CN112530602A - Method and device for analyzing side effect of medicine, electronic equipment and computer storage medium - Google Patents
Method and device for analyzing side effect of medicine, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a method for analyzing side effects of a medicine, which comprises the following steps: acquiring historical clinical data, and training a pre-constructed neural network by using the historical clinical data to obtain a drug analysis model; acquiring personal information and medical examination data of a patient, generating case information, acquiring medicine information prescribed by a doctor for the patient, and performing effect analysis on the case information and the medicine information by using a medicine analysis model to obtain a first side effect inference result; carrying out interaction analysis on the drug information prescribed by the doctor for the patient by utilizing a pre-constructed drug-drug interaction relation system to obtain a second side effect inference result; a final side effect estimation result is obtained from the first side effect estimation result and the second side effect estimation result. The invention also provides a device and equipment for analyzing the side effect of the medicine and a readable storage medium. The invention can combine drug difference with individual difference, and improve accuracy of pharmacological diagnosis.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a method and a device for analyzing side effects of a medicine, electronic equipment and a computer-readable storage medium.
Background
In recent years, the number of medical visits is gradually increased, and the frequency of medical accidents is also increased. Wherein the medical accident is caused in part by the aggravation and even death of the disease caused by the interaction among the medicines prescribed by the doctor. A Drug-Drug Interaction Module (DDI) can analyze the Interaction between different drugs and reduce medical accidents caused by Drug conflict. However, DDI depends on the amount and accuracy of the database, the early warning judgment accuracy is not high, and the individual difference cannot be analyzed only for the drugs themselves, so that there is no conflict between the drugs during detection, but there is still an event of injury through the human body. Therefore, at present, there is a problem that when a medical staff judges whether a medicine has a strong side effect on a patient only through DDI, a judgment result is inaccurate.
Disclosure of Invention
The invention provides a method and a device for analyzing side effects of a medicine, electronic equipment and a computer readable storage medium, and mainly aims to improve the accuracy of pharmacological diagnosis.
In order to achieve the above object, the present invention provides a method for analyzing side effects of a pharmaceutical product, comprising:
acquiring historical clinical data, and training a pre-constructed classification neural network by using the historical clinical data to obtain a drug analysis model;
acquiring personal information and medical examination data of a patient, generating case information, acquiring medicine information prescribed by a doctor for the patient, and performing effect analysis on the case information and the medicine information by using the medicine analysis model to obtain a first side effect inference result;
carrying out interaction analysis on the drug information prescribed by the doctor for the patient by utilizing a pre-constructed drug-drug interaction relation system to obtain a second side effect inference result;
and obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
Optionally, the acquiring historical clinical data includes:
acquiring a case information set of a historical patient, a corresponding case medicine set and a clinical effect corresponding to the case medicine set from a pre-constructed hospital data management system to obtain the historical clinical data.
Optionally, the training the pre-constructed classification neural network by using the historical clinical data to obtain a drug analysis model includes:
vectorizing the historical clinical data to obtain vectorized data, and cleaning the vectorized data to obtain cleaned quantized data;
constructing a disease-drug effect graph according to the hospital clinical data;
and training a pre-constructed classification neural network according to the disease-drug effect graph to obtain the drug analysis model.
Optionally, the training a pre-constructed classification neural network to obtain the drug analysis model further includes:
setting a gradient descending direction of the classification neural network by using a momentum algorithm;
and performing K-fold cross validation on the cleaned quantitative data, searching to obtain optimal parameters, and generating the drug analysis model according to the optimal parameters.
Optionally, the performing interaction analysis on the drug information prescribed by the doctor for the patient by using a pre-constructed drug-drug interaction relationship system to obtain a second side effect inference result includes:
and searching in the drug-drug interaction relationship system according to the drug keywords or the code numbers in the drug information to obtain interaction results of the drug set in four stages of absorption, distribution, metabolism and excretion, performing weight calculation on the interaction results in the four stages, and obtaining the second side effect inference result according to the calculation results.
Optionally, the obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result includes:
when the first side effect inference result and the second side effect inference result are not larger than a preset critical warning value, prompting a user that the doctor prescribes the safety of the medicine information for the patient;
and when the first side effect inference result or the second side effect inference result is larger than a preset critical alarm value, outputting alarm information.
Optionally, after obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result, the method further includes:
and storing case information and medicine information corresponding to the negative result, and updating the medicine-medicine interaction relation system according to the case information and the medicine information corresponding to the negative result.
In order to solve the above problems, the present invention also provides a drug side effect analysis apparatus, comprising:
a model construction module: the system comprises a neural network, a drug analysis model and a data processing module, wherein the neural network is used for acquiring historical clinical data and training a pre-constructed neural network by using the historical clinical data to obtain the drug analysis model;
a detection module: the system comprises a drug analysis model, a drug analysis module, a drug-drug interaction relation system and a data processing module, wherein the drug analysis model is used for analyzing the drug information prescribed by a doctor for the patient to obtain a first side effect inference result;
and the comparison module is used for obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to cause the at least one processor to perform the method of drug side-effect analysis described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium including a storage data area and a storage program area, the storage data area storing created data, the storage program area storing a computer program; wherein the computer program, when executed by a processor, implements a method of analyzing a side effect of a drug as described in any one of the above.
The embodiment of the invention analyzes the case information and the drug information by utilizing the pre-trained drug analysis model, can obtain whether the drug and the patient have negative reaction or not, and obtains a first side effect inference result. And analyzing whether negative reactions exist between the medicines or not through a pre-constructed medicine-medicine interaction relation system to obtain a second side effect inference result, and more accurately analyzing whether the medicines given to the patients by the hospital are reasonable or not through the first side effect inference result and the second side effect inference result.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for analyzing side effects of a drug according to an embodiment of the present invention;
FIG. 2 is a block diagram of a drug side effect analysis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a method for analyzing a side effect of a drug according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a method for analyzing side effects of a medicine. The execution subject of the drug side effect analysis method includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server, a terminal, and the like. In other words, the method for analyzing the side effect of the drug may be performed by software or hardware installed in the terminal device or the server device, and the software may 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.
Fig. 1 is a schematic flow chart of a method for analyzing side effects of a drug according to an embodiment of the present invention. In this embodiment, the method for analyzing side effects of a pharmaceutical product includes:
and S1, acquiring historical clinical data, and training the pre-constructed classification neural network by using the historical clinical data to obtain a drug analysis model.
In detail, in an embodiment of the present invention, the acquiring historical clinical data includes:
acquiring a case information set of a historical patient, a corresponding case medicine set and a clinical effect corresponding to the case medicine set from a pre-constructed hospital data management system to obtain the historical clinical data.
In the embodiment of the invention, the hospital data management system is used for recording all treatment records of patients to be treated in the hospital, including but not limited to personal identity information, disease symptom information, medical examination information, opening and standing medicine information, treatment effect information and the like of the patients.
In detail, in the embodiment of the present invention, the training of the pre-constructed classification neural network by using the historical clinical data to obtain the drug analysis model includes:
vectorizing the historical clinical data to obtain vectorized data, and cleaning the vectorized data to obtain cleaned quantized data; constructing a disease-drug effect graph according to the hospital clinical data; and training a pre-constructed classification neural network according to the disease-drug effect graph to obtain the drug analysis model.
The vectorization process is a process of converting the sample set from text data into vectorized data of a format type that can be executed in the drug analysis model. The cleaning process comprises the steps of removing duplicate, clearing errors and privacy data.
In detail, in the embodiment of the present invention, the constructing a disease-drug effect map according to the hospital clinical data includes:
constructing a case information node, a medicine information node and an effect information node; and according to the case information node, the medicine information node and the effect information node, building the relation graph in the form of a document object model tree by using a hypertext markup language interpreter.
The nodes are storage nodes and are correspondingly connected with one another. The embodiment of the invention respectively stores the case information set, the medicine information set and the corresponding clinical effect in the historical clinical data by using the case information node, the medicine information node and the effect information node.
Further, the embodiment of the present invention constructs the nodes as a relationship graph in a Document Object Model (DOM) tree form through the hypertext markup language (HTML) interpreter.
The HTML is called hypertext markup language, and is a kind of identifying language. It can make the scattered Internet resources connected into a logic whole. The relation graph is in a DOM node tree form, shows a set of the case information nodes, the medicine information nodes and the effect information nodes, can access all the nodes through the relation graph, and modifies and deletes the content in the nodes.
In the embodiment of the invention, the classification neural network is a binary neural network trained by using TensorFlow, and consists of a plurality of neurons, the neurons respectively store data of each node, and according to the relational graph, various case information and types of medicine information are continuously trained and learned, so that the condition or disease patients can be finally judged to be allergic or aggravated to the medicine, the condition information of the patients is finally different, the result of judging the same medicine is possibly different, and the diversity of medicine diagnosis is increased.
In detail, in the embodiment of the present invention, the training a pre-constructed classification neural network to obtain the drug analysis model includes:
setting a gradient descending direction of the classification neural network by using a momentum algorithm; and performing K-fold cross validation on the cleaned quantitative data, searching to obtain optimal parameters, and generating the drug analysis model according to the optimal parameters.
The momentum algorithm is an optimization method in the gradient descending process, the descending speed at the current moment is related to the speed at the previous moment by continuously accumulating the momentum of gradient descending, the influence of interference factors can be reduced, the stability of the descending process is improved, and the optimization speed of the classification neural network is improved.
The K-fold cross validation is that the cleaning quantitative data are divided into K parts in an equal proportion, one part is used as test data, and the other K-1 parts are used as training data. In the embodiment of the invention, the K is set to be 10, and the cleaning quantification data is divided into 10 parts. S2, obtaining personal information and medical examination data of a patient, generating case information, obtaining medicine information prescribed by a doctor for the patient, and performing effect analysis on the case information and the medicine information by using the medicine analysis model to obtain a first side effect inference result.
In an embodiment of the present invention, the personal information includes various indicators of the patient, such as age, sex, medical history, and data on which drugs are known to be allergic. The medical examination data includes user physical index data detected by various medical examination devices, including, for example, blood pressure, blood lipid, folic acid, protein content, and the like.
And constructing case information of the patient by the personal information and the medical examination data, importing the case information and the medicine information issued by a doctor into the medicine analysis model, and judging the first side effect result of the medicine on the patient. In the embodiment of the present invention, the first side effect result may determine whether the drug meets the symptoms of the patient, or whether the drug generates allergy to the patient.
S3, carrying out interaction analysis on the drug information prescribed by the doctor for the patient by utilizing a pre-constructed drug-drug interaction relation system to obtain a second side effect inference result.
In the embodiment of the invention, the Drug-Drug interaction (DDI) system is a tool for Drug information conflict detection commonly used in the medical field, and can retrieve the Drug-Drug interaction database to perform interaction detection on the Drug information to obtain interaction result detection results of each stage.
In detail, in an embodiment of the present invention, the S3 includes:
searching in the drug-drug interaction relationship system according to the drug keywords or the code numbers in the drug information to obtain interaction results of the drug set in four stages of absorption, distribution, metabolism and excretion, performing weight calculation on the interaction results in the four stages, and obtaining the second side effect inference result according to the calculation results.
In the embodiment of the present invention, the interaction results of the absorption and metabolism stages each account for forty percent of the entire process, and the interaction results of the distribution and excretion stages each account for ten percent of the entire process, so that the second side effect inference result is (absorption result + metabolism result) × 0.4+ (distribution result + excretion result) × 0.1.
And S4, obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
In detail, in the preferred embodiment of the present invention, the S4 includes:
when the first side effect inference result and the second side effect inference result are not larger than a preset critical warning value, prompting a user that the doctor prescribes the safety of the medicine information for the patient;
and when the first side effect inference result or the second side effect inference result is larger than a preset critical alarm value, outputting alarm information.
In an embodiment of the present invention, the first side effect inference result or the second side effect inference result outputs an alarm value as long as one of the first side effect inference result and the second side effect inference result is greater than a preset threshold alarm value, that is, shows a negative side effect. Further, when the first side effect inference result is greater than a preset threshold warning value and the second side effect inference result is not greater than the preset threshold warning value, the embodiment of the invention outputs an inference explanation, which indicates that the interaction results generated by the components among the medicines have no negative influence on the patient in each stage.
Further, in the embodiment of the present invention, after the outputting the alarm information, the embodiment of the present invention further includes:
and updating the drug-drug interaction relationship system and the historical clinical data by using case information and drug information corresponding to the alarm information.
In the embodiment of the present invention, the generation of the first side-effect inference result depends on the case information node, the drug information node, and the diagnosis information node, and the medical record database includes the case information node, the drug information node, and the diagnosis information node. The medical record database is updated continuously, so that the further optimization of the drug analysis model is facilitated, and the analysis result is more accurate.
The embodiment of the invention analyzes the case information and the drug information by utilizing the pre-trained drug analysis model, can obtain whether the drug and the patient have negative reaction or not, obtain the first side effect inference result, and then analyze whether the drug and the drug have negative reaction or not by the pre-constructed drug-drug interaction relation system to obtain the second side effect inference result, and can more accurately analyze whether the drug given to the patient by the hospital is reasonable or not by the first side effect inference result and the second side effect inference result, so that the embodiment of the invention can combine the drug difference with the individual difference, and improve the accuracy of pharmacological diagnosis.
Fig. 2 is a schematic block diagram of the apparatus for analyzing adverse drug reactions according to the present invention.
The drug side effect analysis apparatus 100 according to the present invention may be installed in an electronic device. According to the realized functions, the medicine side effect analysis device can comprise a model building module 101, a detection module 102 and a comparison module 103. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the model building module 101 is configured to obtain historical clinical data, and train a pre-built classification neural network by using the historical clinical data to obtain a drug analysis model.
In detail, in the embodiment of the present invention, the model building module 101 obtains a case information set of a historical patient and a corresponding case drug set from a pre-constructed hospital data management system, and obtains the historical clinical data by using the clinical effects of the case information set and the case drug set.
In the embodiment of the invention, the hospital data management system is used for recording all treatment records of patients to be treated in the hospital, including but not limited to personal identity information, disease symptom information, medical examination information, opening and standing medicine information, treatment effect information and the like of the patients.
In detail, in the embodiment of the present invention, when the pre-constructed neural network is trained by using the historical clinical data to obtain a drug analysis model, the model construction module 101 is configured to perform the following operations:
vectorizing the historical clinical data to obtain vectorized data, and cleaning the vectorized data to obtain cleaned quantized data; constructing a disease-drug effect graph according to the hospital clinical data; and training a pre-constructed classification neural network according to the disease-drug effect graph to obtain the drug analysis model.
The vectorization process is a process of converting the sample set from text data into vectorized data of a format type that can be executed in the drug analysis model. The cleaning process comprises the steps of removing duplicate, clearing errors and privacy data.
In detail, in the embodiment of the present invention, the constructing a disease-drug effect map according to the hospital clinical data includes:
constructing a case information node, a medicine information node and an effect information node; and according to the case information node, the medicine information node and the effect information node, building the relation graph in the form of a document object model tree by using a hypertext markup language interpreter.
The nodes are storage nodes and are correspondingly connected with one another. The embodiment of the invention respectively stores the case information set, the medicine information set and the corresponding clinical effect in the historical clinical data by using the case information node, the medicine information node and the effect information node.
Further, the embodiment of the present invention constructs the nodes as a relationship graph in a Document Object Model (DOM) tree form through the hypertext markup language (HTML) interpreter.
The HTML is called hypertext markup language, and is a kind of identifying language. It can make the scattered Internet resources connected into a logic whole. The relation graph is in a DOM node tree form, shows a set of the case information nodes, the medicine information nodes and the effect information nodes, can access all the nodes through the relation graph, and modifies and deletes the content in the nodes.
In the embodiment of the invention, the classification neural network is a binary neural network trained by using TensorFlow, and consists of a plurality of neurons, the neurons respectively store data of nodes, and various case information and medicine information types are enriched by continuous training according to the relational graph, so that the condition or disease patients can be judged to be allergic or aggravated to the medicine, the condition information of the patients is different, the results of the same medicine are judged to be possibly different, and the diversity of medicine diagnosis is increased.
In detail, in the embodiment of the present invention, the training a pre-constructed classification neural network to obtain the drug analysis model includes:
setting the gradient descending direction of the medicine analysis model by using a momentum algorithm; and performing K-fold cross validation on the cleaned quantitative data, searching to obtain optimal parameters, and generating the drug analysis model according to the optimal parameters.
The momentum algorithm is an optimization method in the gradient descending process, the descending speed at the current moment is related to the speed at the last moment by continuously accumulating the gradient descending momentum, the influence of interference factors can be reduced, the stability of the descending process is improved, and the optimization speed of the medicine analysis model is improved.
The K-fold cross validation is that the cleaning quantitative data are divided into K parts in an equal proportion, one part is used as test data, and the other K-1 parts are used as training data. In the embodiment of the invention, the K is set to be 10, and the cleaning quantification data is divided into 10 parts. The detection module 102 is configured to obtain personal information and medical examination data of a patient, generate case information, obtain medicine information prescribed by a doctor for the patient, perform effect analysis on the case information and the medicine information by using the medicine analysis model to obtain a first side effect inference result, and perform interaction analysis on the medicine information prescribed by the doctor for the patient by using a pre-established medicine-medicine interaction relationship system to obtain a second side effect inference result.
In an embodiment of the present invention, the personal information includes various indicators of the patient, such as age, sex, medical history, and data on which drugs are known to be allergic. The medical examination data includes user physical index data detected by various medical examination devices, including, for example, blood pressure, blood lipid, folic acid, protein content, and the like.
And constructing case information of the patient by the personal information and the medical examination data, importing the case information and the medicine information issued by a doctor into the medicine analysis model, and judging the first side effect result of the medicine on the patient. In the embodiment of the present invention, the first side effect result may determine whether the drug meets the symptoms of the patient, or whether the drug generates allergy to the patient.
In the embodiment of the invention, the Drug-Drug interaction (DDI) system is a tool for Drug information conflict detection commonly used in the medical field, and can retrieve the Drug-Drug interaction database to perform interaction detection on the Drug information to obtain interaction result detection results of each stage.
In detail, in an embodiment of the present invention, when a pre-constructed drug-drug interaction relationship system is used to perform interaction analysis on drug information prescribed by the doctor for the patient to obtain a second side effect inference result, the detection module 102 is specifically configured to:
searching in the drug-drug interaction relationship system according to the drug keywords or the code numbers in the drug information to obtain interaction results of the drug set in four stages of absorption, distribution, metabolism and excretion, performing weight calculation on the interaction results in the four stages, and obtaining the second side effect inference result according to the calculation results.
In the embodiment of the present invention, the interaction results of the absorption and metabolism stages each account for forty percent of the entire process, and the interaction results of the distribution and excretion stages each account for ten percent of the entire process, so that the second side effect inference result is (absorption result + metabolism result) × 0.4+ (distribution result + excretion result) × 0.1.
The comparison module 103 is configured to obtain a final side effect inference result according to the first side effect inference result and the second side effect inference result.
In detail, in the preferred embodiment of the present invention, the comparison module 103 is specifically configured to:
when the first side effect inference result and the second side effect inference result are not larger than a preset critical warning value, prompting a user that the doctor prescribes the safety of the medicine information for the patient;
and when the first side effect inference result or the second side effect inference result is larger than a preset critical alarm value, outputting alarm information.
In an embodiment of the present invention, the first side effect inference result or the second side effect inference result outputs an alarm value as long as one of the first side effect inference result and the second side effect inference result is greater than a preset threshold alarm value, that is, shows a negative side effect. Further, when the first side effect inference result is greater than a preset threshold warning value and the second side effect inference result is not greater than the preset threshold warning value, the embodiment of the invention outputs an inference explanation, which indicates that the interaction results generated by the components among the medicines have no negative influence on the patient in each stage.
Further, in the embodiment of the present invention, after the outputting the alarm information, the embodiment of the present invention further includes:
and updating the drug-drug interaction relationship system and the historical clinical data by using case information and drug information corresponding to the alarm information.
In the embodiment of the present invention, the generation of the first side-effect inference result depends on the case information node, the drug information node, and the diagnosis information node, and the medical record database includes the case information node, the drug information node, and the diagnosis information node. The medical record database is updated continuously, so that the further optimization of the drug analysis model is facilitated, and the analysis result is more accurate.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the method for analyzing side effects of drugs according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a drug side-effect analysis program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of the medicine side effect analysis program 12, but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., executing a drug side effect analysis program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and 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 for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally 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 device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The drug side-effect analysis program 12 stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs that, when executed in the processor 10, enable:
acquiring historical clinical data, and training a pre-constructed neural network by using the historical clinical data to obtain a drug analysis model;
acquiring personal information and medical examination data of a patient, generating case information, acquiring medicine information prescribed by a doctor for the patient, and performing effect analysis on the case information and the medicine information by using the medicine analysis model to obtain a first side effect inference result;
carrying out interaction analysis on the drug information prescribed by the doctor for the patient by utilizing a pre-constructed drug-drug interaction relation system to obtain a second side effect inference result;
and obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may 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 said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
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 according to the use of the blockchain node, and the like.
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring historical clinical data, and training a pre-constructed neural network by using the historical clinical data to obtain a drug analysis model;
acquiring personal information and medical examination data of a patient, generating case information, acquiring medicine information prescribed by a doctor for the patient, and performing effect analysis on the case information and the medicine information by using the medicine analysis model to obtain a first side effect inference result;
carrying out interaction analysis on the drug information prescribed by the doctor for the patient by utilizing a pre-constructed drug-drug interaction relation system to obtain a second side effect inference result;
and obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
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 attributes 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 accompanying claims should not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, 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 means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for analyzing side effects of a pharmaceutical product, the method comprising:
acquiring historical clinical data, and training a pre-constructed classification neural network by using the historical clinical data to obtain a drug analysis model;
acquiring personal information and medical examination data of a patient, generating case information, acquiring medicine information prescribed by a doctor for the patient, and performing effect analysis on the case information and the medicine information by using the medicine analysis model to obtain a first side effect inference result;
carrying out interaction analysis on the drug information prescribed by the doctor for the patient by utilizing a pre-constructed drug-drug interaction relation system to obtain a second side effect inference result;
and obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
2. The method of analyzing adverse drug effects of claim 1, wherein said obtaining historical clinical data comprises:
acquiring a case information set of a historical patient, a corresponding case medicine set and a clinical effect corresponding to the case medicine set from a pre-constructed hospital data management system to obtain the historical clinical data.
3. The method for analyzing adverse drug reactions of claim 1, wherein the training of the pre-constructed neural network using the historical clinical data to obtain the drug analysis model comprises:
vectorizing the historical clinical data to obtain vectorized data, and cleaning the vectorized data to obtain cleaned quantized data;
constructing a disease-drug effect graph according to the hospital clinical data;
and training a pre-constructed classification neural network according to the disease-drug effect graph to obtain the drug analysis model.
4. The method of analyzing adverse drug reactions of claim 3, wherein the training of the pre-constructed neural network to obtain the drug analysis model further comprises:
setting a gradient descending direction of the classification neural network by using a momentum algorithm;
and performing K-fold cross validation on the cleaned quantitative data, searching to obtain optimal parameters, and generating the drug analysis model according to the optimal parameters.
5. The method of analyzing adverse drug reactions according to claim 1, wherein the analyzing interaction between the drug information prescribed by the physician to the patient using a pre-established drug-drug interaction relationship system to obtain a second adverse drug reaction inference comprises:
searching in the drug-drug interaction relationship system according to the drug keywords or the code numbers in the drug information to obtain interaction results of the drug set in four stages of absorption, distribution, metabolism and excretion, performing weight calculation on the interaction results in the four stages, and obtaining the second side effect inference result according to the calculation results.
6. The method for analyzing adverse side effects of a pharmaceutical product according to any one of claims 1 to 5, wherein the obtaining a final adverse side effect estimation result from the first adverse side effect estimation result and the second adverse side effect estimation result includes:
when the first side effect inference result and the second side effect inference result are not larger than a preset critical warning value, prompting a user that the doctor prescribes the safety of the medicine information for the patient;
and when the first side effect inference result or the second side effect inference result is larger than a preset critical alarm value, outputting alarm information.
7. The method for analyzing adverse drug effects according to claim 6, further comprising, after outputting the alarm information:
and updating the drug-drug interaction relationship system and the historical clinical data by using case information and drug information corresponding to the alarm information.
8. A drug side effect analysis device, comprising:
a model construction module: the system comprises a neural network, a drug analysis model and a data processing module, wherein the neural network is used for acquiring historical clinical data and training a pre-constructed neural network by using the historical clinical data to obtain the drug analysis model;
a detection module: the system comprises a drug analysis model, a drug analysis module, a drug-drug interaction relation system and a data processing module, wherein the drug analysis model is used for analyzing the drug information prescribed by a doctor for the patient to obtain a first side effect inference result;
and the comparison module is used for obtaining a final side effect inference result according to the first side effect inference result and the second side effect inference result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to cause the at least one processor to perform a method of analyzing a side effect of a pharmaceutical product according to any one of claims 1 to 7.
10. A computer-readable storage medium comprising a storage data area storing created data and a storage program area storing a computer program; wherein the computer program, when executed by a processor, implements the method of analyzing a side effect of a drug according to any one of claims 1 to 7.
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