CN116525053A - Patient report generation method, device, electronic equipment and medium - Google Patents
Patient report generation method, device, electronic equipment and medium Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence and digital medical treatment, and discloses a patient report generation method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library; training a preset disease prediction model by using the marked patient data to obtain a trained disease prediction model; when receiving real-time patient data of a target patient, predicting the disease type of the real-time patient data by using the trained disease prediction model, and generating a patient report of the target patient according to the disease type and the real-time patient data. The invention can improve the accuracy of the patient report.
Description
Technical Field
The present invention relates to the field of artificial intelligence and digital medical, and more particularly, to a method, apparatus, electronic device, and readable storage medium for generating a patient report.
Background
The current social offline medical treatment is tension and takes longer time, so that the online patient inquiry is an important means for knowing the disease condition of the patient, but the current common patient inquiry method is mainly based on information fragments provided by an Internet search platform by the patient, so that the problems of low accuracy, low efficiency and the like of patient report are caused.
Disclosure of Invention
The invention provides a patient report generation method, a device, an electronic device and a readable storage medium, and aims to improve the accuracy of a patient report.
To achieve the above object, the present invention provides a patient report generating method, including:
the method comprises the steps of utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library;
Coding the marked patient data by using a coding layer in a preset disease prediction model to obtain a patient coding matrix;
calculating the attention weight of the patient coding matrix by using an attention mechanism layer in the disease prediction model to obtain a patient weight matrix;
predicting the disease type of the historical patient by using a full connection layer in the disease prediction model according to the disease weight matrix;
calculating a loss value of the disease type by using a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition to obtain a disease prediction model after training is completed;
when receiving real-time patient data of a target patient, predicting the disease type of the real-time patient data by using the trained disease prediction model, and generating a patient report of the target patient according to the disease type and the real-time patient data.
Optionally, the predicting the disease type of the historical patient by using the full connection layer in the disease prediction model according to the disease weight matrix includes:
performing linear transformation on the patient weight matrix by using the first preset parameter, the second preset parameter and the third preset parameter of the full-connection layer in the disease prediction model to obtain a query vector, a key vector and a numerical vector;
Multiplying the query vector by the transposed vector of the key vector to obtain a similarity matrix;
carrying out normalization calculation on the similarity matrix to obtain a normalization matrix;
calculating the normalized matrix by using a preset activation function to obtain an activation matrix;
and performing point multiplication on the activation matrix and the numerical vector to obtain the probability of the disease type corresponding to the patient weight matrix, and determining the disease type of the historical patient according to the probability.
Optionally, the calculating the attention weight of the patient code matrix by using the attention mechanism layer in the disease prediction model to obtain a patient weight matrix includes:
calculating the patient coding matrix by using a weight parameter preset in an attention mechanism layer in the disease prediction model to obtain the score of each individual vector in the patient coding matrix;
and carrying out weighted summation on the score of each individual vector and the corresponding patient coding matrix part to obtain a patient weight matrix.
Optionally, the encoding the marked patient data by using an encoding layer in a preset disease prediction model to obtain a patient encoding matrix, including:
The marked patient data are coded word by utilizing a preset disease prediction model, and a word vector sequence is obtained;
extracting feature vectors of the marked patient data to obtain a word vector sequence;
expanding the word vector sequence according to the word number of the word vector sequence to obtain an aligned word vector sequence aligned with the word vector sequence;
performing cross multiplication on the aligned word vector sequence and a preset transformation matrix to obtain a target word vector sequence with the same dimension as the word vector sequence;
adding the target word vector sequence and the corresponding word vector sequence to obtain a word vector sequence;
performing position index coding on each character of the marked patient data to obtain a patient vector position code;
and adding the word vector sequences and the position codes corresponding to the patient vectors respectively to obtain patient spliced vectors, and coding the patient spliced vectors by using a coding layer in a preset disease prediction model to obtain a patient coding matrix.
Optionally, the classifying and marking the historical patient data to obtain marked patient data includes:
screening disease information from the historical patient data, and extracting key information in the disease information;
Matching the key information with the tags in the pre-constructed tag pool, and taking the successfully matched tag as a target tag of the historical patient data;
binding the target tag with the corresponding historical patient data to obtain marked patient data.
Optionally, after generating the patient report of the target patient according to the disease type, the method further includes:
updating the real-time patient data and the patient report to the pathogen sample library;
selecting a proper diagnosis and treatment scheme for treatment according to the patient report to obtain a treatment result;
predicting a treatment result by using the disease prediction model to obtain a re-diagnosis disease type, and generating a re-diagnosis report of the target patient according to the re-diagnosis disease type and the real-time patient data;
judging whether the target patient is recovered according to the re-diagnosis report;
determining that the target patient is rehabilitated when the review report shows normal;
when the review report shows abnormality, it is determined that the target patient is not recovered.
Optionally, the crawling, by using a preset web crawler, the historical patient data of the historical patient includes:
acquiring the URL of a preset initial webpage by using a preset Python programming language;
Screening target URLs related to historical patients from the preset initial webpage by using a webpage analysis algorithm, and storing the target URLs into a queue to be grabbed;
grabbing target URL from the queue to be grabbed according to a preset searching strategy until the target URL meets a preset limit, and stopping grabbing the target URL;
and carrying out information analysis on the webpage corresponding to the target URL, and filtering analysis results to obtain the historical patient data of the historical patient.
In order to solve the above problems, the present invention also provides a patient report generating apparatus, the apparatus comprising:
the pathogen sample library construction module is used for utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library;
the model training module is used for coding the marked patient data by utilizing a coding layer in a preset disease prediction model to obtain a patient coding matrix, calculating the attention weight of the patient coding matrix by utilizing an attention mechanism layer in the disease prediction model to obtain a patient weight matrix, predicting the disease types of the historical patient by utilizing a full connection layer in the disease prediction model according to the patient weight matrix, calculating the loss value of the disease types by utilizing a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition to obtain a trained disease prediction model;
And the patient report generating module is used for predicting the disease type of the real-time patient data by using the trained disease prediction model when the real-time patient data of the target patient are received, and generating a patient report of the target patient according to the disease type and the real-time patient data.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; and
And a processor executing the computer program stored in the memory to implement the patient report generating method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned patient report generating method.
According to the embodiment of the invention, the historical patient data of a historical patient is crawled by a preset web crawler, the historical patient data is classified and marked to obtain the marked patient data, the marked patient data is stored in the preset distributed storage database to obtain the pathogenic sample library, the coverage area of the pathogenic sample library is ensured to be wide, the fragmentation of disease information is reduced, the accuracy of patient inquiry is improved, secondly, the preset disease prediction model is trained according to the marked patient data in the pathogenic sample library to obtain a trained disease prediction model, the accuracy and coverage area of the trained disease prediction model are ensured from the dimension of training data, finally, the disease type of the real-time patient data is predicted by the trained disease prediction model, and the patient report of the target patient is generated according to the disease type, so that the patient can know the disease condition of the patient anytime and anywhere, and the time of selecting the disease condition according to the patient is ensured, and the pressure of medical resource shortage is relieved. Therefore, the patient report generating method, the device, the equipment and the storage medium provided by the invention can improve the accuracy of the patient report.
Drawings
FIG. 1 is a flowchart of a patient report generating method according to an embodiment of the present invention;
FIGS. 2-3 are flowcharts illustrating one of the steps of a patient report generating method according to one embodiment of the present invention;
FIG. 4 is a block diagram of a patient report generating apparatus according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating an internal structure of an electronic device for implementing a patient report generating method according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a patient report generation method. The execution subject of the patient report generating method includes, but is not limited to, at least one of a server, a terminal, etc. capable of being configured to execute the method provided by the embodiments of the present application. In other words, the patient report generating method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server may include an independent server, and may also include a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, which is a schematic flow chart of a patient report generating method according to an embodiment of the present invention, in an embodiment of the present invention, the patient report generating method includes:
s1, crawling historical patient data of a historical patient by using a preset web crawler, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library.
In the embodiment of the invention, the preset web crawler may be a web data crawling script written by using a programming language. The historic patient may be a patient who has been previously subjected to diagnostic treatment at a hospital. The historical patient data comprises patient condition summary, offline hospital consultation data, special examination reports, health examination reports, doctor online consultation data and the like of the historical patient. The preset distributed storage database may be a database composed of a plurality of independent entities and interconnected with each other through a network.
According to the embodiment of the invention, the preset web crawler is utilized to crawl the historical patient data of the historical patient, so that the comprehensiveness of the historical patient data information is ensured, the target patient is analyzed more accurately, the accuracy of patient consultation is improved, in addition, the degree of manual participation is reduced by utilizing the web crawler to crawl the historical patient data of the historical patient, and the intelligent degree of patient consultation is improved.
Further, as an optional embodiment of the present invention, the crawling the historical patient data of the historical patient by using the preset web crawler includes:
acquiring the URL of a preset initial webpage by using a preset Python programming language;
screening target URLs related to historical patients from the preset initial webpage by using a webpage analysis algorithm, and storing the target URLs into a queue to be grabbed;
grabbing target URL from the queue to be grabbed according to a preset searching strategy until the target URL meets a preset limit, and stopping grabbing the target URL;
and carrying out information analysis on the webpage corresponding to the target URL, and filtering analysis results to obtain the historical patient data of the historical patient.
In the embodiment of the present invention, the preset initial webpage may be a first layer webpage that is set by a programmer and used for crawling information by a web crawler, for example, an open webpage of a certain hospital. The URL may be a string representation of available resources on the internet. The web page analysis algorithm can be PageRank or HITS algorithm. The preset searching strategy can be used for grabbing according to the amount of information needed by the target URL, or grabbing according to the amount of browsing of the target URL.
In an alternative embodiment of the invention, a crawler instruction is written through a Python programming language, and the historical patient data of the historical patient is crawled from the initial webpage according to the crawler instruction, so that the time for manually searching information is shortened, the collection speed of the historical patient data is increased, and the collection efficiency of the historical patient data is improved.
Further, the embodiment of the invention obtains the marked patient data by classifying and marking the historical patient data, finishes classifying the historical patient data, shortens the time for searching the corresponding case when the patient is asked, and improves the efficiency of the patient is asked.
In detail, referring to fig. 2, the classifying and marking the historical patient data to obtain marked patient data includes:
s11, screening disease information from the historical patient data, and extracting key information in the disease information;
s12, matching the key information with the tags in the pre-constructed tag pool, and taking the successfully matched tag as a target tag of the historical patient data;
and S13, binding the target label with the corresponding historical patient data to obtain marked patient data.
In the embodiment of the present invention, the disease information includes a disease name and a disease detection report of the disease suffered by the historical patient. The tag pool contains class tags for all diseases, for example: dermatological, surgical and medical diseases.
In an alternative embodiment of the present invention, the key information in the disease information may be extracted by a supervised or unsupervised key information extraction method. The method for extracting the supervised key information firstly extracts the candidate words of the disease information according to a candidate word template given by a professional, and then judges the extracted candidate words, so as to determine the key information in the disease information. The method for extracting the unsupervised key information comprises the steps of firstly extracting candidate words in the disease information, scoring each candidate word, and finally outputting N candidate words with highest scores as key information.
Further, in an alternative embodiment of the invention, the marked patient data is stored in a preset distributed storage database in a read-write data form to obtain a pathogen sample library, so that the follow-up searching of related diseases is facilitated, the patient inquiry confirmation speed is increased, and the patient inquiry efficiency is improved.
S2, coding the marked patient data by using a coding layer in a preset disease prediction model to obtain a patient coding matrix.
In the embodiment of the present invention, the preset disease prediction model is a deep learning network including a coding layer, an attention mechanism layer and a full connection layer.
The embodiment of the invention utilizes the coding layer in the preset disease prediction model to code the marked patient data to obtain the patient coding matrix, and provides the input matrix for the attention mechanism layer in the disease prediction model so as to determine the disease type of the historical patient.
Further, as an optional embodiment of the present invention, the encoding the marked patient data by using an encoding layer in a preset disease prediction model to obtain a patient encoding matrix includes:
the marked patient data are coded word by utilizing a preset disease prediction model, and a word vector sequence is obtained;
extracting feature vectors of the marked patient data to obtain a word vector sequence;
expanding the word vector sequence according to the word number of the word vector sequence to obtain an aligned word vector sequence aligned with the word vector sequence;
performing cross multiplication on the aligned word vector sequence and a preset transformation matrix to obtain a target word vector sequence with the same dimension as the word vector sequence;
Adding the target word vector sequence and the corresponding word vector sequence to obtain a word vector sequence;
performing position index coding on each character of the marked patient data to obtain a patient vector position code;
and adding the word vector sequences and the position codes corresponding to the patient vectors respectively to obtain patient spliced vectors, and coding the patient spliced vectors by using a coding layer in a preset disease prediction model to obtain a patient coding matrix.
In the embodiment of the present invention, the preset transformation matrix may be a matrix set according to the dimension of the word vector sequence.
In the alternative embodiment of the invention, the condition of word recognition errors is easy to occur because the word vector is simply used, so that the word vector sequence is obtained by carrying out word mixing dimension reduction on the marked patient data, thereby being beneficial to improving the accuracy of the disease prediction model.
And S3, calculating the attention weight of the patient coding matrix by using an attention mechanism layer in the disease prediction model to obtain a patient weight matrix.
In the embodiment of the present invention, the role of the attention mechanism layer may be the following two aspects: deciding which part of the input needs to be focused on; the limited information processing resources are allocated to the important parts.
The embodiment of the invention calculates the attention weight of the patient coding matrix by using the attention mechanism layer in the disease prediction model to obtain the patient weight matrix, so that the disease prediction model can determine which part of the patient coding matrix is transferred with more attention, and the disease type prediction of the historical patient is more accurate.
Further, as an optional embodiment of the present invention, referring to fig. 3, the calculating, by using an attention mechanism layer in the disease prediction model, the attention weight of the patient code matrix to obtain a patient weight matrix includes:
s31, calculating the patient coding matrix by using a weight parameter preset in an attention mechanism layer in the disease prediction model to obtain the score of each individual vector in the patient coding matrix;
and S32, carrying out weighted summation on the score of each individual vector and the corresponding patient coding matrix part to obtain a patient weight matrix.
In the embodiment of the present invention, the preset weight parameter may be a parameter matrix adjusted according to service requirements.
In an alternative embodiment of the present invention, when the decoding layer predicts the disease type of the i-th historical patient, the i-th patient weight matrix in the decoding layer and the patient weight matrix of each encoding layer are calculated to obtain a set of scores, each score represents the attention of the disease prediction model in predicting the patient code matrix at the current position, the higher the score is, the greater the attention of the disease prediction model is, and then the normalization function is used to change the score vector into a probability distribution, and the result is used as weight to be weighted and summed with the corresponding patient weight matrix, so as to obtain the patient weight matrix.
S4, predicting the disease types of the historical patients by using the full-connection layer in the disease prediction model according to the disease weight matrix.
In the embodiment of the present invention, the fully connected layer may be a module in which each node in the disease prediction model is connected to all nodes in the previous layer, so as to integrate the features extracted from the previous layer.
According to the disease weight matrix, the disease type of the historical patient is predicted by using the full-connection layer in the disease prediction model, so that the accuracy of the disease prediction model is improved, the accuracy of the disease type of the historical patient predicted by the disease prediction model is improved, and the accuracy of patient consultation is further ensured.
Further, as an optional embodiment of the present invention, the predicting, according to the patient weight matrix, the disease type of the historical patient using the full connection layer in the disease prediction model includes:
performing linear transformation on the patient weight matrix by using the first preset parameter, the second preset parameter and the third preset parameter of the full-connection layer in the disease prediction model to obtain a query vector, a key vector and a numerical vector;
Multiplying the query vector by the transposed vector of the key vector to obtain a similarity matrix;
carrying out normalization calculation on the similarity matrix to obtain a normalization matrix;
calculating the normalized matrix by using a preset activation function to obtain an activation matrix;
and performing point multiplication on the activation matrix and the numerical vector to obtain the probability of the disease type corresponding to the patient weight matrix, and determining the disease type of the historical patient according to the probability.
In the embodiment of the present invention, the first preset parameter, the second preset parameter and the third preset parameter may be parameter matrices set by a model tester. The preset activation function may be a softmax activation function.
In an alternative embodiment of the invention, the score normalization calculation is performed on the similarity matrix, so that the matrix gradient is more stable and reliable, and the calculation efficiency of the probability of the disease category corresponding to the patient weight matrix is improved.
S5, calculating a loss value of the disease type by using a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition, so as to obtain the disease prediction model after training.
In the embodiment of the invention, the disease type includes severity of disease, disease type, current disease condition, and the like.
In an embodiment of the present invention, the parameter may be a parameter that affects a coding process of the disease prediction model. The preset condition may be a maximum loss value allowed by the disease prediction model.
In an alternative embodiment of the present invention, when the loss value does not meet the preset condition, it is determined that the training effect of the disease prediction model does not reach the preset target, and the accuracy of disease type prediction of the historical patient is greatly affected.
And S6, when the real-time patient data of the target patient are received, predicting the disease type of the real-time patient data by using the trained disease prediction model, and generating a patient report of the target patient according to the disease type and the real-time patient data.
In an embodiment of the present invention, the target patient may be a patient who is currently ill. The real-time patient data may be current physical condition, disease level, etc. data of the target patient.
In an embodiment of the present invention, the patient report includes: disease analysis, disease condition, next diagnosis course, etc.
In an alternative embodiment of the invention, in order to enable the target patient to conduct the patient inquiry process at any time, therefore, when the real-time patient data of the target patient is received at any time, the disease type of the real-time patient data can be predicted by using the disease prediction model which is completed by training, and the patient report of the target patient is generated according to the disease type, so that the patient can judge the current condition in real time and correctly in the whole process inquiry before and after the diagnosis based on the internet data, thereby selecting the optimal treatment time and guaranteeing the life safety of the patient.
In addition, in an optional embodiment of the present invention, after generating the patient report of the target patient according to the disease type, the method further includes:
updating the real-time patient data and the patient report to the pathogen sample library;
Selecting a proper diagnosis and treatment scheme for treatment according to the patient report to obtain a treatment result;
predicting a treatment result by using the disease prediction model to obtain a re-diagnosis disease type, and generating a re-diagnosis report of the target patient according to the re-diagnosis disease type and the real-time patient data;
judging whether the target patient is recovered according to the re-diagnosis report;
determining that the target patient is rehabilitated when the review report shows normal;
when the review report shows abnormality, it is determined that the target patient is not recovered.
In an alternative embodiment of the invention, in order to further expand the data volume of the pathogen sample library, the real-time patient data and the corresponding disease types are updated into the pathogen sample library, so that the timeliness and the accuracy of the data volume of the pathogen sample library are ensured.
According to the embodiment of the invention, the historical patient data of a historical patient is crawled by a preset web crawler, the historical patient data is classified and marked to obtain the marked patient data, the marked patient data is stored in the preset distributed storage database to obtain the pathogenic sample library, the coverage area of the pathogenic sample library is ensured to be wide, the fragmentation of disease information is reduced, the accuracy of patient inquiry is improved, secondly, the preset disease prediction model is trained according to the marked patient data in the pathogenic sample library to obtain a trained disease prediction model, the accuracy and coverage area of the trained disease prediction model are ensured from the dimension of training data, finally, the disease type of the real-time patient data is predicted by the trained disease prediction model, and the patient report of the target patient is generated according to the disease type, so that the patient can know the disease condition of the patient anytime and anywhere, and the time of selecting the disease condition according to the patient is ensured, and the pressure of medical resource shortage is relieved. Therefore, the patient report generating method, the device, the equipment and the storage medium can improve the accuracy of patient inquiry.
Fig. 4 is a functional block diagram of the patient report generating device according to the present invention.
The patient report generating apparatus 100 of the present invention may be mounted in an electronic device. Depending on the functions implemented, the patient report generating device 100 may include a pathogen sample library construction module 101, a model training module 102, and a patient report generating module 103, which may also be referred to as a unit, refers to a series of computer program segments capable of being executed by a processor of an electronic device and performing a fixed function, which are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the pathogen sample library construction module 101 is configured to crawl historical patient data of a historical patient by using a preset web crawler, classify and mark the historical patient data to obtain marked patient data, and store the marked patient data into a preset distributed storage database to obtain a pathogen sample library.
The model training module 102 is configured to encode the marked patient data by using an encoding layer in a preset disease prediction model to obtain a patient encoding matrix, calculate an attention weight of the patient encoding matrix by using an attention mechanism layer in the disease prediction model to obtain a patient weight matrix, predict a disease type of the historical patient by using a full connection layer in the disease prediction model according to the patient weight matrix, calculate a loss value of the disease type by using a preset loss function according to the marked patient data, and perform parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition, thereby obtaining a trained disease prediction model.
The patient report generating module 103 is configured to predict a disease type of the real-time patient data using the trained disease prediction model when receiving the real-time patient data of the target patient, and generate a patient report of the target patient according to the disease type and the real-time patient data.
Fig. 5 is a schematic structural diagram of an electronic device for implementing the patient report generating method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a patient report generating program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in the electronic device and various types of data, such as codes of patient report generating programs, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., patient report generating programs, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication bus 12 may be a peripheral component interconnect standard (PerIPheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 5 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, 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) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The patient report generating program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs which, when run in the processor 10, may implement:
the method comprises the steps of utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library;
Coding the marked patient data by using a coding layer in a preset disease prediction model to obtain a patient coding matrix;
calculating the attention weight of the patient coding matrix by using an attention mechanism layer in the disease prediction model to obtain a patient weight matrix;
predicting the disease type of the historical patient by using a full connection layer in the disease prediction model according to the disease weight matrix;
calculating a loss value of the disease type by using a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition to obtain a disease prediction model after training is completed;
when receiving real-time patient data of a target patient, predicting the disease type of the real-time patient data by using the trained disease prediction model, and generating a patient report of the target patient according to the disease type and the real-time patient data.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or 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).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
the method comprises the steps of utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library;
coding the marked patient data by using a coding layer in a preset disease prediction model to obtain a patient coding matrix;
calculating the attention weight of the patient coding matrix by using an attention mechanism layer in the disease prediction model to obtain a patient weight matrix;
predicting the disease type of the historical patient by using a full connection layer in the disease prediction model according to the disease weight matrix;
calculating a loss value of the disease type by using a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition to obtain a disease prediction model after training is completed;
When receiving real-time patient data of a target patient, predicting the disease type of the real-time patient data by using the trained disease prediction model, and generating a patient report of the target patient according to the disease type and the real-time patient data.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed electronic device, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of generating a patient report, the method comprising:
the method comprises the steps of utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library;
coding the marked patient data by using a coding layer in a preset disease prediction model to obtain a patient coding matrix;
Calculating the attention weight of the patient coding matrix by using an attention mechanism layer in the disease prediction model to obtain a patient weight matrix;
predicting the disease type of the historical patient by using a full connection layer in the disease prediction model according to the disease weight matrix;
calculating a loss value of the disease type by using a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition to obtain a disease prediction model after training is completed;
when receiving real-time patient data of a target patient, predicting the disease type of the real-time patient data by using the trained disease prediction model, and generating a patient report of the target patient according to the disease type and the real-time patient data.
2. The patient report generating method according to claim 1, wherein predicting the disease type of the historic patient using the full connection layer in the disease prediction model according to the patient weight matrix comprises:
performing linear transformation on the patient weight matrix by using the first preset parameter, the second preset parameter and the third preset parameter of the full-connection layer in the disease prediction model to obtain a query vector, a key vector and a numerical vector;
Multiplying the query vector by the transposed vector of the key vector to obtain a similarity matrix;
carrying out normalization calculation on the similarity matrix to obtain a normalization matrix;
calculating the normalized matrix by using a preset activation function to obtain an activation matrix;
and performing point multiplication on the activation matrix and the numerical vector to obtain the probability of the disease type corresponding to the patient weight matrix, and determining the disease type of the historical patient according to the probability.
3. The patient report generating method according to claim 1, wherein the calculating the attention weight of the patient code matrix using the attention mechanism layer in the disease prediction model to obtain a patient weight matrix comprises:
calculating the patient coding matrix by using a weight parameter preset in an attention mechanism layer in the disease prediction model to obtain the score of each individual vector in the patient coding matrix;
and carrying out weighted summation on the score of each individual vector and the corresponding patient coding matrix part to obtain a patient weight matrix.
4. The patient report generating method as set forth in claim 1, wherein the encoding the marked patient data using an encoding layer in a predetermined disease prediction model to obtain a patient encoding matrix comprises:
The marked patient data are coded word by utilizing a preset disease prediction model, and a word vector sequence is obtained;
extracting feature vectors of the marked patient data to obtain a word vector sequence;
expanding the word vector sequence according to the word number of the word vector sequence to obtain an aligned word vector sequence aligned with the word vector sequence;
performing cross multiplication on the aligned word vector sequence and a preset transformation matrix to obtain a target word vector sequence with the same dimension as the word vector sequence;
adding the target word vector sequence and the corresponding word vector sequence to obtain a word vector sequence;
performing position index coding on each character of the marked patient data to obtain a patient vector position code;
and adding the word vector sequences and the position codes corresponding to the patient vectors respectively to obtain patient spliced vectors, and coding the patient spliced vectors by using a coding layer in a preset disease prediction model to obtain a patient coding matrix.
5. The patient report generating method as set forth in claim 1, wherein said classifying and marking the historical patient data to obtain the marked patient data includes:
Screening disease information from the historical patient data, and extracting key information in the disease information;
matching the key information with the tags in the pre-constructed tag pool, and taking the successfully matched tag as a target tag of the historical patient data;
binding the target tag with the corresponding historical patient data to obtain marked patient data.
6. The patient report generating method according to claim 1, wherein after the patient report of the target patient is generated according to the disease type, further comprising:
updating the real-time patient data and the patient report to the pathogen sample library;
selecting a proper diagnosis and treatment scheme for treatment according to the patient report to obtain a treatment result;
predicting a treatment result by using the disease prediction model to obtain a re-diagnosis disease type, and generating a re-diagnosis report of the target patient according to the re-diagnosis disease type and the real-time patient data;
judging whether the target patient is recovered according to the re-diagnosis report;
determining that the target patient is rehabilitated when the review report shows normal;
when the review report shows abnormality, it is determined that the target patient is not recovered.
7. The patient report generating method as claimed in claim 1, wherein the crawling of the historical patient data of the historical patient using the predetermined web crawler comprises:
acquiring the URL of a preset initial webpage by using a preset Python programming language;
screening target URLs related to historical patients from the preset initial webpage by using a webpage analysis algorithm, and storing the target URLs into a queue to be grabbed;
grabbing target URL from the queue to be grabbed according to a preset searching strategy until the target URL meets a preset limit, and stopping grabbing the target URL;
and carrying out information analysis on the webpage corresponding to the target URL, and filtering analysis results to obtain the historical patient data of the historical patient.
8. A patient report generating device, the device comprising:
the pathogen sample library construction module is used for utilizing a preset web crawler to crawl historical patient data of a historical patient, classifying and marking the historical patient data to obtain marked patient data, and storing the marked patient data into a preset distributed storage database to obtain a pathogen sample library;
the model training module is used for coding the marked patient data by utilizing a coding layer in a preset disease prediction model to obtain a patient coding matrix, calculating the attention weight of the patient coding matrix by utilizing an attention mechanism layer in the disease prediction model to obtain a patient weight matrix, predicting the disease types of the historical patient by utilizing a full connection layer in the disease prediction model according to the patient weight matrix, calculating the loss value of the disease types by utilizing a preset loss function according to the marked patient data, and carrying out parameter iterative adjustment on the disease prediction model according to the loss value until the loss value meets a preset condition to obtain a trained disease prediction model;
And the patient report generating module is used for predicting the disease type of the real-time patient data by using the trained disease prediction model when the real-time patient data of the target patient are received, and generating a patient report of the target patient according to the disease type and the real-time patient data.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the patient report generating method of any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, which when executed by a processor implements a patient report generating method according to any one of claims 1 to 7.
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| CN116936132B (en) * | 2023-09-18 | 2024-04-26 | 深圳市即达健康医疗科技有限公司 | Intelligent medical condition monitoring method and system based on big data |
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