CN113870973A - Information output method, device, computer equipment and medium based on artificial intelligence - Google Patents
Information output method, device, computer equipment and medium based on artificial intelligence Download PDFInfo
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Abstract
The application is applicable to the technical field of artificial intelligence, and provides an information output method, an information output device, computer equipment and a medium based on artificial intelligence, wherein the method comprises the following steps: acquiring symptom information and a disease detection result of a patient, and performing disease identification on the symptom information and the disease detection result to obtain a disease of the patient; determining a first prescription according to the disease of the patient, and inquiring pathological information of the disease of the patient; carrying out pathological matching on the disease detection result according to the pathological information, and generating a second prescription according to the matching result of pathological diagnosis; acquiring a diagnosis record of a patient, and screening contraindicated medicines of the first prescription and the second prescription according to the diagnosis record; and generating prescription output information according to the first prescription and the second prescription after the tabu medicine is screened, and outputting the prescription output information. According to the method and the device, accurate prescription information can be automatically pushed to the patient and/or the doctor based on the symptom information and the disease detection result of the patient, and the efficiency and the accuracy rate of prescription making are improved.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to an artificial intelligence based information output method, apparatus, computer device, and medium.
Background
With the health care of people, the needs of patients with diseases are increasing. However, in medical institutions, disease diagnosis is mainly completed according to personal experience of doctors, and the number of doctors with abundant experience is too small to meet the large number of diagnosis requirements.
In the existing disease diagnosis process, doctors prescribe prescriptions according to the physical sign state, body feeling and other information of patients and by combining the experience of the doctors, so that the inquiry time of each patient is longer, and the prescription efficiency is reduced.
Disclosure of Invention
In view of this, the embodiments of the present application provide an information output method, apparatus, computer device and medium based on artificial intelligence, so as to solve the problem of low prescription efficiency in the current doctor inquiry process.
A first aspect of an embodiment of the present application provides an information output method based on artificial intelligence, including:
acquiring symptom information and a disease detection result of a patient, and performing disease identification on the symptom information and the disease detection result to obtain a patient disease;
determining a first prescription according to the patient diseases, and inquiring pathological information of the patient diseases, wherein the pathological information comprises sign information of the corresponding patient diseases under different disease degrees;
carrying out pathological matching on the disease detection result according to the pathological information, and generating a second prescription according to the matching result of the pathological diagnosis;
acquiring a diagnosis record of the patient, and screening contraindication medicines of the first prescription and the second prescription according to the diagnosis record;
generating prescription output information according to the first prescription and the second prescription after the tabu medicine is screened, and outputting the prescription output information.
Further, the determining a first prescription according to the patient disease and inquiring the pathological information of the patient disease comprises:
acquiring a disease number of the disease of the patient, and matching the disease number with a pre-stored prescription query table to obtain the first prescription, wherein the prescription query table stores corresponding relations between different disease numbers and corresponding fixed prescriptions;
and matching the disease number with a pre-stored pathology query table to obtain the pathology information, wherein the pathology query table stores corresponding relations between different disease numbers and corresponding pathology information.
Further, the obtaining a diagnosis record of the patient and performing contraindication drug screening on the first prescription and the second prescription according to the diagnosis record comprises:
inquiring contraindication medicines recorded in the diagnosis record, and respectively matching the contraindication medicines with medicines in the first prescription and the second prescription;
and if the taboo medicine is successfully matched with the medicine in the first prescription or the second prescription, deleting the matched medicine in the first prescription or the second prescription.
Further, the method further comprises:
inquiring an inquiry doctor according to the disease of the patient, and determining the inquiry state of the inquiry doctor;
if the inquiry state of the inquiry doctor is an idle state, establishing an inquiry session between the inquiry doctor and the patient, and sending the symptom information, the disease detection result, the patient disease and the prescription output information in the inquiry session.
Further, after determining the consulting state of the consulting doctor, the method further comprises the following steps:
if the inquiry state of the inquiry doctor is busy, inquiring the current inquiry session of the inquiry doctor, and sending the current inquiry session to the patient;
and if the patient joins the current inquiry session, sending the historical session record of the current inquiry session to the patient, and setting the patient to be in an unauthorized speaking state.
Further, the querying an inquiring doctor according to the patient disease and determining an inquiring state of the inquiring doctor comprises:
determining an inquiry clinic according to the disease number of the patient disease, and acquiring an inquiry image of the inquiry clinic;
and carrying out face recognition on the inquiry image, and determining the inquiry doctors and the inquiry states of the inquiry doctors according to the face recognition result.
Further, the disease identification of the symptom information and the disease detection result to obtain the disease of the patient includes:
extracting characteristic words from the symptom information to obtain disease characteristic words, and inquiring disease sign data according to the disease characteristic words;
and comparing the disease detection result with the disease sign data, and determining the disease of the patient according to the comparison result of the disease data.
A second aspect of an embodiment of the present application provides an information output apparatus, including:
the disease identification unit is used for acquiring symptom information and a disease detection result of a patient and carrying out disease identification on the symptom information and the disease detection result to obtain a patient disease;
the pathology inquiry unit is used for determining a first prescription according to the patient diseases and inquiring the pathology information of the patient diseases, wherein the pathology information comprises sign information of the corresponding patient diseases under different disease degrees;
the pathology matching unit is used for carrying out pathology matching on the disease detection result according to the pathology information and generating a second prescription according to the matching result of the pathology diagnosis;
the drug screening unit is used for acquiring the diagnosis record of the patient and screening contraindicated drugs for the first prescription and the second prescription according to the diagnosis record;
and the information output unit is used for generating prescription output information according to the first prescription and the second prescription after the tabu medicine is screened, and outputting the prescription output information.
A third aspect of the embodiments of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the computer device, where the processor implements the steps of the artificial intelligence based information output method provided in the first aspect when executing the computer program.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the artificial intelligence based information output method provided in the first aspect.
According to the information output method, the information output device, the computer equipment and the medium based on the artificial intelligence, the first prescription is determined according to the disease of the patient, the necessary prescription corresponding to the disease of the patient can be effectively obtained, pathological information of the disease of the patient is inquired, pathological diagnosis is carried out on a disease detection result according to the pathological information, an auxiliary prescription required by the current symptom of the patient can be effectively obtained, contraindication medicine screening is carried out on the first prescription and the second prescription through diagnosis records, the patient is prevented from taking contraindication medicines, accurate prescription information can be automatically pushed to the patient and/or a doctor, and the efficiency and the accuracy rate of prescription development are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart of an implementation of an artificial intelligence-based information output method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating an implementation of an artificial intelligence-based information output method according to another embodiment of the present application;
fig. 3 is a block diagram of an information output apparatus according to an embodiment of the present application;
fig. 4 is a block diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
In the embodiment of the application, the information output method based on the artificial intelligence is realized based on the artificial intelligence technology, and the automatically generated prescription output information can be output to a doctor, so that the doctor can conveniently diagnose a patient, and the prescription issuing efficiency is improved.
Referring to fig. 1, fig. 1 shows a flowchart of an implementation of an artificial intelligence based information output method provided in an embodiment of the present application, where the artificial intelligence based information output method is applied to any computer device, where the computer device may be a server, a mobile phone, a tablet, or a wearable intelligent device, and the artificial intelligence based information output method includes:
step S10, obtaining symptom information and disease detection results of the patient, and carrying out disease identification on the symptom information and the disease detection results to obtain the disease of the patient;
in this embodiment, the information output method based on artificial intelligence is applied to a cloud medical server, a patient can directly send symptom information and a disease detection result to the cloud medical server through a mobile phone during an inquiry waiting period, and the cloud medical server performs disease identification on the acquired symptom information and disease detection result so as to identify the patient disease of the patient based on the symptom information and disease detection result of the patient. Optionally, in this embodiment, the disease category of the patient may be very chronic disease.
Optionally, in this step, the symptom information and the disease detection result of the patient are input into the pre-trained disease recognition model to perform disease recognition, so as to obtain the disease of the patient, in this embodiment, the model training is performed on the disease recognition model according to the disease sample data until the disease recognition model converges, so as to obtain the pre-trained disease recognition model, where the disease sample data includes the correspondence between different patient diseases and the corresponding sample symptoms and the sample disease detection result, so that the pre-trained disease recognition model can effectively recognize the corresponding patient disease based on the input symptom information and the disease detection result.
Step S20, determining a first prescription according to the disease of the patient, and inquiring pathological information of the disease of the patient;
the pathological information includes sign information of corresponding patient diseases under different disease degrees, for example, the pathological information a1 includes sign information corresponding to the patient disease B1 under early, middle and late disease degrees, respectively, the sign information is used for determining indications of the severity and criticality of the patient, the sign information includes information such as heart rate, pulse, blood pressure, respiration, pain, blood oxygen, pupil and corneal reflex, and the first prescription is used for representing a basic prescription corresponding to the target very slow disease.
Optionally, in this step, the determining a first prescription according to the patient disease and querying pathological information of the patient disease includes:
acquiring a disease number of the disease of the patient, and matching the disease number with a prestored prescription query table to obtain the first prescription;
matching the disease number with a pre-stored pathology query table to obtain the pathology information;
in the step, the disease number is matched with a prestored prescription query table, so that a basic prescription corresponding to the disease of the patient can be effectively obtained.
Further, in this step, the disease identification of the symptom information and the disease detection result to obtain the disease of the patient includes:
extracting characteristic words from the symptom information to obtain disease characteristic words, and inquiring disease sign data according to the disease characteristic words;
the disease characteristic words are used for representing diseases related in the symptom information, and in the step, the characteristic words are extracted from the symptom information through a disease identification model, so that the disease characteristic words used for representing the symptom information can be effectively obtained;
comparing the disease detection result with the disease sign data, and determining the disease of the patient according to the comparison result of the disease data;
the disease detection result is compared with the physical sign standard data to judge whether abnormal data exist in the disease detection result, and the disease of the patient is determined according to the abnormal data in the disease detection result.
Step S30, carrying out pathological matching on the disease detection result according to the pathological information, and generating a second prescription according to the matching result of the pathological diagnosis;
the disease detection result is subjected to pathological diagnosis according to the pathological information so as to determine the current diseased state of the patient, and the corresponding auxiliary prescriptions in different diseased states are different, so that in the step, the second prescription is generated according to the matching result of the pathological diagnosis, and the accuracy of the output information of the follow-up prescription is improved.
Optionally, in this step, the disease state corresponding to the disease detection result is determined by matching the sign information stored in the disease detection result with the feature information stored in the pathological information.
Step S40, obtaining the diagnosis record of the patient, and screening contraindicated drugs for the first prescription and the second prescription according to the diagnosis record;
in the step, contra-drug screening is carried out on the first prescription and the second prescription through the diagnosis record, so that the accuracy of output information of the subsequent prescriptions is improved, and the contra-drug or the allergic drug is prevented from being taken by the patient;
optionally, in this step, the obtaining a diagnosis record of the patient and performing tabu drug screening on the first prescription and the second prescription according to the diagnosis record includes:
inquiring contraindication medicines recorded in the diagnosis record, and respectively matching the contraindication medicines with medicines in the first prescription and the second prescription;
if the taboo medicine is successfully matched with the medicine in the first prescription or the second prescription, deleting the matched medicine in the first prescription or the second prescription;
if the taboo medicine is successfully matched with the medicine in the first prescription or the second prescription, the taboo medicine which cannot be taken by the patient exists in the first prescription or the second prescription, and the accuracy of output information of the follow-up prescription is improved by deleting the matched medicine in the first prescription or the second prescription.
Optionally, in this step, if the taboo medicine is successfully matched with the medicine in the first prescription, the matched medicine in the first prescription is deleted, the associated medicine corresponding to the deleted medicine is queried, the related medicine and the corresponding deleted medicine have similar drug effects, the queried associated medicine is added to the first prescription, and the associated medicine is subjected to a replacement marking in the first prescription, where the replacement marking is used to remind a doctor that the associated medicine is a replacement medicine.
Step S50, generating prescription output information according to the first prescription and the second prescription after tabu medicine screening, and outputting the prescription output information;
the method comprises the steps of obtaining prescription output information by combining medicines in a first prescription and a second prescription after tabu medicines are screened, effectively providing prescription reference for a doctor to diagnose a patient by outputting the prescription output information, preferably, directly sending the prescription output information to the patient in the step, and achieving the effect of automatically pushing accurate prescription information to the patient.
In the step, the cloud medical server effectively facilitates the corresponding doctor to check the prescription output information by storing the prescription output information in the disease detection result of the patient.
In the embodiment, the first prescription is determined according to the disease of the patient, the necessary prescription corresponding to the disease of the patient can be effectively obtained, the pathological information of the disease of the patient is inquired, the pathological diagnosis is carried out on the disease detection result according to the pathological information, the auxiliary prescription required by the current symptom of the patient can be effectively obtained, the taboo medicine screening is carried out on the first prescription and the second prescription according to the diagnosis record, the patient is prevented from taking the taboo medicine, the accurate prescription information can be automatically pushed to the patient and/or a doctor, and the efficiency and the accuracy of prescription development are improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating an implementation of an artificial intelligence based information output method according to another embodiment of the present application. With respect to the embodiment of fig. 1, the artificial intelligence based information output method provided by this embodiment is used to further refine the steps after step S50 in the embodiment of fig. 1, and includes:
step S60, inquiring an inquiry doctor according to the patient disease, and determining the inquiry state of the inquiry doctor;
the method comprises the steps that the disease number of a patient disease is matched with a pre-stored doctor query table to obtain an inquiry doctor, the inquiry state of the inquiry doctor is determined by querying the use state of an account corresponding to the inquiry doctor, and the doctor query table stores corresponding relations between different disease numbers and corresponding inquiry doctors; in this step, the on-line diagnosis can be performed by the doctor for the patient.
Optionally, in this step, the image of the inquiry clinic corresponding to the inquiry doctor may be obtained, and face recognition may be performed based on the image of the inquiry clinic, so as to determine the inquiry state of the inquiry doctor.
Specifically, in this step, the querying an inquiring doctor according to the patient disease and determining an inquiring state of the inquiring doctor includes:
determining an inquiry clinic according to the disease number of the patient disease, and acquiring an inquiry image of the inquiry clinic;
carrying out face recognition on the inquiry image, and determining the inquiry doctors and the inquiry states of the inquiry doctors according to the face recognition result;
the method comprises the steps of determining a doctor and a patient in an inquiry image by carrying out face recognition on the inquiry image, determining the inquiry state of the inquiry doctor to be an idle state when detecting that the patient does not exist in the inquiry image, and determining the inquiry state of the inquiry doctor to be a busy state when detecting that the patient exists in the inquiry image.
Step S70, if the inquiry state of the inquiry doctor is idle, establishing an inquiry session between the inquiry doctor and the patient, and sending the symptom information, the disease detection result, the patient disease, and the prescription output information in the inquiry session;
if the inquiry state is the idle state, the inquiry session between the inquiry doctors and the patients is established, so that the communication between the patients and the inquiry doctors is facilitated, and the symptom information, the disease detection result, the target very slow disease and the prepared prescription are sent to the inquiry session, so that the data reference is provided for the inquiry operation of the doctors.
In the step, the inquiry session between the doctor and the patient can be established based on any group chat application, and the doctor can effectively and conveniently inquire the patient based on the inquiry session.
Step S80, if the inquiry state of the inquiry doctor is busy, inquiring the current inquiry session of the inquiry doctor, and sending the current inquiry session to the patient;
if the inquiry state is busy, the inquiry of the current inquiry state by the patient is facilitated by inquiring the current inquiry session of the inquiry doctor and sending the current inquiry session to the patient, and the patient can conveniently reply questions of the doctor in the diagnosis process.
Step S90, if the patient joins the current inquiry session, sending the history session record of the current inquiry session to the patient, and setting the patient to be in an unauthorized speaking state;
if the patient joins the current inquiry session, the historical session record of the current inquiry session is sent to the patient, so that the patient can effectively and conveniently know the inquiry flow of the doctor, and the patient is set to be in an unauthorized speaking state, so that the interference of the patient on the inquiry of the doctor in the current inquiry session is prevented.
It should be noted that, in the historical conversation record, the private information of the patient needs to be deleted to prevent the leakage of the private information of the patient, the private information includes the name of the patient, the age of the patient, the address of the patient, the diagnosis result of the patient, and the like, and the historical conversation record includes a communication record between the doctor and the corresponding patient, so as to facilitate the understanding of the inquiry flow by the patient based on the communication record.
In this embodiment, the inquiry state of the inquiry doctor is determined by querying the use state of the account corresponding to the inquiry doctor, if the inquiry state is an idle state, communication between the patient and the inquiry doctor is facilitated by establishing an inquiry session between the inquiry doctor and the patient, and the current inquiry session is sent to the patient by sending symptom information, a disease detection result, a patient disease and prescription output information to the inquiry session to provide data reference for the inquiry operation of the doctor, and if the inquiry state is a busy state, the current inquiry session of the inquiry doctor is queried, so that the inquiry of the current inquiry state by the patient is facilitated, and the patient has a reference effect on questions of the doctor in the diagnosis process.
Referring to fig. 3, fig. 3 is a block diagram of an information output apparatus 100 according to an embodiment of the present disclosure. In this embodiment, the information output apparatus 100 includes units for executing the steps in the embodiments corresponding to fig. 1 and 2. Please refer to fig. 1 and fig. 2 and the related descriptions in the embodiments corresponding to fig. 1 and fig. 2. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 3, the information output apparatus 100 includes: a disease recognition unit 10, a pathology inquiry unit 11, a pathology matching unit 12, a drug screening unit 13, and an information output unit 14, wherein:
and the disease identification unit 10 is used for acquiring symptom information and a disease detection result of the patient, and performing disease identification on the symptom information and the disease detection result to obtain the disease of the patient. In this embodiment, the disease recognition model is model-trained according to disease sample data until the disease recognition model converges to obtain the pre-trained disease recognition model, where the disease sample data includes a correspondence between different patient diseases and corresponding sample symptoms and sample disease detection results, so that the pre-trained disease recognition model can effectively recognize the corresponding patient diseases based on the input symptom information and disease detection results.
Wherein the disease recognition unit 10 is further configured to: extracting characteristic words from the symptom information to obtain disease characteristic words, and inquiring disease sign data according to the disease characteristic words;
and comparing the disease detection result with the disease sign data, and determining the disease of the patient according to the comparison result of the disease data.
The pathology query unit 11 is configured to determine a first prescription according to the patient disease, and query pathology information of the patient disease, where the pathology information includes sign information of the corresponding patient disease at different disease degrees, for example, the pathology information a1 includes sign information corresponding to the patient disease B1 at early, middle and late disease degrees, the sign information is used to determine indications of the severity and the criticality of the patient disease, the sign information includes information of heart rate, pulse, blood pressure, respiration, pain, blood oxygen, pupil, corneal reflex, and the like, and the first prescription is used to characterize a basic prescription corresponding to the target very slow disease.
Wherein, the pathology query unit 11 is further configured to: acquiring a disease number of the disease of the patient, and matching the disease number with a pre-stored prescription query table to obtain the first prescription, wherein the prescription query table stores corresponding relations between different disease numbers and corresponding fixed prescriptions;
and matching the disease number with a pre-stored prescription query table to obtain the pathological information, wherein the prescription query table stores the corresponding relationship between different disease numbers and corresponding fixed prescriptions, the disease numbers are used for representing the corresponding patient diseases, the disease numbers of different patient diseases are different, the pathological query table stores the corresponding relationship between different disease numbers and corresponding pathological information, and in the unit, the disease numbers are matched with the pre-stored prescription query table to effectively obtain the basic prescriptions corresponding to the patient diseases.
And the pathology matching unit 12 is used for carrying out pathology matching on the disease detection result according to the pathology information and generating a second prescription according to the matching result of the pathology diagnosis. The unit generates a second prescription according to the matching result of the pathological diagnosis, so that the accuracy of the output information of the follow-up prescription is improved.
And the drug screening unit 13 is configured to obtain a diagnosis record of the patient, and screen contraindicated drugs for the first prescription and the second prescription according to the diagnosis record. The unit is used for screening contraindicated medicines of the first prescription and the second prescription through the diagnosis record, so that the accuracy of output information of the follow-up prescriptions is improved, and the contraindicated medicines or the allergic medicines are prevented from being taken by the patient.
Wherein the drug screening unit 13 is further configured to: inquiring contraindication medicines recorded in the diagnosis record, and respectively matching the contraindication medicines with medicines in the first prescription and the second prescription;
and if the taboo medicine is successfully matched with the medicine in the first prescription or the second prescription, deleting the matched medicine in the first prescription or the second prescription. If the taboo medicine is successfully matched with the medicine in the first prescription or the second prescription, the taboo medicine which cannot be taken by the patient exists in the first prescription or the second prescription, and the accuracy of output information of the follow-up prescription is improved by deleting the matched medicine in the first prescription or the second prescription. Optionally, in the unit, if the taboo medicine is successfully matched with the medicine in the first prescription, the matched medicine in the first prescription is deleted, the associated medicine corresponding to the deleted medicine is inquired, the related medicine and the corresponding deleted medicine have similar drug effects, the inquired associated medicine is added into the first prescription, and the associated medicine is subjected to a replacement marking in the first prescription, wherein the replacement marking is used for reminding a doctor that the associated medicine is the replaced medicine.
And the information output unit 14 is used for generating prescription output information according to the first prescription and the second prescription after the tabu medicine is screened, and outputting the prescription output information. The output information of the prescription is obtained by combining the medicines in the first prescription and the second prescription after the tabu medicines are screened, and the prescription reference is effectively provided for the diagnosis of the patient by the doctor by outputting the output information of the prescription.
Wherein, the information output unit 14 is further configured to: inquiring an inquiry doctor according to the disease of the patient, and determining the inquiry state of the inquiry doctor;
if the inquiry state of the inquiry doctor is an idle state, establishing an inquiry session between the inquiry doctor and the patient, and sending the symptom information, the disease detection result, the patient disease and the prescription output information in the inquiry session.
The method comprises the steps that the disease number of a patient disease is matched with a pre-stored doctor query table to obtain an inquiry doctor, the inquiry state of the inquiry doctor is determined by querying the use state of an account corresponding to the inquiry doctor, and the doctor query table stores corresponding relations between different disease numbers and corresponding inquiry doctors; in this unit, the on-line diagnosis can be performed by the doctor for the patient.
Optionally, if the inquiry state is the idle state, by establishing an inquiry session between the inquiry doctors and the patients, communication between the patients and the inquiry doctors is facilitated, and by sending the symptom information, the disease detection result, the target very slow disease and the prepared prescription in the inquiry session, data reference is provided for the inquiry operation of the doctors.
Optionally, the information output unit 14 is further configured to: if the inquiry state of the inquiry doctor is busy, inquiring the current inquiry session of the inquiry doctor, and sending the current inquiry session to the patient;
and if the patient joins the current inquiry session, sending the historical session record of the current inquiry session to the patient, and setting the patient to be in an unauthorized speaking state.
If the patient joins the current inquiry session, the historical session record of the current inquiry session is sent to the patient, so that the patient can effectively and conveniently know the inquiry flow of the doctor, and the patient is set to be in an unauthorized speaking state, so that the interference of the patient on the inquiry of the doctor in the current inquiry session is prevented.
It should be noted that, in the historical conversation record, the private information of the patient needs to be deleted to prevent the leakage of the private information of the patient, the private information includes the name of the patient, the age of the patient, the address of the patient, the diagnosis result of the patient, and the like, and the historical conversation record includes a communication record between the doctor and the corresponding patient, so as to facilitate the understanding of the inquiry flow by the patient based on the communication record.
Further, the information output unit 14 is further configured to: determining an inquiry clinic according to the disease number of the patient disease, and acquiring an inquiry image of the inquiry clinic;
and carrying out face recognition on the inquiry image, and determining the inquiry doctors and the inquiry states of the inquiry doctors according to the face recognition result. The method comprises the steps of determining a doctor and a patient in an inquiry image by carrying out face recognition on the inquiry image, determining the inquiry state of the inquiry doctor to be an idle state when detecting that the patient does not exist in the inquiry image, and determining the inquiry state of the inquiry doctor to be a busy state when detecting that the patient exists in the inquiry image.
In the embodiment, the first prescription is determined according to the disease of the patient, the necessary prescription corresponding to the disease of the patient can be effectively obtained, the pathological information of the disease of the patient is inquired, the pathological diagnosis is carried out on the disease detection result according to the pathological information, the auxiliary prescription required by the current symptom of the patient can be effectively obtained, the taboo medicine screening is carried out on the first prescription and the second prescription according to the diagnosis record, the patient is prevented from taking the taboo medicine, the accurate prescription information can be automatically pushed to the patient and/or a doctor, and the efficiency and the accuracy of prescription development are improved.
Fig. 4 is a block diagram of a computer device 2 according to another embodiment of the present application. As shown in fig. 4, the computer device 2 of this embodiment includes: a processor 20, a memory 21 and a computer program 22, such as a program based on an artificial intelligence based information output method, stored in said memory 21 and executable on said processor 20. The processor 20, when executing the computer program 22, implements the steps of the above-described embodiments of artificial intelligence based information output methods, such as S10-S50 shown in fig. 1, or S60-S90 shown in fig. 2. Alternatively, when the processor 20 executes the computer program 22, the functions of the units in the embodiment corresponding to fig. 3, for example, the functions of the units 10 to 14 shown in fig. 3, are implemented, for which reference is specifically made to the relevant description in the embodiment corresponding to fig. 3, and details are not described here.
Illustratively, the computer program 22 may be divided into one or more units, which are stored in the memory 21 and executed by the processor 20 to accomplish the present application. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 22 in the computer device 2. For example, the computer program 22 may be divided into a disease identification unit 10, a pathology query unit 11, a pathology matching unit 12, a drug screening unit 13, and an information output unit 14, each of which functions as described above.
The computer device may include, but is not limited to, a processor 20, a memory 21. Those skilled in the art will appreciate that fig. 4 is merely an example of a computer device 2 and is not intended to limit the computer device 2 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the computer device may also include input output devices, network access devices, buses, etc.
The processor 20 may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. The memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device 2. Further, the memory 21 may also include both an internal storage unit and an external storage device of the computer device 2. The memory 21 is used for storing the computer program and other programs and data required by the computer device. The memory 21 may also be used to temporarily store data that has been output or is to be output.
In addition, functional units in the embodiments of the present application 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, and can also be realized in a form of a software functional unit.
The integrated module, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable storage medium may be non-volatile or volatile. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An information output method based on artificial intelligence is characterized by comprising the following steps:
acquiring symptom information and a disease detection result of a patient, and performing disease identification on the symptom information and the disease detection result to obtain a patient disease;
determining a first prescription according to the patient diseases, and inquiring pathological information of the patient diseases, wherein the pathological information comprises sign information of the corresponding patient diseases under different disease degrees;
carrying out pathological matching on the disease detection result according to the pathological information, and generating a second prescription according to the matching result of the pathological diagnosis;
acquiring a diagnosis record of the patient, and screening contraindication medicines of the first prescription and the second prescription according to the diagnosis record;
generating prescription output information according to the first prescription and the second prescription after the tabu medicine is screened, and outputting the prescription output information.
2. The method for outputting information based on artificial intelligence according to claim 1, wherein the determining a first prescription according to the disease of the patient and inquiring pathological information of the disease of the patient comprises:
acquiring a disease number of the disease of the patient, and matching the disease number with a pre-stored prescription query table to obtain the first prescription, wherein the prescription query table stores corresponding relations between different disease numbers and corresponding fixed prescriptions;
and matching the disease number with a pre-stored pathology query table to obtain the pathology information, wherein the pathology query table stores corresponding relations between different disease numbers and corresponding pathology information.
3. The artificial intelligence based information output method of claim 1, wherein the obtaining of the diagnosis record of the patient and the tabu drug screening of the first prescription and the second prescription according to the diagnosis record comprises:
inquiring contraindication medicines recorded in the diagnosis record, and respectively matching the contraindication medicines with medicines in the first prescription and the second prescription;
and if the taboo medicine is successfully matched with the medicine in the first prescription or the second prescription, deleting the matched medicine in the first prescription or the second prescription.
4. The artificial intelligence based information output method of claim 1, further comprising:
inquiring an inquiry doctor according to the disease of the patient, and determining the inquiry state of the inquiry doctor;
if the inquiry state of the inquiry doctor is an idle state, establishing an inquiry session between the inquiry doctor and the patient, and sending the symptom information, the disease detection result, the patient disease and the prescription output information in the inquiry session.
5. The artificial intelligence based information output method of claim 4, wherein after determining the consulting status of the consulting doctor, further comprising:
if the inquiry state of the inquiry doctor is busy, inquiring the current inquiry session of the inquiry doctor, and sending the current inquiry session to the patient;
and if the patient joins the current inquiry session, sending the historical session record of the current inquiry session to the patient, and setting the patient to be in an unauthorized speaking state.
6. The artificial intelligence based information output method of claim 4, wherein said querying an inquiring physician according to the patient's disease and determining the inquiring status of the inquiring physician comprises:
determining an inquiry clinic according to the disease number of the patient disease, and acquiring an inquiry image of the inquiry clinic;
and carrying out face recognition on the inquiry image, and determining the inquiry doctors and the inquiry states of the inquiry doctors according to the face recognition result.
7. The artificial intelligence based information output method according to any one of claims 1 to 6, wherein the performing disease recognition on the symptom information and the disease detection result to obtain the disease of the patient includes:
extracting characteristic words from the symptom information to obtain disease characteristic words, and inquiring disease sign data according to the disease characteristic words;
and comparing the disease detection result with the disease sign data, and determining the disease of the patient according to the comparison result of the disease data.
8. An information output apparatus based on artificial intelligence, comprising:
the disease identification unit is used for acquiring symptom information and a disease detection result of a patient and carrying out disease identification on the symptom information and the disease detection result to obtain a patient disease;
the pathology inquiry unit is used for determining a first prescription according to the patient diseases and inquiring the pathology information of the patient diseases, wherein the pathology information comprises sign information of the corresponding patient diseases under different disease degrees;
the pathology matching unit is used for carrying out pathology matching on the disease detection result according to the pathology information and generating a second prescription according to the matching result of the pathology diagnosis;
the drug screening unit is used for acquiring the diagnosis record of the patient and screening contraindicated drugs for the first prescription and the second prescription according to the diagnosis record;
and the information output unit is used for generating prescription output information according to the first prescription and the second prescription after the tabu medicine is screened, and outputting the prescription output information.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114420242A (en) * | 2022-01-22 | 2022-04-29 | 平安国际智慧城市科技股份有限公司 | Medical auxiliary information display method, system, equipment and medium based on voice input |
| CN117151655A (en) * | 2023-10-30 | 2023-12-01 | 天津市智恩博莱生物科技有限公司 | Comprehensive analysis management system and method for multiple project detection results |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109935290A (en) * | 2019-03-20 | 2019-06-25 | 杭州卓健信息科技有限公司 | A kind of drug matching system and its matching process based on big data analysis |
| CN112164440A (en) * | 2020-06-18 | 2021-01-01 | 丁玉 | Artificial intelligence medicine prescription system, medicine prescription method and artificial intelligence pharmacy |
| CN112289401A (en) * | 2020-11-10 | 2021-01-29 | 桃江县缘湘聚文化传媒有限责任公司 | Method and storage medium for intelligently making prescription based on big data |
-
2021
- 2021-09-27 CN CN202111134644.6A patent/CN113870973A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109935290A (en) * | 2019-03-20 | 2019-06-25 | 杭州卓健信息科技有限公司 | A kind of drug matching system and its matching process based on big data analysis |
| CN112164440A (en) * | 2020-06-18 | 2021-01-01 | 丁玉 | Artificial intelligence medicine prescription system, medicine prescription method and artificial intelligence pharmacy |
| CN112289401A (en) * | 2020-11-10 | 2021-01-29 | 桃江县缘湘聚文化传媒有限责任公司 | Method and storage medium for intelligently making prescription based on big data |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114420242A (en) * | 2022-01-22 | 2022-04-29 | 平安国际智慧城市科技股份有限公司 | Medical auxiliary information display method, system, equipment and medium based on voice input |
| CN117151655A (en) * | 2023-10-30 | 2023-12-01 | 天津市智恩博莱生物科技有限公司 | Comprehensive analysis management system and method for multiple project detection results |
| CN117151655B (en) * | 2023-10-30 | 2024-01-23 | 天津市智恩博莱生物科技有限公司 | Comprehensive analysis management system and method for multiple project detection results |
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