from the available medical resources;
利用所述训练完成的医疗资源推荐模型对所述待分析患者信息特征及所述可用医疗资源特征进行匹配分析,得到推荐医疗资源数据;Using the trained medical resource recommendation model to perform matching analysis on the characteristics of the patient information to be analyzed and the characteristics of the available medical resources to obtain recommended medical resource data;
将所述推荐医疗资源数据反馈给所述用户端。Feedback the recommended medical resource data to the client.
本申请其中一个实施,待分析患者可通过用户端设置的与病情相关的标签筛选项输入相关的病例数据,得到待分析患者信息。In one implementation of the present application, the patient to be analyzed can enter the relevant case data through the label filter items related to the condition set on the user terminal to obtain the patient's information to be analyzed.
本申请其中一个实施,所述推荐医疗资源数据中的医院数据和医生数据的选项可以为多项,当前用户可根据自身的实际需求再次进行筛选。In one implementation of the present application, there may be multiple options for hospital data and doctor data in the recommended medical resource data, and the current user can re-screen according to his actual needs.
本申请实施例通过分别从所述历史患者数据集、所述历史医疗资源集中提取历史患者信息特征集及历史医疗资源特征集;利用所述历史患者就诊数据,提取所述历史患者信息特征集与所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集,有利于提高患者与推荐医疗资源的匹配度,进而提高推荐医疗资源准确度;利用对比散度算法训练得到医疗资源推荐模型,加快模型收敛速度,降低了模型迭代次数,提升了医疗资源推荐准确性。因此本申请提出的医疗资源推荐方法,可以解决医疗资源推荐准确度低的问题。In the embodiment of the present application, the historical patient information feature set and the historical medical resource feature set are respectively extracted from the historical patient data set and the historical medical resource set; the historical patient information feature set and the historical patient information feature set are extracted using the historical patient data The associated features between the historical medical resource feature sets, using the historical patient information feature set, the historical medical resource feature set, and the associated features, construct a historical patient medical recommendation sample set, which is conducive to improving the relationship between patients and recommended medical treatment. The matching degree of resources improves the accuracy of recommended medical resources; the medical resource recommendation model is obtained by using contrastive divergence algorithm training, which speeds up model convergence, reduces the number of model iterations, and improves the accuracy of medical resource recommendation. Therefore, the medical resource recommendation method proposed in this application can solve the problem of low accuracy in medical resource recommendation.
如图4所示,是本申请一实施例提供的医疗资源推荐装置的功能模块图。本申请所述医疗资源推荐装置100可以安装于电子设备中。根据实现的功能,所述医疗资源推荐装置100可以包括特征提取模块101、样本构建模块102、模型训练模块103及推荐模块104。本申请所述模块也可以称之为单元,是指一种能够被电子设备处理器所执行,并且能够完成固定功能的一系列计算机程序段,其存储在电子设备的存储器中。As shown in FIG. 4 , it is a functional block diagram of a medical resource recommendation device provided by an embodiment of the present application. The medical resource recommendation apparatus 100 described in this application may be installed in electronic equipment. According to the functions realized, the medical resource recommendation device 100 may include a feature extraction module 101 , a sample construction module 102 , a model training module 103 and a recommendation module 104 . The module described in this application can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of the electronic device and can complete fixed functions, and are stored in the memory of the electronic device.
在本实施例中,关于各模块/单元的功能如下:In this embodiment, the functions of each module/unit are as follows:
所述特征提取模块101,用于获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;The feature extraction module 101 is configured to acquire historical patient data sets and historical medical resource sets, extract historical patient information feature sets and historical patient visit data from the historical patient data sets, and extract historical medical treatment data from the historical medical resource sets. resource feature set;
所述样本构建模块102,用于利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;The sample construction module 102 is configured to use the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set, and use the historical patient information feature set, the The historical medical resource feature set and the associated features are used to construct a historical patient medical recommendation sample set;
所述模型训练模块103,用于基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;The model training module 103 is used to train the pre-built medical resource recommendation model based on the historical patient medical recommendation sample set by using the contrastive divergence algorithm, so as to obtain the trained medical resource recommendation model;
所述推荐模块104,用于获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。The recommendation module 104 is used to obtain patient information to be analyzed and available medical resources, and use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources to obtain recommended medical resource data.
详细地,本申请实施例中所述医疗资源推荐装置100中所述的各模块在使用时采用与上述图1至图3中所述的医疗资源推荐方法一样的技术手段,并能够产生相同的技术效果,这里不再赘述。In detail, each module described in the medical resource recommendation device 100 in the embodiment of the present application adopts the same technical means as the medical resource recommendation method described in the above-mentioned Fig. 1 to Fig. 3 , and can generate the same The technical effect will not be repeated here.
如图5所示,是本申请一实施例提供的实现医疗资源推荐方法的电子设备的结构示意图。As shown in FIG. 5 , it is a schematic structural diagram of an electronic device implementing a method for recommending medical resources provided by an embodiment of the present application.
所述电子设备1可以包括处理器10、存储器11、通信总线12以及通信接口13,还可以包括存储在所述存储器11中并可在所述处理器10上运行的计算机程序,如医疗资源推荐程序。The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include computer programs stored in the memory 11 and operable on the processor 10, such as medical resource recommendation program.
其中,所述处理器10在一些实施例中可以由集成电路组成,例如可以由单个封装的集成电路所组成,也可以是由多个相同功能或不同功能封装的集成电路所组成,包括一个或者多个中央处理器(Central Processing unit,CPU)、微处理器、数字处理芯片、图形处理器及各种控制芯片的组合等。所述处理器10是所述电子设备的控制核心(Control Unit),利用各种接口和线路连接整个电子设备的各个部件,通过运行或执行存储在所述存储器11内的程序或者模块(例如执行医疗资源推荐程序等),以及调用存储在所述存储器11内的数据,以执行电子设备的各种功能和处理数据。Wherein, the processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or A combination of multiple central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, by running or executing programs or modules stored in the memory 11 (for example, executing medical resource recommendation program, etc.), and call the data stored in the memory 11 to execute various functions of the electronic device and process data.
所述存储器11至少包括一种类型的可读存储介质,所述可读存储介质包括闪存、移动硬盘、多媒体卡、卡型存储器(例如:SD或DX存储器等)、磁性存储器、磁盘、光盘等,所述计算机可读存储介质可以是非易失性的,也可以是易失性的。所述存储器11在一些实施例中可以是电子设备的内部存储单元,例如该电子设备的移动硬盘。所述存储器11在另一些实施例中也可以是电子设备的外部存储设备,例如电子设备上配备的插接式移动硬盘、智能存储卡(Smart Media Card,SMC)、安全数字(Secure Digital,SD)卡、闪存卡(Flash Card)等。进一步地,所述存储器11还可以既包括电子设备的内部存储单元也包括外部存储设备。所述存储器11不仅可以用于存储安装于电子设备的应用软件及各类数据,例如医疗资源推荐程序的代码等,还可以用于暂时地存储已经输出或者将要输出的数据。The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. , the computer-readable storage medium may be non-volatile or volatile. The storage 11 may be an internal storage unit of the electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory 11 can also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk equipped on an electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD ) card, flash card (Flash Card), etc. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various data installed in the electronic device, such as the code of the medical resource recommendation program, etc., but also can be used to temporarily store the data that has been output or will be output.
所述通信总线12可以是外设部件互连标准(peripheral component interconnect,简称PCI)总线或扩展工业标准结构(extended industry standard architecture,简称EISA)总线等。该总线可以分为地址总线、数据总线、控制总线等。所述总线被设置为实现所述存储器11以及至少一个处理器10等之间的连接通信。The communication bus 12 may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (EISA for short) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10 and the like.
所述通信接口13用于上述电子设备与其他设备之间的通信,包括网络接口和用户接口。可选地,所述网络接口可以包括有线接口和/或无线接口(如WI-FI接口、蓝牙接口等),通常用于在该电子设备与其他电子设备之间建立通信连接。所述用户接口可以是显示器(Display)、输入单元(比如键盘(Keyboard)),可选地,用户接口还可以是标准的有线接口、无线接口。可选地,在一些实施例中,显示器可以是LED显示器、液晶显示器、 触控式液晶显示器以及OLED(Organic Light-Emitting Diode,有机发光二极管)触摸器等。其中,显示器也可以适当的称为显示屏或显示单元,用于显示在电子设备中处理的信息以及用于显示可视化的用户界面。The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display) or an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like. Wherein, the display may also be properly referred to as a display screen or a display unit, and is used for displaying information processed in the electronic device and for displaying a visualized user interface.
图5仅示出了具有部件的电子设备,本领域技术人员可以理解的是,图5示出的结构并不构成对所述电子设备1的限定,可以包括比图示更少或者更多的部件,或者组合某些部件,或者不同的部件布置。FIG. 5 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 5 does not constitute a limitation to the electronic device 1, and may include fewer or more components, or combinations of certain components, or different arrangements of components.
例如,尽管未示出,所述电子设备还可以包括给各个部件供电的电源(比如电池),优选地,电源可以通过电源管理装置与所述至少一个处理器10逻辑相连,从而通过电源管理装置实现充电管理、放电管理、以及功耗管理等功能。电源还可以包括一个或一个以上的直流或交流电源、再充电装置、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。所述电子设备还可以包括多种传感器、蓝牙模块、Wi-Fi模块等,在此不再赘述。For example, although not shown, the electronic device may also include a power supply (such as a battery) for supplying power to various components. Preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that Realize functions such as charge management, discharge management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components. The electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be repeated here.
应该了解,所述实施例仅为说明之用,在专利申请范围上并不受此结构的限制。It should be understood that the embodiments are only for illustration, and are not limited by the structure in the scope of the patent application.
所述电子设备1中的所述存储器11存储的医疗资源推荐程序是多个指令的组合,在所述处理器10中运行时,可以实现:The medical resource recommendation program stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:
获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;
利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;
基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;
获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
具体地,所述处理器10对上述指令的具体实现方法可参考附图对应实施例中相关步骤的描述,在此不赘述。Specifically, for the specific implementation method of the above instructions by the processor 10, reference may be made to the description of relevant steps in the corresponding embodiments in the drawings, and details are not repeated here.
进一步地,所述电子设备1集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读存储介质中。所述计算机可读存储介质可以是易失性的,也可以是非易失性的。例如,所述计算机可读介质可以包括:能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计 算机存储器、只读存储器(ROM,Read-Only Memory)。Further, if the integrated modules/units of the electronic device 1 are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory).
本申请还提供一种计算机可读存储介质,所述可读存储介质存储有计算机程序,所述计算机程序在被电子设备的处理器所执行时,可以实现:The present application also provides a computer-readable storage medium, the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can realize:
获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;
利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;
基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;
获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。在本申请所提供的几个实施例中,应该理解到,所揭露的设备,装置和方法,可以通过其它的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如,所述模块的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data. In the several embodiments provided in this application, it should be understood that the disclosed devices, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.
所述作为分离部件说明的模块可以是或者也可以不是物理上分开的,作为模块显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外,在本申请各个实施例中的各功能模块可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用硬件加软件功能模块的形式实现。In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software function modules.
对于本领域技术人员而言,显然本申请不限于上述示范性实施例的细节,而且在不背离本申请的精神或基本特征的情况下,能够以其他的具体形式实现本申请。It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.
因此,无论从哪一点来看,均应将实施例看作是示范性的,而且是非限制性的,本申请的范围由所附权利要求而不是上述说明限定,因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本申请内。不应将权利要求中的任何附关联图标记视为限制所涉及的权利要求。Therefore, the embodiments should be regarded as exemplary and not restrictive in all points of view, and the scope of the application is defined by the appended claims rather than the foregoing description, and it is intended that the scope of the present application be defined by the appended claims rather than by the foregoing description. All changes within the meaning and range of equivalents of the elements are embraced in this application. Any reference sign in a claim should not be construed as limiting the claim concerned.
本申请所指区块链是分布式数据存储、点对点传输、共识机制、加密算法等计算机技术的新型应用模式。区块链(Blockchain),本质上是一个去中心化的数据库,是一串使用密码学方法相关联产生的数据块,每一个数据块中包含了一批次网络交易的信息,用于验证 其信息的有效性(防伪)和生成下一个区块。区块链可以包括区块链底层平台、平台产品服务层以及应用服务层等。The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain (Blockchain), essentially a decentralized database, is a series of data blocks associated with each other using cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
本申请实施例可以基于人工智能技术对相关的数据进行获取和处理。其中,人工智能(Artificial Intelligence,AI)是利用数字计算机或者数字计算机控制的机器模拟、延伸和扩展人的智能,感知环境、获取知识并使用知识获得最佳结果的理论、方法、技术及应用系统。The embodiments of the present application may acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .
此外,显然“包括”一词不排除其他单元或步骤,单数不排除复数。系统权利要求中陈述的多个单元或装置也可以由一个单元或装置通过软件或者硬件来实现。第一、第二等词语用来表示名称,而并不表示任何特定的顺序。In addition, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or devices stated in the system claims may also be realized by one unit or device through software or hardware. The terms first, second, etc. are used to denote names and do not imply any particular order.
最后应说明的是,以上实施例仅用以说明本申请的技术方案而非限制,尽管参照较佳实施例对本申请进行了详细说明,本领域的普通技术人员应当理解,可以对本申请的技术方案进行修改或等同替换,而不脱离本申请技术方案的精神和范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application without limitation. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solutions of the present application.
Claims (20)
- 一种医疗资源推荐方法,其中,所述方法包括:A method for recommending medical resources, wherein the method includes:获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- 如权利要求1所述的医疗资源推荐方法,其中,所述从所述历史患者数据集中提取历史患者信息特征集,包括:The medical resource recommendation method according to claim 1, wherein said extracting a feature set of historical patient information from said historical patient data set comprises:将所述历史患者数据集统一转换为文本格式,得到历史患者文本数据集;Converting the historical patient data set into a text format uniformly to obtain the historical patient text data set;对所述历史患者文本数据集进行分词及词性标注,得到分词及词性标注的结果;Carrying out word segmentation and part-of-speech tagging on the historical patient text data set to obtain the results of word segmentation and part-of-speech tagging;根据所述词性标注的结果从所述分词中提取名词及名词短语,并根据所述名词及名词短语,统计得到历史患者信息特征频率,根据所述历史患者信息特征频率生成频繁模式树;Nouns and noun phrases are extracted from the word segmentation according to the results of the part-of-speech tagging, and according to the nouns and noun phrases, statistically obtain historical patient information characteristic frequencies, and generate frequent pattern trees according to the historical patient information characteristic frequencies;识别所述频繁模式树中的特征,得到候选历史患者信息特征集;identifying features in the frequent pattern tree to obtain a feature set of candidate historical patient information;计算所述候选历史患者信息特征集中各个特征的点互信息值,并从所述候选历史患者信息特征集中过滤掉点互信息值小于预设的标准阈值的历史患者信息特征,得到历史患者信息特征集。Calculate the point mutual information value of each feature in the candidate historical patient information feature set, and filter out the historical patient information features whose point mutual information value is less than a preset standard threshold from the candidate historical patient information feature set to obtain the historical patient information feature set.
- 如权利要求1所述的医疗资源推荐方法,其中,所述利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,包括:The method for recommending medical resources according to claim 1, wherein said using said historical patient visit data to extract the associated features between said historical patient information feature set and said historical medical resource feature set comprises:对所述历史患者信息特征集中每个历史患者信息特征及所述历史医疗资源特征集中每个历史医疗资源特征进行分词,从所述分词结果中提取关键词汇及关系词汇;Segmenting each historical patient information feature in the historical patient information feature set and each historical medical resource feature in the historical medical resource feature set, and extracting key words and relational words from the word segmentation results;删除与所述历史患者就诊数据无关的所述关键词汇及关系词汇,得到所述历史患者信息特征集与所述历史医疗资源特征集之间的关联特征。The key words and relational words irrelevant to the historical patient visit data are deleted to obtain the associated features between the historical patient information feature set and the historical medical resource feature set.
- 如权利要求1所述的医疗资源推荐方法,其中,所述基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型,包括:The medical resource recommendation method according to claim 1, wherein, based on the historical patient medical recommendation sample set, the pre-built medical resource recommendation model is trained using a contrastive divergence algorithm to obtain the trained medical resource recommendation models, including:将所述历史患者医疗推荐样本集划分为训练集和测试集;Dividing the historical patient medical recommendation sample set into a training set and a test set;根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型;According to the training set, using a contrastive divergence algorithm to adjust the parameters of the medical resource recommendation model, and iteratively training the medical resource recommendation model to obtain a trained medical resource recommendation model;利用所述测试集对所述经过训练的医疗资源推荐模型进行测试和调整,得到训练完成的医疗资源推荐模型。The test set is used to test and adjust the trained medical resource recommendation model to obtain a trained medical resource recommendation model.
- 如权利要求4所述的医疗资源推荐方法,其中,所述根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型,包括:The medical resource recommendation method according to claim 4, wherein, according to the training set, the parameters of the medical resource recommendation model are adjusted using a contrastive divergence algorithm, and the medical resource recommendation model is iteratively trained to obtain the Trained medical resource recommendation models, including:将所述训练集中所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征转化为特征向量,并对所述特征向量进行均值、方差、白化处理,并对所述处理后的特征向量按照从小到大的顺序均匀归一到0、1之间,得到归一化的样本集;Convert the historical patient information feature set, the historical medical resource feature set, and the associated features in the training set into feature vectors, and perform mean value, variance, and whitening processing on the feature vectors, and process the processed The eigenvectors of are uniformly normalized to between 0 and 1 in order from small to large to obtain a normalized sample set;初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值,及所述可视层神经元的偏置参数和隐藏层神经元的偏置参数;Initializing the weight value between the neurons of the visible layer and the neurons of the hidden layer in the medical resource recommendation model, and the bias parameters of the neurons of the visible layer and the bias parameters of the neurons of the hidden layer;利用所述归一化的样本集、所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数,对所述可视层神经元和所述隐藏层神经元进行循环迭代,并计算所述隐藏层神经元的激活概率;Using the normalized sample set, the weight value, the bias parameter of the neuron in the visible layer and the bias parameter of the neuron in the hidden layer, the neuron in the visible layer and the neuron in the hidden layer Layer neurons perform loop iterations, and calculate the activation probability of the hidden layer neurons;利用所述循环迭代后的隐藏层神经元,反向循环迭代所述可视层神经元,并计算所述可视层神经元的激活概率;Using the hidden layer neurons after the loop iteration, iterate the visible layer neurons in a reverse cycle, and calculate the activation probability of the visible layer neurons;根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值及所述偏置参数进行调整,利用调整后的所述权重值及所述偏置参数,重新计算所述可视层神经元的激活概率,根据所述可视层神经元的激活概率的最大值对应的医疗资源推荐模型的输出结果作为预测推荐医疗资源;According to the activation probability of the neuron in the hidden layer and the activation probability of the neuron in the visible layer, use a contrastive divergence algorithm to adjust the weight value and the bias parameter, and use the adjusted weight value and The bias parameter is to recalculate the activation probability of the visible layer neurons, and use the output result of the medical resource recommendation model corresponding to the maximum value of the activation probability of the visible layer neurons as the predicted recommended medical resources;利用损失函数计算所述预测推荐医疗资源与所述历史患者就诊数据之间的损失值,并根据所述损失值调整所述医疗资源推荐模型的参数,返回所述初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值的步骤,直到所述损失值小于预设的损失阈值,得到经过训练的医疗资源推荐模型。Using a loss function to calculate the loss value between the predicted and recommended medical resources and the historical patient visit data, and adjust the parameters of the medical resource recommendation model according to the loss value, and return to the initialization of the medical resource recommendation model The step of weighting the neurons in the visible layer and the neurons in the hidden layer until the loss value is less than a preset loss threshold, to obtain a trained medical resource recommendation model.
- 如权利要求5中所述的医疗资源推荐方法,其中,所述根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值及所述偏置参数进行调整,包括:The method for recommending medical resources as claimed in claim 5, wherein, according to the activation probability of neurons in the hidden layer and the activation probability of neurons in the visible layer, the weight value and The bias parameters to adjust include:利用所述归一化的样本集初始化所述可视层神经元、所述隐藏层神经元的状态向量、 所述可视层神经元和所述隐藏层神经元之间的权重值;Using the normalized sample set to initialize the visible layer neuron, the state vector of the hidden layer neuron, the weight value between the visible layer neuron and the hidden layer neuron;利用所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,执行K步吉布斯采样,得到t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量;Utilize the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer to perform K-step Gibbs sampling to obtain the state vector of the neurons in the visible layer and the neurons in the hidden layer corresponding to the t-1 moment The state vector of , the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to time t;利用所述t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量,对所述归一化的样本集进行循环计算得到对比误差值;Utilize the state vector of the visible layer neuron corresponding to the t-1 moment and the state vector of the hidden layer neuron, the state vector of the visible layer neuron corresponding to the t moment and the state vector of the hidden layer neuron, to the described The normalized sample set is cyclically calculated to obtain the comparison error value;利用所述对比误差值对所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数进行调整。The weight value, the bias parameter of the visible layer neuron and the bias parameter of the hidden layer neuron are adjusted by using the comparison error value.
- 如权利要求1至6中任意一项所述的医疗资源推荐方法,其中,所述获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据,包括:The method for recommending medical resources according to any one of claims 1 to 6, wherein said acquisition of patient information to be analyzed and available medical resources is performed using the trained medical resource recommendation model for said patient information to be analyzed and Available medical resources can be matched and analyzed to obtain recommended medical resource data, including:获取用户端上传的待分析患者信息及可用医疗资源;Obtain patient information to be analyzed and available medical resources uploaded by the client;从所述待分析患者信息中提取待分析患者信息特征及从所述可用医疗资源中提取可用医疗资源特征;extracting the characteristics of the patient information to be analyzed from the patient information to be analyzed and the characteristics of available medical resources from the available medical resources;利用所述训练完成的医疗资源推荐模型对所述待分析患者信息特征及所述可用医疗资源特征进行匹配分析,得到推荐医疗资源数据;Using the trained medical resource recommendation model to perform matching analysis on the characteristics of the patient information to be analyzed and the characteristics of the available medical resources to obtain recommended medical resource data;将所述推荐医疗资源数据反馈给所述用户端。Feedback the recommended medical resource data to the client.
- 一种医疗资源推荐装置,其中,所述装置包括:A device for recommending medical resources, wherein the device includes:特征提取模块,用于获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;The feature extraction module is used to obtain historical patient data sets and historical medical resource sets, extract historical patient information feature sets and historical patient visit data from the historical patient data sets, and extract historical medical resource feature sets from the historical medical resource sets ;样本构建模块,用于利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;The sample construction module is used to use the historical patient visit data to extract the associated features between the historical patient information feature set and the historical medical resource feature set, and use the historical patient information feature set and the historical medical resource feature set The feature set and the associated features are used to construct a medical recommendation sample set for historical patients;模型训练模块,用于基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;The model training module is used to train the pre-built medical resource recommendation model based on the historical patient medical recommendation sample set using the contrastive divergence algorithm to obtain the trained medical resource recommendation model;推荐模块,用于获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。The recommendation module is used to obtain the patient information to be analyzed and the available medical resources, and use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and the available medical resources to obtain recommended medical resource data.
- 一种电子设备,其中,所述电子设备包括:An electronic device, wherein the electronic device includes:至少一个处理器;以及,at least one processor; and,与所述至少一个处理器通信连接的存储器;其中,a memory communicatively coupled to the at least one processor; wherein,所述存储器存储有可被所述至少一个处理器执行的计算机程序,所述计算机程序被所述至少一个处理器执行,以使所述至少一个处理器能够执行如下所述的医疗资源推荐方法:The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the medical resource recommendation method as follows:获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- 如权利要求9所述的电子设备,其中,所述从所述历史患者数据集中提取历史患者信息特征集,包括:The electronic device according to claim 9, wherein said extracting a feature set of historical patient information from said historical patient data set comprises:将所述历史患者数据集统一转换为文本格式,得到历史患者文本数据集;Converting the historical patient data set into a text format uniformly to obtain the historical patient text data set;对所述历史患者文本数据集进行分词及词性标注,得到分词及词性标注的结果;Carrying out word segmentation and part-of-speech tagging on the historical patient text data set to obtain the results of word segmentation and part-of-speech tagging;根据所述词性标注的结果从所述分词中提取名词及名词短语,并根据所述名词及名词短语,统计得到历史患者信息特征频率,根据所述历史患者信息特征频率生成频繁模式树;Nouns and noun phrases are extracted from the word segmentation according to the results of the part-of-speech tagging, and according to the nouns and noun phrases, statistically obtain historical patient information characteristic frequencies, and generate frequent pattern trees according to the historical patient information characteristic frequencies;识别所述频繁模式树中的特征,得到候选历史患者信息特征集;identifying features in the frequent pattern tree to obtain a feature set of candidate historical patient information;计算所述候选历史患者信息特征集中各个特征的点互信息值,并从所述候选历史患者信息特征集中过滤掉点互信息值小于预设的标准阈值的历史患者信息特征,得到历史患者信息特征集。Calculate the point mutual information value of each feature in the candidate historical patient information feature set, and filter out the historical patient information features whose point mutual information value is less than a preset standard threshold from the candidate historical patient information feature set to obtain the historical patient information feature set.
- 如权利要求9所述的电子设备,其中,所述利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,包括:The electronic device according to claim 9, wherein said extracting the associated features between the historical patient information feature set and the historical medical resource feature set by using the historical patient visit data comprises:对所述历史患者信息特征集中每个历史患者信息特征及所述历史医疗资源特征集中每个历史医疗资源特征进行分词,从所述分词结果中提取关键词汇及关系词汇;Segmenting each historical patient information feature in the historical patient information feature set and each historical medical resource feature in the historical medical resource feature set, and extracting key words and relational words from the word segmentation results;删除与所述历史患者就诊数据无关的所述关键词汇及关系词汇,得到所述历史患者信息特征集与所述历史医疗资源特征集之间的关联特征。The key words and relational words irrelevant to the historical patient visit data are deleted to obtain the associated features between the historical patient information feature set and the historical medical resource feature set.
- 如权利要求9所述的电子设备,其中,所述基于所述历史患者医疗推荐样本集, 利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型,包括:The electronic device according to claim 9, wherein, based on the historical patient medical recommendation sample set, the pre-built medical resource recommendation model is trained using a contrastive divergence algorithm to obtain a trained medical resource recommendation model, include:将所述历史患者医疗推荐样本集划分为训练集和测试集;Dividing the historical patient medical recommendation sample set into a training set and a test set;根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型;According to the training set, using a contrastive divergence algorithm to adjust the parameters of the medical resource recommendation model, and iteratively training the medical resource recommendation model to obtain a trained medical resource recommendation model;利用所述测试集对所述经过训练的医疗资源推荐模型进行测试和调整,得到训练完成的医疗资源推荐模型。The test set is used to test and adjust the trained medical resource recommendation model to obtain a trained medical resource recommendation model.
- 如权利要求12所述的电子设备,其中,所述根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型,包括:The electronic device according to claim 12, wherein, according to the training set, the parameters of the medical resource recommendation model are adjusted using a contrastive divergence algorithm, and the medical resource recommendation model is iteratively trained to obtain a trained Medical resource recommendation model, including:将所述训练集中所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征转化为特征向量,并对所述特征向量进行均值、方差、白化处理,并对所述处理后的特征向量按照从小到大的顺序均匀归一到0、1之间,得到归一化的样本集;Convert the historical patient information feature set, the historical medical resource feature set, and the associated features in the training set into feature vectors, and perform mean value, variance, and whitening processing on the feature vectors, and process the processed The eigenvectors of are uniformly normalized to between 0 and 1 in order from small to large to obtain a normalized sample set;初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值,及所述可视层神经元的偏置参数和隐藏层神经元的偏置参数;Initializing the weight value between the neurons of the visible layer and the neurons of the hidden layer in the medical resource recommendation model, and the bias parameters of the neurons of the visible layer and the bias parameters of the neurons of the hidden layer;利用所述归一化的样本集、所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数,对所述可视层神经元和所述隐藏层神经元进行循环迭代,并计算所述隐藏层神经元的激活概率;Using the normalized sample set, the weight value, the bias parameter of the neuron in the visible layer and the bias parameter of the neuron in the hidden layer, the neuron in the visible layer and the neuron in the hidden layer Layer neurons perform loop iterations, and calculate the activation probability of the hidden layer neurons;利用所述循环迭代后的隐藏层神经元,反向循环迭代所述可视层神经元,并计算所述可视层神经元的激活概率;Using the hidden layer neurons after the loop iteration, iterate the visible layer neurons in a reverse cycle, and calculate the activation probability of the visible layer neurons;根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值及所述偏置参数进行调整,利用调整后的所述权重值及所述偏置参数,重新计算所述可视层神经元的激活概率,根据所述可视层神经元的激活概率的最大值对应的医疗资源推荐模型的输出结果作为预测推荐医疗资源;According to the activation probability of the neuron in the hidden layer and the activation probability of the neuron in the visible layer, use a contrastive divergence algorithm to adjust the weight value and the bias parameter, and use the adjusted weight value and The bias parameter is to recalculate the activation probability of the visible layer neurons, and use the output result of the medical resource recommendation model corresponding to the maximum value of the activation probability of the visible layer neurons as the predicted recommended medical resources;利用损失函数计算所述预测推荐医疗资源与所述历史患者就诊数据之间的损失值,并根据所述损失值调整所述医疗资源推荐模型的参数,返回所述初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值的步骤,直到所述损失值小于预设的损失阈值,得到经过训练的医疗资源推荐模型。Using a loss function to calculate the loss value between the predicted and recommended medical resources and the historical patient visit data, and adjust the parameters of the medical resource recommendation model according to the loss value, and return to the initialization of the medical resource recommendation model The step of weighting the neurons in the visible layer and the neurons in the hidden layer until the loss value is less than a preset loss threshold, to obtain a trained medical resource recommendation model.
- 如权利要求13所述的电子设备,其中,所述根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值及所述偏置参数进行调 整,包括:The electronic device according to claim 13, wherein, according to the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer, the contrastive divergence algorithm is used to compare the weight value and the bias Adjust the configuration parameters, including:利用所述归一化的样本集初始化所述可视层神经元、所述隐藏层神经元的状态向量、所述可视层神经元和所述隐藏层神经元之间的权重值;Using the normalized sample set to initialize the visible layer neuron, the state vector of the hidden layer neuron, the weight value between the visible layer neuron and the hidden layer neuron;利用所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,执行K步吉布斯采样,得到t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量;Utilize the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer to perform K-step Gibbs sampling to obtain the state vector of the neurons in the visible layer and the neurons in the hidden layer corresponding to the t-1 moment The state vector of , the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to time t;利用所述t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量,对所述归一化的样本集进行循环计算得到对比误差值;Utilize the state vector of the visible layer neuron corresponding to the t-1 moment and the state vector of the hidden layer neuron, the state vector of the visible layer neuron corresponding to the t moment and the state vector of the hidden layer neuron, to the described The normalized sample set is cyclically calculated to obtain the comparison error value;利用所述对比误差值对所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数进行调整。The weight value, the bias parameter of the visible layer neuron and the bias parameter of the hidden layer neuron are adjusted by using the comparison error value.
- 一种计算机可读存储介质,存储有计算机程序,其中,所述计算机程序被处理器执行时实现如下所述的医疗资源推荐方法:A computer-readable storage medium, storing a computer program, wherein, when the computer program is executed by a processor, the following method for recommending medical resources is implemented:获取历史患者数据集及历史医疗资源集,从所述历史患者数据集中提取历史患者信息特征集以及提取历史患者就诊数据,从所述历史医疗资源集中提取历史医疗资源特征集;Obtaining a historical patient data set and a historical medical resource set, extracting a historical patient information feature set and historical patient visit data from the historical patient data set, and extracting a historical medical resource feature set from the historical medical resource set;利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,利用所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征,构建历史患者医疗推荐样本集;Using the historical patient visit data, extract the associated features between the historical patient information feature set and the historical medical resource feature set, use the historical patient information feature set, the historical medical resource feature set and the association features, constructing a medical recommendation sample set for historical patients;基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型;Based on the historical patient medical recommendation sample set, using the contrastive divergence algorithm to train the pre-built medical resource recommendation model to obtain the trained medical resource recommendation model;获取待分析患者信息及可用医疗资源,利用所述训练完成的医疗资源推荐模型对所述待分析患者信息及可用医疗资源进行匹配分析,得到推荐医疗资源数据。Obtain patient information to be analyzed and available medical resources, use the trained medical resource recommendation model to perform matching analysis on the patient information to be analyzed and available medical resources, and obtain recommended medical resource data.
- 如权利要求15所述的计算机可读存储介质,其中,所述从所述历史患者数据集中提取历史患者信息特征集,包括:The computer-readable storage medium of claim 15, wherein said extracting a feature set of historical patient information from said historical patient data set comprises:将所述历史患者数据集统一转换为文本格式,得到历史患者文本数据集;Converting the historical patient data set into a text format uniformly to obtain the historical patient text data set;对所述历史患者文本数据集进行分词及词性标注,得到分词及词性标注的结果;Carrying out word segmentation and part-of-speech tagging on the historical patient text data set to obtain the results of word segmentation and part-of-speech tagging;根据所述词性标注的结果从所述分词中提取名词及名词短语,并根据所述名词及名词短语,统计得到历史患者信息特征频率,根据所述历史患者信息特征频率生成频繁模式树;Nouns and noun phrases are extracted from the word segmentation according to the results of the part-of-speech tagging, and according to the nouns and noun phrases, statistically obtain historical patient information characteristic frequencies, and generate frequent pattern trees according to the historical patient information characteristic frequencies;识别所述频繁模式树中的特征,得到候选历史患者信息特征集;identifying features in the frequent pattern tree to obtain a feature set of candidate historical patient information;计算所述候选历史患者信息特征集中各个特征的点互信息值,并从所述候选历史患者信息特征集中过滤掉点互信息值小于预设的标准阈值的历史患者信息特征,得到历史患者 信息特征集。Calculate the point mutual information value of each feature in the candidate historical patient information feature set, and filter out the historical patient information features whose point mutual information value is less than a preset standard threshold from the candidate historical patient information feature set to obtain the historical patient information feature set.
- 如权利要求15所述的计算机可读存储介质,其中,所述利用所述历史患者就诊数据,提取所述历史患者信息特征集及所述历史医疗资源特征集之间的关联特征,包括:The computer-readable storage medium according to claim 15, wherein said extracting the associated features between the historical patient information feature set and the historical medical resource feature set by using the historical patient visit data comprises:对所述历史患者信息特征集中每个历史患者信息特征及所述历史医疗资源特征集中每个历史医疗资源特征进行分词,从所述分词结果中提取关键词汇及关系词汇;Segmenting each historical patient information feature in the historical patient information feature set and each historical medical resource feature in the historical medical resource feature set, and extracting key words and relational words from the word segmentation results;删除与所述历史患者就诊数据无关的所述关键词汇及关系词汇,得到所述历史患者信息特征集与所述历史医疗资源特征集之间的关联特征。The key words and relational words irrelevant to the historical patient visit data are deleted to obtain the associated features between the historical patient information feature set and the historical medical resource feature set.
- 如权利要求15所述的计算机可读存储介质,其中,所述基于所述历史患者医疗推荐样本集,利用对比散度算法,对预构建的医疗资源推荐模型进行训练,得到训练完成的医疗资源推荐模型,包括:The computer-readable storage medium according to claim 15, wherein, based on the historical patient medical recommendation sample set, a contrastive divergence algorithm is used to train a pre-built medical resource recommendation model to obtain a trained medical resource Recommended models, including:将所述历史患者医疗推荐样本集划分为训练集和测试集;Dividing the historical patient medical recommendation sample set into a training set and a test set;根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型;According to the training set, using a contrastive divergence algorithm to adjust the parameters of the medical resource recommendation model, and iteratively training the medical resource recommendation model to obtain a trained medical resource recommendation model;利用所述测试集对所述经过训练的医疗资源推荐模型进行测试和调整,得到训练完成的医疗资源推荐模型。The test set is used to test and adjust the trained medical resource recommendation model to obtain a trained medical resource recommendation model.
- 如权利要求18所述的计算机可读存储介质,其中,所述根据所述训练集,利用对比散度算法调整所述医疗资源推荐模型的参数,对所述医疗资源推荐模型进行迭代训练,得到经过训练的医疗资源推荐模型,包括:The computer-readable storage medium according to claim 18, wherein, according to the training set, the parameters of the medical resource recommendation model are adjusted using a contrastive divergence algorithm, and the medical resource recommendation model is iteratively trained to obtain Trained medical resource recommendation models, including:将所述训练集中所述历史患者信息特征集、所述历史医疗资源特征集及所述关联特征转化为特征向量,并对所述特征向量进行均值、方差、白化处理,并对所述处理后的特征向量按照从小到大的顺序均匀归一到0、1之间,得到归一化的样本集;Convert the historical patient information feature set, the historical medical resource feature set, and the associated features in the training set into feature vectors, and perform mean value, variance, and whitening processing on the feature vectors, and process the processed The eigenvectors of are uniformly normalized to between 0 and 1 in order from small to large to obtain a normalized sample set;初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值,及所述可视层神经元的偏置参数和隐藏层神经元的偏置参数;Initializing the weight value between the neurons of the visible layer and the neurons of the hidden layer in the medical resource recommendation model, and the bias parameters of the neurons of the visible layer and the bias parameters of the neurons of the hidden layer;利用所述归一化的样本集、所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数,对所述可视层神经元和所述隐藏层神经元进行循环迭代,并计算所述隐藏层神经元的激活概率;Using the normalized sample set, the weight value, the bias parameter of the neuron in the visible layer and the bias parameter of the neuron in the hidden layer, the neuron in the visible layer and the neuron in the hidden layer Layer neurons perform loop iterations, and calculate the activation probability of the hidden layer neurons;利用所述循环迭代后的隐藏层神经元,反向循环迭代所述可视层神经元,并计算所述可视层神经元的激活概率;Using the hidden layer neurons after the loop iteration, iterate the visible layer neurons in a reverse cycle, and calculate the activation probability of the visible layer neurons;根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值及所述偏置参数进行调整,利用调整后的所述权重值及所述偏置参数,重 新计算所述可视层神经元的激活概率,根据所述可视层神经元的激活概率的最大值对应的医疗资源推荐模型的输出结果作为预测推荐医疗资源;According to the activation probability of the neuron in the hidden layer and the activation probability of the neuron in the visible layer, use a contrastive divergence algorithm to adjust the weight value and the bias parameter, and use the adjusted weight value and The bias parameter is to recalculate the activation probability of the visible layer neurons, and use the output result of the medical resource recommendation model corresponding to the maximum value of the activation probability of the visible layer neurons as the predicted recommended medical resources;利用损失函数计算所述预测推荐医疗资源与所述历史患者就诊数据之间的损失值,并根据所述损失值调整所述医疗资源推荐模型的参数,返回所述初始化所述医疗资源推荐模型中可视层神经元和隐藏层神经元之间的权重值的步骤,直到所述损失值小于预设的损失阈值,得到经过训练的医疗资源推荐模型。Using a loss function to calculate the loss value between the predicted and recommended medical resources and the historical patient visit data, and adjust the parameters of the medical resource recommendation model according to the loss value, and return to the initialization of the medical resource recommendation model The step of weighting the neurons in the visible layer and the neurons in the hidden layer until the loss value is less than a preset loss threshold, to obtain a trained medical resource recommendation model.
- 如权利要求19所述的计算机可读存储介质,其中,所述根据所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,利用对比散度算法对所述权重值及所述偏置参数进行调整,包括:The computer-readable storage medium according to claim 19, wherein, according to the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer, the contrastive divergence algorithm is used to compare the weight values and The bias parameters to adjust include:利用所述归一化的样本集初始化所述可视层神经元、所述隐藏层神经元的状态向量、所述可视层神经元和所述隐藏层神经元之间的权重值;Using the normalized sample set to initialize the visible layer neuron, the state vector of the hidden layer neuron, the weight value between the visible layer neuron and the hidden layer neuron;利用所述隐藏层神经元的激活概率及所述可视层神经元的激活概率,执行K步吉布斯采样,得到t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量;Utilize the activation probability of the neurons in the hidden layer and the activation probability of the neurons in the visible layer to perform K-step Gibbs sampling to obtain the state vector of the neurons in the visible layer and the neurons in the hidden layer corresponding to the t-1 moment The state vector of , the state vector of the visible layer neuron and the state vector of the hidden layer neuron corresponding to time t;利用所述t-1时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量、t时刻对应的可视层神经元的状态向量及隐藏层神经元的状态向量,对所述归一化的样本集进行循环计算得到对比误差值;Utilize the state vector of the visible layer neuron corresponding to the t-1 moment and the state vector of the hidden layer neuron, the state vector of the visible layer neuron corresponding to the t moment and the state vector of the hidden layer neuron, to the described The normalized sample set is cyclically calculated to obtain the comparison error value;利用所述对比误差值对所述权重值、所述可视层神经元的偏置参数及所述隐藏层神经元的偏置参数进行调整。The weight value, the bias parameter of the visible layer neuron and the bias parameter of the hidden layer neuron are adjusted by using the comparison error value.
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