CN111582708A - Medical information detection method, system, electronic device and computer-readable storage medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a medical information detection method, a medical information detection system, an electronic device and a computer-readable storage medium. The medical information detection method comprises the following steps: collecting dialogue voice information of a doctor and a patient; acquiring medical information to be detected; analyzing the dialogue voice information to obtain a voice analysis result; and obtaining a detection result according to the voice analysis result and the medical information to be detected. By the method, the speech sound information is analyzed and the detection result is obtained by combining the medical information to be detected, so that the technical problem that objective medical information of a doctor cannot be accurately obtained in real time in the prior art is solved.
Description
Technical Field
The present disclosure relates to the field of intelligent conversations, and in particular, to a method, a system, an electronic device, and a computer-readable storage medium for detecting medical information.
Background
With the increasingly fierce market in the medical field, the quality of life of people is continuously improved, and the requirements of the public on the medical quality, the medical environment, the medical time and the efficiency are higher and higher. Always, the doctor-patient relationship is a hot problem in the medical field, and the doctor-patient contradiction problem is closely and inseparably connected with the doctor-patient relationship. In the middle, there are cases caused by problems of the patients themselves, but there are also cases caused by various factors such as doctor attitude, service quality, etc. In addition, in either case, if we can find early, advance prevention, we can also alleviate or even resolve many of the drastic conflicts. Therefore, a set of doctor service quality detection system is indispensable.
In the prior art, evaluation of doctors mainly depends on manual evaluation, evaluation of superior, colleagues and medical supervision departments and medical records of doctors basically form all doctor service quality evaluation standards. However, in the prior art, the human evaluation of superior, colleagues and related departments contains subjective factors, and no data and evidence provide objective support; the medical records of doctors can only record the results such as whether the doctor is cured and whether an accident occurs and negative feedback, even if the doctor is in place, few patients can leave positive feedback, so a large number of non-negative results contain a lot of positive information and are not mined; medical records and screening of cases and data statistics require a lot of effort, are costly, and do not have the capability of real-time feedback. Therefore, how to accurately acquire objective medical information of a doctor in real time becomes a technical problem to be solved urgently.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, an embodiment of the present disclosure provides a method for detecting medical information, including:
collecting dialogue voice information of a doctor and a patient;
acquiring medical information to be detected;
analyzing the dialogue voice information to obtain a voice analysis result;
and obtaining a detection result according to the voice analysis result and the medical information to be detected.
Further, the analyzing the dialog voice information to obtain a voice analysis result includes:
and recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result used for matching with the medical information.
Further, the recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result for matching with the medical information includes:
splitting the dialogue voice information into dialogue data which separates the doctor and the patient according to a preset voice recognition algorithm; wherein the session data is used for matching with the medical information.
Further, the dialogue data includes:
one or more of text information segmented by sentence and word, average speech rate, single sentence word count, and interval silence duration from the next sentence.
Further, the obtaining a detection result according to the voice analysis result and the medical information to be detected includes:
matching the medical information to be detected according to the voice analysis result;
and outputting the matched first medical information and the unmatched second medical information.
Further, the matching the medical information to be detected according to the voice parsing result includes:
acquiring a corresponding matching algorithm according to the medical information to be detected;
and judging whether the voice analysis result comprises information matched with the medical information needing to be detected or not by using the matching algorithm.
Further, the method further comprises:
calculating a matching value according to the first medical information and the second medical information; the matching value is used to evaluate the medical quality of the doctor.
Further, the medical information to be detected includes preset matching rules.
Further, the method further comprises:
acquiring image information of a doctor;
and obtaining a detection result according to the image information and the medical information to be detected.
In a second aspect, an embodiment of the present disclosure provides a medical information detection system, including:
the data memory is used for storing medical information to be detected;
the terminal equipment is used for collecting conversation voice information of a doctor and a patient;
the central control server is used for receiving the dialogue voice information sent by the terminal system; analyzing the dialogue voice information to obtain a voice analysis result; acquiring the medical information to be detected;
and the analysis server is used for obtaining a detection result according to the voice analysis result and the medical information needing to be detected.
In a third aspect, an embodiment of the present disclosure provides an apparatus for detecting medical information, including:
the conversation voice acquisition module is used for acquiring conversation voice information of doctors and patients;
the medical information acquisition module is used for acquiring medical information to be detected;
the voice analysis module is used for analyzing the dialogue voice information to obtain a voice analysis result;
and the detection result acquisition module is used for acquiring a detection result according to the voice analysis result and the medical information to be detected.
Further, the voice parsing module is further configured to:
and recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result used for matching with the medical information.
Further, the voice parsing module is further configured to:
splitting the dialogue voice information into dialogue data which separates the doctor and the patient according to a preset voice recognition algorithm; wherein the session data is used for matching with the medical information.
Further, the dialogue data includes: one or more of text information segmented by sentence and word, average speech rate, single sentence word count, and interval silence duration from the next sentence.
Further, the detection result obtaining module is further configured to:
matching the medical information to be detected according to the voice analysis result;
and outputting the matched first medical information and the unmatched second medical information.
Further, the detection result obtaining module is further configured to:
acquiring a corresponding matching algorithm according to the medical information to be detected;
and judging whether the voice analysis result comprises information matched with the medical information needing to be detected or not by using the matching algorithm.
Further, the medical information detection apparatus further includes:
the matching value calculating module is used for calculating a matching value according to the first medical information and the second medical information; the matching value is used to evaluate the medical quality of the doctor.
Further, the medical information to be detected includes preset matching rules.
Further, the medical information detection apparatus further includes:
the image acquisition module is used for acquiring image information of a doctor;
and the image detection result acquisition module is used for acquiring a detection result according to the image information and the medical information to be detected.
In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of detecting medical information of any one of the first aspect.
In a fifth aspect, the disclosed embodiment provides a non-transitory computer-readable storage medium, which is characterized by storing computer instructions for causing a computer to execute the method for detecting medical information in any one of the foregoing first aspects.
The embodiment of the disclosure discloses a medical information detection method, a medical information detection system, an electronic device and a computer-readable storage medium. The medical information detection method comprises the following steps: collecting dialogue voice information of a doctor and a patient; acquiring medical information to be detected; analyzing the dialogue voice information to obtain a voice analysis result; and obtaining a detection result according to the voice analysis result and the medical information to be detected. By the method, the speech sound information is analyzed and the detection result is obtained by combining the medical information to be detected, so that the technical problem that objective medical information of a doctor cannot be accurately obtained in real time in the prior art is solved.
The foregoing is a summary of the present disclosure, and for the purposes of promoting a clear understanding of the technical means of the present disclosure, the present disclosure may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a schematic view of an application scenario of an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a method for detecting medical information according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of step S204 of the method for detecting medical information according to the embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a medical information detection system provided by an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an embodiment of a medical information detection apparatus provided in an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Fig. 1 is a schematic view of an application scenario of the embodiment of the present disclosure. As shown in fig. 1, a patient 101 communicates with a doctor 102, and voices uttered by the patient 101 and the doctor 102 are received by a terminal device 103, typically, the terminal device 103 is a computer used by the doctor, and the terminal device 103 is connected with a voice recognition device 105 through a network 104, wherein the voice recognition device 105 may be a computer or an intelligent terminal; the network 104 on which the terminal device 103 communicates with the voice recognition device 105 may be a wireless network, such as a 5G network and a wifi network, or may be a wired network, such as an optical fiber network. In this application scenario, the patient 101 and the doctor 102 speak a voice, the terminal device 103 collects the voice and transmits the voice to the voice recognition device 105, and the voice recognition device 105 recognizes dialogue data of the user voice and detects medical information therein according to the dialogue data.
It will be appreciated that the speech recognition device 105 and the terminal device 103 may be arranged together, i.e. the terminal device 103 may incorporate speech recognition functionality, such that a user's speech input may be recognized directly in the terminal device 103.
Fig. 2 is a flowchart of an embodiment of a method for detecting medical information according to an embodiment of the present disclosure, where the method for detecting medical information according to this embodiment may be performed by a device for detecting medical information, where the device for detecting medical information may be implemented as software, or implemented as a combination of software and hardware, and the device for detecting medical information may be integrated in a certain device in a system for detecting medical information, such as a server for detecting medical information or a terminal device for detecting medical information. As shown in fig. 2, the method comprises the steps of:
step S201, collecting dialogue voice information of a doctor and a patient;
illustratively, in this step, the dialogue voices of the doctor and the patient are collected by the voice collecting device of the terminal device 103 and stored in the terminal device 103, and a dialogue voice file between the doctor and the patient is generated.
It can be understood that the collection can be triggered manually by the doctor, for example, a physical or virtual button is arranged on the terminal, and after the doctor clicks the button, the collection of the indoor conversation voice is started; or the acquisition may be triggered by a specific wake word, such as when the doctor says "hello" or the patient says "hello" triggering acquisition of a conversational voice in the room. Similarly, the ending of the acquisition may be triggered by the above-mentioned manual mode or by the wake-up word "bye", which is not described herein again.
Step S202, acquiring medical information to be detected;
illustratively, the medical information to be detected includes preset matching rules, wherein the matching rules include, but are not limited to: one or more of a business capability matching rule, a service specification matching rule, and a service attitude matching rule. The medical information to be detected at least comprises a preset matching rule. Illustratively, the matching rule includes: among the conversational speech information: the first sentence needs to include a greeting like "hello" or "hello"; the conversational speech message needs to include a "good, i'm understanding, | thank you, doctor, resolution of my question | … …", and similar patient affirmation doctor statements; the patient needs to have apology after waiting for a long time, for example, a mute time length threshold in the dialogue voice is set first, and if the fact that the mute time length exceeds the mute time length threshold is detected, the first sentence needs to include apology, no good meaning, and long waiting for you. For matching of fixed expressions in the above examples, matching may be performed by configuring keywords, different keywords being set for different matching rules.
It is to be understood that the matching rules are only examples, and the matching rules may be specifically set according to different medical departments, different levels of doctors, and other various scenarios to match the medical information to be detected.
It is understood that, before the step S202, the method further includes: all medical information is configured and stored in a database. In this step, all medical information is configured, where the all medical information is a set of medical information, and when the medical information is used, the medical information to be detected may be selected from the set of medical information, and at this time, the medical information to be detected may be acquired from the database by using the ID of the medical information to be detected; for example, for each department, a subset of medical information may be preset, where the subset of medical information includes an ID of the medical information that the department needs to detect, and after the dialog voice information is acquired, specific medical information in the subset of medical information is acquired directly through the ID of the medical information as the medical information that needs to be detected.
Step S203, analyzing the dialogue voice information to obtain a voice analysis result;
in this step, speech recognition is performed on the dialogue speech information to obtain a speech analysis result. Illustratively, after obtaining the voice dialog information, the voice dialog information is recognized through a voice recognition technique. Illustratively, the speech dialogue information is encoded and decoded by a speech recognition model, and then a text corresponding to the speech signal is output. Typically, the encoding process is a process of extracting features of the speech dialogue information, which may extract features of the speech signal, and typical speech signal features may be mel-frequency cepstrum coefficients and the like; typically, the decoding process is a process of decoding the encoded data and outputting a text by using an acoustic model and a language model, wherein the acoustic model is used for recognizing the speech in the speech signal feature, and the language model is used for recognizing the language of the speech so as to recognize the text corresponding to the semantic output of the speech.
Optionally, the step S203 includes: and recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result used for matching with the medical information. The preset speech recognition algorithm is an algorithm set for the medical information, and exemplarily, if the sounds of two users need to be distinguished in the medical information, the speech recognition algorithm needs to be capable of splitting a two-channel audio corresponding to the dialogue speech information; the medical information needs to detect the silence time between two sounds, the speech recognition algorithm needs to be able to perform speech endpoint detection, etc. In summary, the specific capabilities of the speech recognition algorithm need to be determined according to the needs of the medical information.
Illustratively, in the present disclosure, the speech recognition algorithm is capable of performing the following tasks: detecting the mute duration; detecting the voice frequency of the split double channels by the recording duration, thereby identifying whether the speaker is a doctor or a patient; performing word segmentation and sentence segmentation on the generated dialogue voice text, thereby calculating the average speed of speech, namely the speed of speech of a single sentence; two audios split from the two-channel audio share the same time stamp, and whether the call robbing occurs or not and the time length of the call robbing can be judged based on the time stamp. It can be understood that the speech recognition algorithm can also perform other tasks to adapt to the preset medical information, which is not described herein again.
Based on the voice recognition algorithm, the recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result used for matching with the medical information, and the method comprises the following steps: splitting the dialogue voice information into dialogue data which separates the doctor and the patient according to a preset voice recognition algorithm; wherein the session data is used for matching with the medical information. The dialogue data is the voice parsing result, and exemplarily includes: one or more of text information segmented by sentence and word, average speech rate, single sentence word count, and interval silence duration from the next sentence.
The text information is used for carrying out semantic recognition on the dialogue voice information subsequently; the average speech rate and the single sentence speech rate can represent the emotion of a doctor or a patient, and if the speech rate is higher, the emotion is more excited; the word number of the single sentence can represent the service attitude of the doctor, and the more words, the more detailed explanation of the doctor; the interval mute time from the next sentence can be combined with the characters in the next sentence to indicate whether the doctor expresses apology or not, and the like. The above-mentioned combinations of the session data or the session data may constitute various judgment logics for judging whether the session data includes the medical information to be detected.
And step S204, obtaining a detection result according to the voice analysis result and the medical information to be detected.
In this step, it is determined whether or not there is information matching the medical information to be detected in the dialogue data based on the dialogue data obtained in step S203 and the matching rule obtained in step S202.
Optionally, the step S204 includes:
step S301, matching the medical information to be detected according to the voice analysis result;
and step S302, outputting the matched first medical information and the unmatched second medical information.
Optionally, in step S301, a corresponding matching algorithm is obtained according to the medical information to be detected, and the matching algorithm is used to determine whether the voice parsing result includes information that matches the medical information to be detected; wherein the matching algorithm may be provided by a natural language processing algorithm, which may, for example, accomplish the following tasks: using a longest substring matching algorithm to achieve keyword matching; matching regular expressions; context repeated retrieval is realized through text retrieval; and calculating semantic similarity through a pre-trained semantic similarity model, and judging whether a sentence with a specific semantic appears. If the medical information to be detected contains a keyword to be matched, detecting whether the dialogue data contains the keyword by using a longest substring matching algorithm; by matching with a regular expression, it can be detected whether the dialog data includes a statement conforming to a regular expression, where the regular expression is an expression conforming to a certain pattern, for example, a certain keyword appears in a certain context scene. It can be understood that the natural language processing algorithm may also complete other tasks to adapt to the preset medical information, which is not described herein again.
When step S301 is executed, the medical information that needs to be detected may or may not be matched, and the matching result is output in step S302 regardless of whether the medical information that needs to be detected cannot be matched, the matched medical information is referred to as first medical information, and the unmatched medical information is referred to as second medical information.
After obtaining the first medical information and the second medical information, the medical information detection method may further include: calculating a matching value according to the first medical information and the second medical information; the matching value is used to evaluate the medical quality of the doctor. Illustratively, the matching value is calculated according to a matching preset value and an unmatching preset value of the medical information, and each medical information has a corresponding matching preset value and unmatching, for example, the matching preset value of the greeting is 1, and the unmatching preset value is-1; the default value of the apology is 2, and the unmatched default value is-2; the preset value of the speed of speech is 3, the unmatched preset value is-3, and if the corresponding medical information is matched, the matched preset value is obtained; if the corresponding medical information is not matched, the unmatched preset values are obtained, all the preset values corresponding to the medical information to be detected can be obtained through the step S204, the preset values are added to obtain the matching values of the dialogue data, and the matching values are exemplarily used for evaluating the medical quality of the doctor.
Further, the medical information detection method further includes: acquiring image information of a doctor; and obtaining a detection result according to the image information and the medical information to be detected. In this embodiment, the medical information to be detected further includes image rule information, such as that a doctor needs to wear a white gown, wear a hat and wear a mask. If the image information of the doctor detects information matched with the medical information, the image of the doctor can be judged to be in accordance with the rule in the medical information. It will be appreciated that preset values may also be obtained and added to the matching value calculation. The image information is not limited to wearing information, and may also be expression information, motion information, and the like, detection and matching of the image information are similar to the method for the voice, and the detected method, the detected object, and the medical information are all replaced by the image information, which is not described herein again.
Fig. 4 is a schematic diagram of a medical information detection system provided in an embodiment of the present disclosure. As shown in fig. 4, the medical information detection system includes: terminal equipment 401, a data storage 402, a central control server 403 and an analysis server 404; wherein the content of the first and second substances,
the data memory is used for storing medical information to be detected;
the terminal equipment is used for collecting conversation voice information of a doctor and a patient;
the central control server is used for receiving the dialogue voice information sent by the terminal system; analyzing the dialogue voice information to obtain a voice analysis result; acquiring the medical information to be detected;
and the analysis server is used for obtaining a detection result according to the voice analysis result and the medical information needing to be detected.
Illustratively, the operation flow of the medical information detection system is as follows:
all medical information to be detected is configured and stored in the data storage in advance, specifically, all medical information to be detected is stored in the data storage in a database form, and each medical information includes an ID in the database for uniquely identifying the medical information;
when a patient enters a consulting room, the terminal equipment of the doctor is triggered to collect conversation voice information between the doctor and the patient to generate a conversation voice file. When the recording is finished, the terminal device sends the dialogue voice information and the corresponding ID of the medical information needing to be detected to the central control server, where the ID may be a default ID of a department where the doctor is located, and the ID may include a plurality of IDs, which are a subset of a set formed by the IDs of all the medical information needing to be detected.
And after receiving the dialogue voice information, the central control server calls a corresponding voice recognition algorithm to split the dialogue voice information into dialogue data of doctors and dialogue data of patients, wherein the dialogue data can be in a text form. The dialogue data includes text information segmented by sentences and words, an average speech rate, a single sentence word count, and interval mute time from the next sentence. And the central control server acquires corresponding medical information from the database according to the received ID of the medical information needing to be detected. And then sending the dialogue data and the medical information to an analysis server.
The analysis service calls a corresponding natural language processing algorithm to detect the dialogue data according to the definition of each piece of medical information to be detected so as to determine which pieces of the medical information to be detected are detected in the dialogue data and which pieces of the medical information to be detected are not detected in the dialogue data.
Further, after the analysis server obtains the detection result, the analysis server feeds the detection result back to the central control server. And after receiving the detection result, the central control server is also used for calling a matching value calculation algorithm, calculating a matching value according to the detection result, and outputting the matching value and displaying corresponding information by the central control server after the calculation is finished.
Illustratively, by the method and the system, the service data of the doctor can be mined to obtain the data supporting and evaluating the service quality of the doctor, and objective bases are provided for further evaluating the service quality of the doctor.
Illustratively, in one embodiment, the medical information is information for judging the quality of service of a doctor, and may be preset information or information learned from a large amount of high-quality service information through deep learning. Typically, the medical information is a rule for evaluating doctor service quality, and examples of the rule are as follows: 1. the doctor has to wear a white gown to wear the mask, and the rule can identify whether the wearing meets the specification by acquiring the image of the doctor; 2. greetings, such as 'hello', 'hello' and the like, must be included in the first sentence of voice of the doctor, and whether the doctor speaks the greetings can be judged by configuring key words and using the key words to search the first sentence of the doctor; 3. whether the doctor solves the problem of the patient or not can configure a regular expression 'good, i understand' thank you 'and the doctor solves my problem | …' in the medical information so as to judge whether the words spoken by the patient include the sentences or not; 4. after the patient waits for a long time, apology must be spoken, and the rule needs to set a mute duration threshold first, then locate the first sentence after the mute exceeds the threshold through a speech recognition algorithm, and then judge whether apology is included in the voice of the doctor through configured keywords or a regular expression. It is to be understood that the above-mentioned rules are only examples and do not constitute a limitation to the present disclosure, and when determining the evaluation rule, the rule may be defined according to an actual scene to form medical information, and the rule may be detected through a logical combination of a speech recognition algorithm and a natural language processing algorithm.
The embodiment of the disclosure discloses a method for detecting medical information, wherein the method for detecting the medical information comprises the following steps: collecting dialogue voice information of a doctor and a patient; acquiring medical information to be detected; analyzing the dialogue voice information to obtain a voice analysis result; and obtaining a detection result according to the voice analysis result and the medical information to be detected. By the method, the speech sound information is analyzed and the detection result is obtained by combining the medical information to be detected, so that the technical problem that objective medical information of a doctor cannot be accurately obtained in real time in the prior art is solved.
In the above, although the steps in the above method embodiments are described in the above sequence, it should be clear to those skilled in the art that the steps in the embodiments of the present disclosure are not necessarily performed in the above sequence, and may also be performed in other sequences such as reverse, parallel, and cross, and further, on the basis of the above steps, other steps may also be added by those skilled in the art, and these obvious modifications or equivalents should also be included in the protection scope of the present disclosure, and are not described herein again.
Fig. 5 is a schematic structural diagram of an embodiment of a medical information detection apparatus provided in an embodiment of the present disclosure, and as shown in fig. 5, the apparatus 500 includes: a conversation voice acquisition module 501, a medical information acquisition module 502, a voice analysis module 503 and a detection result acquisition module 504. Wherein the content of the first and second substances,
a conversation voice collecting module 501, configured to collect conversation voice information of a doctor and a patient;
a medical information obtaining module 502, configured to obtain medical information to be detected;
the voice analysis module 503 is configured to analyze the dialog voice information to obtain a voice analysis result;
and a detection result obtaining module 504, configured to obtain a detection result according to the voice analysis result and the medical information that needs to be detected.
Further, the voice parsing module 503 is further configured to:
and recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result used for matching with the medical information.
Further, the voice parsing module 503 is further configured to:
splitting the dialogue voice information into dialogue data which separates the doctor and the patient according to a preset voice recognition algorithm; wherein the session data is used for matching with the medical information.
Further, the dialogue data includes: one or more of text information segmented by sentence and word, average speech rate, single sentence word count, and interval silence duration from the next sentence.
Further, the detection result obtaining module 504 is further configured to:
matching the medical information to be detected according to the voice analysis result;
and outputting the matched first medical information and the unmatched second medical information.
Further, the detection result obtaining module 504 is further configured to:
acquiring a corresponding matching algorithm according to the medical information to be detected;
and judging whether the voice analysis result comprises information matched with the medical information needing to be detected or not by using the matching algorithm.
Further, the medical information detection apparatus further includes:
the matching value calculating module is used for calculating a matching value according to the first medical information and the second medical information; the matching value is used to evaluate the medical quality of the doctor.
Further, the medical information to be detected includes preset matching rules.
Further, the medical information detection apparatus further includes:
the image acquisition module is used for acquiring image information of a doctor;
and the image detection result acquisition module is used for acquiring a detection result according to the image information and the medical information to be detected.
The apparatus shown in fig. 5 can perform the method of the embodiment shown in fig. 2-3, and the detailed description of this embodiment can refer to the related description of the embodiment shown in fig. 2-3. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 2 to fig. 5, and are not described herein again.
Referring now to FIG. 6, a block diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: collecting dialogue voice information of a doctor and a patient; acquiring medical information to be detected; analyzing the dialogue voice information to obtain a voice analysis result; and obtaining a detection result according to the voice analysis result and the medical information to be detected.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (13)
1. A method for detecting medical information, comprising:
collecting dialogue voice information of a doctor and a patient;
acquiring medical information to be detected;
analyzing the dialogue voice information to obtain a voice analysis result;
and obtaining a detection result according to the voice analysis result and the medical information to be detected.
2. The method for detecting medical information according to claim 1, wherein the parsing the dialogue voice information to obtain a voice parsing result comprises:
and recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice analysis result used for matching with the medical information.
3. The method for detecting medical information according to claim 2, wherein the recognizing the dialogue voice information according to a preset voice recognition algorithm to obtain at least one voice parsing result for matching with the medical information comprises:
splitting the dialogue voice information into dialogue data which separates the doctor and the patient according to a preset voice recognition algorithm; wherein the session data is used for matching with the medical information.
4. The method for detecting medical information according to claim 3, wherein the dialogue data includes:
one or more of text information segmented by sentence and word, average speech rate, single sentence word count, and interval silence duration from the next sentence.
5. The method for detecting medical information according to claim 1, wherein the obtaining a detection result according to the voice parsing result and the medical information to be detected comprises:
matching the medical information to be detected according to the voice analysis result;
and outputting the matched first medical information and the unmatched second medical information.
6. The method for detecting medical information according to claim 5, wherein the matching the medical information to be detected according to the voice parsing result comprises:
acquiring a corresponding matching algorithm according to the medical information to be detected;
and judging whether the voice analysis result comprises information matched with the medical information needing to be detected or not by using the matching algorithm.
7. The method for detecting medical information according to claim 5, wherein the method further comprises:
calculating a matching value according to the first medical information and the second medical information; the matching value is used to evaluate the medical quality of the doctor.
8. The method for detecting medical information according to claim 1, wherein the medical information to be detected includes a preset matching rule.
9. The method for detecting medical information according to claim 1, wherein the method further comprises:
acquiring image information of a doctor;
and obtaining a detection result according to the image information and the medical information to be detected.
10. A system for detecting medical information, comprising:
the data memory is used for storing medical information to be detected;
the terminal equipment is used for collecting conversation voice information of a doctor and a patient;
the central control server is used for receiving the dialogue voice information sent by the terminal system; analyzing the dialogue voice information to obtain a voice analysis result; acquiring the medical information to be detected;
and the analysis server is used for obtaining a detection result according to the voice analysis result and the medical information needing to be detected.
11. An apparatus for detecting medical information, comprising:
the conversation voice acquisition module is used for acquiring conversation voice information of doctors and patients;
the medical information acquisition module is used for acquiring medical information to be detected;
the voice analysis module is used for analyzing the dialogue voice information to obtain a voice analysis result;
and the detection result acquisition module is used for acquiring a detection result according to the voice analysis result and the medical information to be detected.
12. An electronic device, comprising:
a memory for storing computer readable instructions; and
a processor for executing the computer readable instructions such that the processor when executed implements the method for detecting medical information according to any one of claims 1-9.
13. A non-transitory computer-readable storage medium storing computer-readable instructions which, when executed by a computer, cause the computer to perform the method of detecting medical information of any one of claims 1-9.
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