Drug adverse event data management system and method based on big data
Through a drug adverse event data management system based on big data, we collect and analyze drug adverse information, distinguish behavioral users, establish an information database, judge the type of drug adverse event, and solve the drug adverse event in the existing technology through the analysis of the overlapping degree of formula information. The timeliness and accuracy of data management have achieved effective data support and warning effects for drug market launch.
Patent Information
- Application Number
- CN202411021177.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-29
- Publication Date
- 2024-12-17
AI Technical Summary
During the existing data management of adverse drug events, the timely collection of adverse drug information for batches is poor, and it is difficult to accurately determine whether adverse drug events are normal or abnormal reactions, resulting in poor data accuracy and lack of effective data support and Warning effect.
A drug adverse event data management system based on big data was designed. Through the data collection unit, suspected analysis unit and early warning prompt unit, the drug adverse information is collected and analyzed, the behavioral users are distinguished, and the drug adverse event information database is established, and a variety of types are compared. Use medication, judge the type of adverse drug events, and provide early warning prompts through the analysis of the overlapping degree of drug formula information.
It realizes timely early warning and handling of adverse drug events, reduces the impact of adverse events, improves the accuracy and effectiveness of data, and provides effective data support and warning effects for drug market launch.
Smart Images
Figure CN119153129A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data management, and in particular to a drug adverse event data management system and method based on big data. Background Art
[0002] Adverse drug events (ADEs) refer to harmful reactions that are unrelated to the therapeutic purpose or unexpected during the use of drugs. These reactions may include side effects, drug misuse, drug abuse, etc., which may cause damage to the patient's health or even endanger his life.
[0003] Referring to the Chinese patent, a method for detecting adverse drug events (announcement number: CN118098635A, announcement date: 2024.05.28), this patent detects the reaction of the patient's historical adverse events and analyzes whether the reaction of the historical adverse events exceeds the reasonable range, so that the patient's medication can be adjusted in time to ensure patient safety and treatment effect; by comparing and analyzing the relationship between the historical adverse event characterization value and the new adverse event characterization value, it provides medical staff with a means to predict the risk of new adverse events. Medical staff can take corresponding measures to reduce the risk of new adverse events based on this relationship, thereby improving the safety and effectiveness of drug treatment.
[0004] However, there are still defects in the existing adverse drug event data management process. For example, in the process of collecting adverse drug information, it is usually processed when a large number of people appear, which has caused a great impact. The timely warning effect is poor. In addition, when patients use multiple types of medications, it is necessary to judge whether the adverse drug event is a normal expected reaction or an abnormal expected reaction. As long as the adverse reaction caused by taking the drug is recorded, the data accuracy will be poor. There is no analysis of various drug formula information, resulting in a lack of effective data support and data warning effect when the subsequent drugs are put on the market. For this reason, the present invention proposes a drug adverse event data management system and method based on big data to solve the above-mentioned problems. Summary of the invention
[0005] 1. Technical issues to be solved
[0006] In view of the deficiencies in the prior art, the present invention provides a drug adverse event data management system and method based on big data, which solves the problems mentioned in the above background technology.
[0007] (II) Technical solution
[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A drug adverse event data management system based on big data, comprising:
[0009] Data collection unit: collects adverse information of behavioral users, and divides the behavioral users into first behavioral users and second behavioral users. The first behavioral user is the first patient who has adverse reactions, and the second behavioral user is other patients who have adverse reactions. The adverse drug information is analyzed according to the data of the behavioral users;
[0010] Among them, behavioral users are specifically patients who have adverse drug reactions, and adverse information is specifically unexpected drug reactions;
[0011] Suspected analysis unit: distinguish the medication conditions of patients with adverse reactions, and divide them into one type of medication and multiple types of medication, establish a drug adverse event information database, compare the multiple types of medication with the drug adverse event information database, and judge whether the drug adverse event is a normal expected reaction or an abnormal expected reaction;
[0012] Early warning prompt unit: collect the first formula information of the first drug, generate a first table using the first formula information, collect the second formula information of the second drug, generate a second table using the second formula information, analyze the overlap of the formula information in the first table and the second table, and issue early warning prompts for some drug formulas, wherein the first drug is specifically a drug that produces adverse reactions, and the second drug is specifically other drugs.
[0013] Preferably, the specific method of analyzing the adverse drug information according to the data of the behavioral user in the data collection unit is:
[0014] S1: First, the time of drug launch is recorded as AT, then the time of the first patient with adverse reaction is recorded as BT, and BT-AT is recorded as the gap period of drug adverse reaction. At this time, the number of other patients with adverse reactions is recorded as St, t is the sequence number of the time range, and St is a floating value or a fixed value;
[0015] S2: Then set the number threshold YS. When St≥YS, the time when the number of other patients with adverse reactions reaches St is recorded as CT. CT is the prompt warning time of drug adverse reaction events. After reaching time CT, the sale of the drug is suspended.
[0016] Preferably, when BT-AT is recorded as the idle period of adverse drug reactions in S1, when the same type of drugs are approved for marketing, they need to be approved for marketing after the idle period BT-AT ends.
[0017] Preferably, the specific method of comparing the multiple types of medications with the adverse drug event information database in the suspected analysis unit to determine whether the adverse drug event is a normal expected reaction or an abnormal expected reaction is:
[0018] P1: First, establish a drug adverse event information database, denoted as Ks, record other drugs that have adverse reactions with the current drug in Ks, record the drug with the current adverse reaction as YA, and then record other drugs in multiple types of medication as Yn, where n is the serial number of different drugs;
[0019] P2: At this time, Yn is compared with Ks. When Yn∈Ks, the adverse drug event is judged to be a normal expected reaction. At this time, the adverse drug event is judged to be an abnormal expected reaction.
[0020] Preferably, when recording other drugs that have adverse reactions with the current drug in Ks in P1, the name of the drug, the time when it cannot be taken, the adverse reaction status after taking it, and the corresponding emergency measures are specifically recorded.
[0021] Preferably, the specific manner in which the early warning prompt unit analyzes the overlap of the formula information in the first table and the second table and issues early warning prompts for some drug formulas is:
[0022] M1: First, record the formula information in the first table as Kj, and record the formula information in the second table as Fj, where j is the serial number of the formula information. Then extract the part of Fj∈Kj and record it as Fv. At this time, Fv is the existing drug formula information closest to Kj. Then record the difference between Kj and Fv as QE, where QE is the drug formula for early warning.
[0023] Preferably, the formula information in the second table recorded in M1 is recorded as Fj, and all of them are drugs that do not produce adverse reactions.
[0024] Preferably, the method further includes a population analysis unit: collecting the ages of patients who have adverse reactions, analyzing the degree of adverse reactions caused by the drug at different age stages, and recording and storing the analysis results.
[0025] The present invention also discloses a method for managing adverse drug event data based on big data, which specifically includes the following steps:
[0026] Step 1: Collect adverse information of behavioral users, and divide behavioral users into first behavioral users and second behavioral users. The first behavioral user is the first patient who has adverse reactions, and the second behavioral user is other patients who have adverse reactions. Analyze the adverse drug information based on the data of the behavioral users;
[0027] Step 2: Differentiate the medication conditions of patients with adverse reactions into one type of medication and multiple types of medication, establish a drug adverse event information database, compare the multiple types of medication with the drug adverse event information database, and determine whether the drug adverse event is a normal expected reaction or an abnormal expected reaction;
[0028] Step 3: Collect the first formula information of the first drug, generate a first table using the first formula information, collect the second formula information of the second drug, generate a second table using the second formula information, analyze the overlap of the formula information in the first table and the second table, and issue early warning prompts for some drug formulas;
[0029] Step 4: Collect the ages of patients who experience adverse reactions, analyze the degree of adverse reactions caused by the drug at different age stages, and record and store the results of the analysis.
[0030] (III) Beneficial effects
[0031] The present invention provides a drug adverse event data management system and method based on big data. Compared with the prior art, it has the following beneficial effects:
[0032] This big data-based adverse drug event data management system and method analyzes adverse drug information by collecting data from behavioral users. It can promptly issue adverse drug event warnings based on the number of people with adverse drug reactions and the time, reduce the impact of adverse drug events, and distinguish the medication conditions of patients with adverse reactions. When using multiple types of medications, it can judge whether the adverse drug events are normal expected reactions or abnormal expected reactions, reduce errors caused by data, cooperate with the overlap analysis of drug formula information, reduce the analysis process of drug ingredients, and analyze different drug formulas. It can provide effective data support and data warning effects for subsequent drug launches. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 This is a block diagram of a drug adverse event data management system based on big data shown in an embodiment of the present application;
[0034] Figure 2 A schematic diagram of the structure of an electronic device shown in an embodiment of the present application;
[0035] Figure 3 This is a flow chart of a method for managing adverse drug event data based on big data, as shown in an embodiment of the present application. DETAILED DESCRIPTION
[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0037] See also Figure 1-Figure 3, the embodiment of the present invention provides five technical solutions:
[0038] Embodiment 1
[0039] A drug adverse event data management system based on big data, comprising:
[0040] Data collection unit: collects adverse information of behavioral users, and divides the behavioral users into first behavioral users and second behavioral users, wherein the first behavioral user is the first patient with adverse reactions, and the second behavioral user is other patients with adverse reactions, and analyzes adverse drug information based on the data of behavioral users; wherein the behavioral users are specifically patients with adverse drug reactions, and the adverse information is specifically unexpected drug reactions; the specific method of analyzing adverse drug information based on the data of behavioral users in the data collection unit is:
[0041] S1: First, the time of drug launch is recorded as AT, then the time of the first patient with adverse reaction is recorded as BT, and BT-AT is recorded as the gap period of drug adverse reaction. At this time, the number of other patients with adverse reaction is recorded as St, t is the sequence number of the time range, where St is a floating value or a fixed value; when BT-AT is recorded as the gap period of drug adverse reaction in S1, when the same type of drugs are approved for marketing, they need to be approved for marketing after the gap period BT-AT ends;
[0042] S2: Then set the number threshold YS. When St≥YS, the time when the number of other patients with adverse reactions reaches St is recorded as CT. CT is the prompt warning time of drug adverse reaction events. After reaching time CT, the sale of the drug is suspended.
[0043] Combined with the analysis of examples, the data provided by the hospital is currently used as the basis for data statistics. When the patient visits the hospital for treatment and the adverse drug reaction is confirmed, the time is recorded as BT. Then BT-AT is the gap period of adverse drug reactions. This is because the adverse drug reactions are exposed only after a large number of people use them for a long time after the drug is launched on the market. The generation of BT at this time is due to the randomness of the data and the patient's own reasons. No warning is given at this time. When the number threshold YS is set to 10 people, when 10 people have adverse drug reactions, a drug adverse event prompt is issued. At this time, the sale of the drug is urgently suspended, and the relevant departments, hospitals and major pharmacies are connected to the Internet for information exchange, and data management is carried out based on big data.
[0044] Suspected analysis unit: Distinguish the medication situation of patients with adverse reactions into one type of medication and multiple types of medication, establish a drug adverse event information database, compare multiple types of medication with the drug adverse event information database, and judge whether the drug adverse event is a normal expected reaction or an abnormal expected reaction; the specific method of comparing multiple types of medication with the drug adverse event information database in the suspected analysis unit to judge whether the drug adverse event is a normal expected reaction or an abnormal expected reaction is:
[0045] P1: First, establish a drug adverse event information database, denoted as Ks, record other drugs that have adverse reactions with the current drug in Ks, record the drug with the current adverse reaction as YA, and then record other drugs in multiple types of medication as Yn, where n is the serial number of different drugs; in P1, when recording other drugs that have adverse reactions with the current drug in Ks, specifically record the name of the drug, the time when it cannot be taken, the adverse reaction status after taking it, and the corresponding emergency measures;
[0046] P2: At this time, Yn is compared with Ks. When Yn∈Ks, the adverse drug event is judged to be a normal expected reaction. At this time, the adverse drug event is judged to be an abnormal expected reaction.
[0047] Combined with the analysis of examples, taking drug A as an example, the drugs that produce adverse reactions with drug A are recorded as drug B, drug C and drug D in turn. Drug B, drug C and drug D constitute the adverse event information database Ks. Ks records the names of drug B, drug C and drug D, the time when the drugs in Ks cannot be taken after taking drug A, the adverse reaction status of taking drugs in Ks after taking drug A, and the corresponding emergency measures, which are taking other drugs for relief or seeking medical treatment directly. When Yn∈Ks, then this is an expected reaction of the drug and is not counted as an adverse drug reaction of the drug. Otherwise, adverse drug reaction records are required.
[0048] Early warning prompt unit: collect first formula information of a first drug, generate a first table using the first formula information, collect second formula information of a second drug, generate a second table using the second formula information, analyze the overlap of formula information in the first table and the second table, and issue early warning prompts for some drug formulas, wherein the first drug is specifically a drug that produces adverse reactions, and the second drug is specifically other drugs; the early warning prompt unit analyzes the overlap of formula information in the first table and the second table, and issues early warning prompts for some drug formulas in a specific manner as follows:
[0049] M1: First, the formula information in the first table is recorded as Kj, and the formula information in the second table is recorded as Fj, where j is the serial number of the formula information. Then, the part of Fj∈Kj is extracted and recorded as Fv. At this time, Fv is the existing drug formula information closest to Kj. Then, the difference between Kj and Fv is recorded as QE, where QE is the drug formula for the early warning prompt. The formula information in the second table recorded in M1 is recorded as Fj, which are all drugs that have no adverse reactions.
[0050] It should be noted that, in the process of making early warning judgments, the time of drug Fj's launch should be before or even earlier than the time of drug Kj's launch, and it is preferred that drug Fj should be one that has been on the market for more than 6 months.
[0051] Combined with the example for analysis, take Kj's components as A, B, C, D and E, and search for formula information in Fj that is only one or more of A, B, C, D and E. Take the closest formula information as A, B, C and D as an example. At this time, the difference between Kj and Fv is E, so the formula information E needs an early warning.
[0052] Embodiment 2
[0053] This embodiment is based on the first embodiment, and differs from the first embodiment in that it also includes a population analysis unit: collecting the age of patients who have adverse reactions, analyzing the degree of adverse reactions caused by drugs at different age stages, and recording and storing the analysis results; dividing the population at different stages into age segments, and then analyzing the proportion of each age group in the adverse reaction data, and analyzing the degree of impact based on the proportion.
[0054] Embodiment 3
[0055] Compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments for implementation.
[0056] Embodiment 4
[0057] A method for managing adverse drug event data based on big data specifically includes the following steps:
[0058] Step 1: Collect adverse information of behavioral users, and divide behavioral users into first behavioral users and second behavioral users. The first behavioral user is the first patient who has adverse reactions, and the second behavioral user is other patients who have adverse reactions. Analyze the adverse drug information based on the data of the behavioral users;
[0059] Step 2: Differentiate the medication conditions of patients with adverse reactions into one type of medication and multiple types of medication, establish a drug adverse event information database, compare the multiple types of medication with the drug adverse event information database, and determine whether the drug adverse event is a normal expected reaction or an abnormal expected reaction;
[0060] Step 3: Collect the first formula information of the first drug, generate a first table using the first formula information, collect the second formula information of the second drug, generate a second table using the second formula information, analyze the overlap of the formula information in the first table and the second table, and issue early warning prompts for some drug formulas;
[0061] Step 4: Collect the ages of patients who experience adverse reactions, analyze the degree of adverse reactions caused by the drug at different age stages, and record and store the results of the analysis.
[0062] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.
[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0064] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
[0065] Embodiment 5
[0066] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides an electronic device and corresponding embodiments.
[0067] See also Figure 2 , the electronic device 1000 includes a memory 1010 and a processor 1020 .
[0068] The processor 1020 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
[0069] The memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 1010 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.
[0070] The memory 1010 stores executable codes, and when the executable codes are processed by the processor 1020 , the processor 1020 can execute part or all of the methods described above.
[0071] The scheme of the present application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, refer to the relevant description of other embodiments. Those skilled in the art should also know that the actions and modules involved in the description are not necessarily required for the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.
[0072] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.
[0073] Alternatively, the present application can also be implemented as a non-temporary machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or electronic device, server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.
[0074] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the application herein may be implemented as electronic hardware, computer software, or combinations of both.
[0075] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system and method according to multiple embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and the part of the module, the program segment or the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or the flow chart, and the combination of the square boxes in the block diagram and / or the flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0076] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.
Claims
1. A drug adverse event data management system based on big data, characterized in that: include: Data collection unit: collects adverse information of behavioral users, and divides the behavioral users into first behavioral users and second behavioral users. The first behavioral user is the first patient who has adverse reactions, and the second behavioral user is other patients who have adverse reactions. The adverse drug information is analyzed according to the data of the behavioral users; Among them, behavioral users are specifically patients who have adverse drug reactions, and adverse information is specifically unexpected drug reactions; Suspected analysis unit: distinguish the medication conditions of patients with adverse reactions, and divide them into one type of medication and multiple types of medication, establish a drug adverse event information database, compare the multiple types of medication with the drug adverse event information database, and judge whether the drug adverse event is a normal expected reaction or an abnormal expected reaction; Early warning prompt unit: collect the first formula information of the first drug, generate a first table using the first formula information, collect the second formula information of the second drug, generate a second table using the second formula information, analyze the overlap of the formula information in the first table and the second table, and issue early warning prompts for some drug formulas, wherein the first drug is specifically a drug that produces adverse reactions, and the second drug is specifically other drugs.
2. The big data-based drug adverse event data management system according to claim 1, characterized in that: The specific method of analyzing the adverse drug information according to the data of the behavioral user in the data collection unit is: S1: First, the time of drug launch is recorded as AT, then the time of the first patient with adverse reaction is recorded as BT, and BT-AT is recorded as the gap period of drug adverse reaction. At this time, the number of other patients with adverse reactions is recorded as St, t is the sequence number of the time range, and St is a floating value or a fixed value; S2: Then set the number threshold YS. When St≥YS, the time when the number of other patients with adverse reactions reaches St is recorded as CT. CT is the prompt warning time of drug adverse reaction events. After reaching time CT, the sale of the drug is suspended.
3. The big data-based drug adverse event data management system according to claim 2, characterized in that: When BT-AT is recorded as the idle period of adverse drug reactions in S1, when the same type of drugs are approved for marketing, they need to be approved for marketing after the idle period BT-AT ends.
4. The drug adverse event data management system based on big data according to claim 1, characterized in that: The specific method of comparing the multiple types of medications with the adverse drug event information database in the suspected analysis unit to determine whether the adverse drug event is a normal expected reaction or an abnormal expected reaction is: P1: First, establish a drug adverse event information database, denoted as Ks, record other drugs that have adverse reactions with the current drug in Ks, record the drug with the current adverse reaction as YA, and then record other drugs in multiple types of medication as Yn, where n is the serial number of different drugs; P2: At this time, Yn is compared with Ks. When Yn∈Ks, the adverse drug event is judged to be a normal expected reaction. At this time, the adverse drug event is judged to be an abnormal expected reaction.
5. The big data-based drug adverse event data management system according to claim 4, characterized in that: When recording other drugs that have adverse reactions with the current drug in Ks in P1, the name of the drug, the time when it cannot be taken, the adverse reaction status after taking it, and the corresponding emergency measures are specifically recorded.
6. The big data-based drug adverse event data management system according to claim 1, characterized in that: The specific method of analyzing the overlap of the formula information in the first table and the second table in the early warning prompt unit and issuing early warning prompts for some drug formulas is: M1: First, record the formula information in the first table as Kj, and record the formula information in the second table as Fj, where j is the serial number of the formula information. Then extract the part of Fj∈Kj and record it as Fv. At this time, Fv is the existing drug formula information closest to Kj. Then record the difference between Kj and Fv as QE, where QE is the drug formula for early warning.
7. The big data-based drug adverse event data management system according to claim 6, characterized in that: The formula information in the second table recorded in the M1 is recorded as Fj, and all of them are drugs that do not produce adverse reactions.
8. The big data-based drug adverse event data management system according to claim 1, characterized in that: It also includes a population analysis unit: it collects the ages of patients who experience adverse reactions, analyzes the degree of adverse reactions caused by drugs at different age stages, and records and stores the results of the analysis.
9. A method for managing adverse drug event data based on big data, characterized in that: The specific steps include: Step 1: Collect adverse information of behavioral users, and divide behavioral users into first behavioral users and second behavioral users. The first behavioral user is the first patient who has adverse reactions, and the second behavioral user is other patients who have adverse reactions. Analyze the adverse drug information based on the data of the behavioral users; Step 2: Differentiate the medication conditions of patients with adverse reactions into one type of medication and multiple types of medication, establish a drug adverse event information database, compare the multiple types of medication with the drug adverse event information database, and determine whether the drug adverse event is a normal expected reaction or an abnormal expected reaction; Step 3: Collect the first formula information of the first drug, generate a first table using the first formula information, collect the second formula information of the second drug, generate a second table using the second formula information, analyze the overlap of the formula information in the first table and the second table, and issue early warning prompts for some drug formulas; Step 4: Collect the ages of patients who experience adverse reactions, analyze the degree of adverse reactions caused by the drug at different age stages, and record and store the results of the analysis.