CN112037908A - Aural vertigo diagnosis and treatment device and system and big data analysis platform - Google Patents
Aural vertigo diagnosis and treatment device and system and big data analysis platform Download PDFInfo
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Abstract
The invention provides an aural vertigo diagnosis and treatment device, a system and a big data analysis platform. The diagnosis and treatment device for aural vertigo comprises: the vertigo information acquisition module is used for acquiring vertigo information of a patient; the sickness probability acquisition module is connected with the vertigo information acquisition module and is used for processing the vertigo information of the patient by using a diagnosis model so as to acquire the sickness probability of the patient; the treatment scheme generation module is connected with the vertigo information acquisition module and the illness probability acquisition module and is used for processing the vertigo information of the patient and the illness probability of the patient by using a treatment scheme generation model so as to generate a treatment scheme of the patient; the diagnosis model and the treatment plan generation model are big data models constructed according to historical diagnosis and treatment data of a plurality of patients. The aural vertigo diagnosis and treatment device can acquire vertigo information of a patient and automatically generate a treatment scheme according to the vertigo information.
Description
Technical Field
The invention belongs to the field of computer-aided diagnosis, relates to a diagnosis and treatment device, and particularly relates to an aural vertigo diagnosis and treatment device, a system and a big data analysis platform.
Background
Aural vertigo is one of the common diseases in the department of otorhinolaryngology, has high incidence and great harm, is mainly characterized by rotary vertigo, and relates to a plurality of organs and systems such as a central nervous system, a sensory system, a motor system and the like. Aural vertigo is usually accompanied by functional impairment of vestibular organs of ears, even if correct medication is used in time, partial functions still cannot be completely recovered, and the balance dysfunction of patients is the biggest obstacle affecting the life quality of vertigo patients. The scientific rehabilitation training can effectively enable the patient to completely recover the normal life through function compensation, and is an effective auxiliary treatment means for the vertigo sequela.
However, the inventor finds that in practical application, the diagnosis of otogenic vertigo in clinic often needs imaging and neuroelectrophysiological examination for definite diagnosis, the treatment often needs cooperation of a plurality of specialized departments such as otolaryngology department, neurology department, geriatrics department, rehabilitation department, nursing department and the like, the treatment process is complex, and the requirement on the level of medical staff is high; especially for the primary hospitals, the primary hospitals are restricted by medical level and medical equipment, and the primary hospitals are difficult to make reasonable diagnosis and treatment for the aural vertigo of patients.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide an aural vertigo diagnosing and treating device, a system and a big data analysis platform, which are used to solve the problems of complicated diagnosing and treating process and high level requirement on medical staff in the existing aural vertigo treating scheme.
To achieve the above and other related objects, a first aspect of the present invention provides an aural vertigo diagnosing and treating device; the diagnosis and treatment device for aural vertigo comprises: the vertigo information acquisition module is used for acquiring vertigo information of a patient; the sickness probability acquisition module is connected with the vertigo information acquisition module and is used for processing the vertigo information of the patient by using a diagnosis model so as to acquire the sickness probability of the patient; the treatment scheme generation module is connected with the vertigo information acquisition module and the illness probability acquisition module and is used for processing the vertigo information of the patient and the illness probability of the patient by using a treatment scheme generation model so as to generate a treatment scheme of the patient; wherein the diagnosis model and the treatment plan generation model are big data models constructed according to historical clinical data of a plurality of patients.
In an embodiment of the first aspect, the diagnosis and treatment device for aural vertigo further includes: the vertigo questionnaire generating module is used for generating an aural vertigo questionnaire; the otogenic vertigo questionnaire is used for assisting the patient to input questionnaire information; the vertigo information acquisition module acquires vertigo information of the patient according to the questionnaire information.
In an embodiment of the first aspect, the questionnaire information is voice-based questionnaire information; the vertigo information obtaining module comprises: the voice recognition unit is used for converting the questionnaire information in the voice form into a character sequence; the natural language processing unit is connected with the voice recognition unit and used for performing natural language processing on the character sequence to acquire a key text in the character sequence; and the questionnaire matching unit is connected with the natural language processing unit and the vertigo questionnaire generating module and is used for matching the key texts with the aural vertigo questionnaire to obtain the vertigo information of the patient.
In an embodiment of the first aspect, the diagnosis and treatment device for aural vertigo further includes a vertigo detecting module; the vertigo detection module is connected with the vertigo information acquisition module and is used for carrying out vertigo detection on the patient so as to acquire an vertigo detection result of the patient; the vertigo detection result of the patient comprises a vestibular evoked myogenic potential, an acoustic immittance test result, a pure tone audiometric result, a video seismogram, an otolith inspection result, a head-flicking test result and/or a dynamic and static balance table test result; the vertigo information acquisition module acquires vertigo information of the patient according to the vertigo detection result of the patient.
In an embodiment of the first aspect, the diagnostic model and/or the treatment plan generating model is generated by a model building module, and the model building module comprises: the data acquisition unit is used for acquiring historical diagnosis and treatment data of the plurality of patients; the characteristic extraction unit is connected with the data acquisition unit and is used for extracting characteristic data from the historical diagnosis and treatment data of the patients; and the model training unit is connected with the characteristic extraction unit and used for training a big data model according to the characteristic data so as to obtain the diagnosis model and/or the treatment scheme generation model.
In an embodiment of the first aspect, the model training unit includes: the model training subunit is connected with the feature extraction unit and used for training at least 2 big data models according to the feature data to obtain at least 2 alternative models; and the model selecting subunit is connected with the model training subunit and used for selecting the diagnosis model from the alternative models according to the performance of each alternative model.
In an embodiment of the first aspect, the model building module is included in the diagnosis and treatment device for aural vertigo, and is connected to the prevalence probability acquiring module and the treatment plan generating module.
In an embodiment of the first aspect, the diagnosis and treatment device for aural vertigo further includes: and the visit suggestion generation module is connected with the treatment scheme generation module and is used for generating a visit suggestion according to the treatment scheme of the patient.
A second aspect of the invention provides a big data analysis platform; the big data analysis platform comprises: the storage module is used for storing historical diagnosis and treatment data of a plurality of patients; and the model building module is connected with the storage module and is used for carrying out big data analysis on the historical diagnosis and treatment data of the patients so as to obtain a diagnosis model and/or a treatment scheme generation model.
The third aspect of the invention provides an aural vertigo diagnosis and treatment system; the diagnosis and treatment system for aural vertigo comprises: the big data analysis platform of the second aspect of the invention is used for obtaining a diagnosis model and/or a treatment scheme generation model; the diagnosis and treatment device for aural vertigo of the first aspect of the invention is in communication connection with the big data analysis platform and is used for diagnosing and treating aural vertigo of a patient.
As described above, the technical solution of the diagnosis and treatment device, system and big data analysis platform for aural vertigo according to the present invention has the following beneficial effects:
the diagnosis and treatment device for aural vertigo can process vertigo information of a patient by utilizing two big data models, namely a diagnosis model and a treatment scheme generation model, so that the sickness probability and the treatment scheme of the patient are obtained. The process basically does not need manual participation, so the diagnosis of the aural vertigo is not limited by the level of medical staff and the diagnosis and treatment process is simple.
Drawings
Fig. 1 is a schematic structural diagram of an aural vertigo diagnosing and treating device according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an aural vertigo diagnosing and treating device according to another embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a vertigo information obtaining module in an embodiment of the diagnosis and treatment device for aural vertigo of the present invention.
Fig. 4 is a schematic structural diagram of a model building module in an embodiment of the diagnosis and treatment device for aural vertigo according to the present invention.
Fig. 5A is a flowchart illustrating a big data model training process performed by the diagnosis and treatment device for aural vertigo according to an embodiment of the present invention.
Fig. 5B is a flowchart illustrating a weight updating subroutine performed by the aural vertigo diagnosing apparatus according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of an aural vertigo diagnosing and treating system according to an embodiment of the present invention.
Description of the element reference numerals
1 aural vertigo diagnosis and treatment device
11 vertigo information acquisition module
111 speech recognition unit
112 natural language processing unit
113 questionnaire matching unit
12 ill probability acquisition module
13 treatment plan generating module
14 model building module
141 data acquisition unit
142 feature extraction unit
143 model training unit
15 vertigo questionnaire generating module
S41-43 step
S421 to S424 steps
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated. Moreover, in this document, relational terms such as "first," "second," and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The diagnosis of otogenic vertigo in clinic usually needs imaging and neuroelectrophysiological examination for definite diagnosis, the treatment usually needs the cooperation of a plurality of specialized departments such as otolaryngology, neurology, geriatrics, rehabilitation and nursing department, the treatment process is complex and the level requirement on medical staff is high; especially for the primary hospitals, the primary hospitals are restricted by medical level and medical equipment, and the primary hospitals are difficult to make reasonable diagnosis and treatment for the aural vertigo of patients. In view of the above problem, the present invention provides an aural vertigo diagnosis and treatment device, which can process vertigo information of a patient using two big data models, namely a diagnosis model and a treatment scheme generation model, to obtain an incidence of disease and a treatment scheme of the patient. The process basically does not need manual participation, so the diagnosis of the aural vertigo is not limited by the level of medical staff and the diagnosis and treatment process is simple.
Referring to fig. 1, in an embodiment of the present invention, the diagnosis and treatment device for aural vertigo 1 includes:
and the dizziness information acquisition module 11 is used for acquiring the dizziness information of the patient. Wherein the vertigo information of the patient comprises symptom information of the patient, basic information of the patient, laboratory test data of the patient, and the like. In particular, the symptom information is, for example, visual rotation, nausea, tinnitus, etc., the basic information is, for example, sex, age, place of residence, etc. of the patient, and the laboratory test data is, for example, blood test data.
And the sickness probability acquiring module 12 is connected with the vertigo information acquiring module 11 and is used for processing the vertigo information of the patient by using a diagnosis model so as to acquire the sickness probability of the patient. The diagnosis model is a big data model constructed according to historical diagnosis and treatment data of a plurality of patients, and the big data model is a classification model, a prediction model, a clustering model and the like. The patient refers to a patient having the same or similar vertigo information as the patient, and the plurality of patients may or may not include the patient. The probability of the patient suffering from aural vertigo may be the probability of suffering from aural vertigo, or the probability of suffering from 2 or more diseases including aural vertigo. In this embodiment, the sickness probability acquiring module 12 uses the vertigo information of the patient as an input of the diagnosis model, and an output of the diagnosis model is the sickness probability of the patient.
Particularly, for any disease, the probability of suffering from the disease of the patient can be any value between 0 and 1, and is used for quantitatively representing the possibility that the patient suffers from the disease; the probability of the patient suffering from the disease can be either 0 or 1, which is used for qualitatively indicating whether the patient possibly suffers from the disease.
And the treatment scheme generating module 13 is connected to the vertigo information acquiring module 11 and the illness probability acquiring module 12, and is configured to process the vertigo information of the patient and the illness probability of the patient by using a treatment scheme generating model to generate a treatment scheme of the patient. The treatment plan generation model is a big data model constructed according to historical diagnosis and treatment data of a plurality of patients, and the big data model is a correlation model, a regression model and the like. In this embodiment, the treatment plan generating module 13 takes the vertigo information of the patient and the probability of illness of the patient as the input of the treatment plan generating model, and the output of the treatment plan generating model is the treatment plan of the patient. The treatment regimen comprises a medication regimen, a visit regimen and/or lifestyle advice.
Wherein the diagnosis model and the treatment plan generation model are big data models constructed according to historical clinical data of a plurality of patients.
Specifically, when the diagnosis model is constructed, the historical diagnosis and treatment data of the patient comprise vertigo information of the patient and illness condition of the patient. Wherein the vertigo information of the patient comprises symptom information, basic information and/or laboratory test data and the like. The diseased condition of the patient includes the disease that the patient actually suffers from, which may be the disease that the patient eventually diagnosed after a hospital visit, which may be obtained from the patient's case.
When the diagnosis plan generation model is constructed, the historical diagnosis and treatment data of the patient comprise vertigo information of the patient, illness condition of the patient and treatment plan of the patient. Wherein the treatment plan of the patient refers to the treatment plan adopted by the patient for the disease suffered by the patient, and the treatment plan can be the treatment plan given by a doctor after the patient visits, and can be obtained from the patient case.
As can be seen from the above description, the diagnosis and treatment device for aural vertigo according to this embodiment can process vertigo information of a patient by using two big data models, namely, a diagnosis model and a treatment plan generation model, so as to obtain an incidence probability and a treatment plan of the patient. The process basically does not need manual participation, so the diagnosis of the aural vertigo is not limited by the level of medical staff and the diagnosis and treatment process is simple.
Unlike general diseases, the symptoms of aural vertigo diseases are often highly subjective, and vertigo information obtained by only objectively examining a patient is often not comprehensive enough, thereby affecting diagnosis of aural vertigo. To address this problem, referring to fig. 2, in an embodiment of the present invention, the aural vertigo treating device 1 further includes a vertigo questionnaire generating module 15. The vertigo questionnaire generating module 15 is used for generating an aural vertigo questionnaire; the otogenic vertigo questionnaire is used for assisting the patient to input questionnaire information; the vertigo information acquisition module acquires vertigo information of the patient according to the questionnaire information.
Specifically, the patient may obtain the aural vertigo questionnaire generated by the vertigo questionnaire generating module 15 through a web page, an applet, or the like, and input questionnaire information according to the aural vertigo questionnaire. The questionnaire information can be questionnaire information in a text form or questionnaire information in a voice form. The vertigo information acquiring module 11 acquires questionnaire information input by a patient, and acquires vertigo information of the patient according to the questionnaire information.
Preferably, the otogenic vertigo questionnaire comprises a vertigo symptom questionnaire, an onset machine questionnaire, a concomitant symptom questionnaire, a disease history questionnaire, and/or a medication history questionnaire.
Wherein the vertigo symptom questionnaire is used to assist the patient in entering symptoms at the onset of vertigo, the vertigo symptom questionnaire being, for example: whether the attack is the rotation of the heaven or the object, how long the attack duration is and whether the attack is repeated or not; the illness time machine questionnaire is used for assisting a patient to input illness time, and comprises the following steps: whether attack occurs when lying down or rising up quickly from a sitting or lying position, whether attack occurs after insomnia or poor rest, etc.; the accompanying symptom questionnaire is used to assist the patient in entering accompanying symptoms when the vertigo episode is present, such as: whether headache is accompanied during attack, whether fluctuating hearing is reduced during the course of disease, etc.; the disease history sub-questionnaire is used to assist the patient in entering a medical history and/or family genetic history, such as: whether the history of eye diseases exists or not, whether the direct relatives have the history of similar diseases or not, and the like; the medication history questionnaire is used to assist the patient in entering medication information, such as: ototoxic drugs have recently been used, sedatives have recently been used, and the like.
As can be seen from the above description, in the present embodiment, the vertigo questionnaire generating module can generate an aural vertigo questionnaire, and the patient can input questionnaire information according to the aural vertigo questionnaire. Wherein, the questionnaire information provides subjective information for patients, so that symptoms of aural vertigo diseases with strong subjectivity can be obtained.
Referring to fig. 3, in an embodiment of the present invention, the vertigo information obtaining module 11 includes:
and the voice recognition unit 111 is used for converting the questionnaire information in the voice form into a character sequence. Specifically, the voice recognition unit 111 preprocesses the questionnaire information in the form of voice, and converts each frame waveform into a multidimensional vector using a cepstrum coefficient (LPCC); then, the speech recognition unit 111 may convert the multidimensional vector into phoneme information using a deep learning acoustic model, match the factor information with a dictionary, and convert the factor information into a text sequence. Such as noise reduction, mute cut, sound framing, etc. For example, the speech recognition unit 111 may convert the multi-dimensional vectors into phoneme information using a deep learning acoustic model based on a long-short term memory artificial neural network (LSTM). The LSTM is composed of a forgetting gate, an input gate and an output gate to control the hidden output state of the cell state, and the LSTM mainly comprises the following steps when used for processing the multi-dimensional vector:
step 1, forgetting the door to forget some cell states. In particular, at time t, the forgetting gate receives a long-term memory Ct-1(output from previous cell module, i.e., cell state at previous time) and decide to retain and forget Ct-1By which part of C the process may be carried outt-1Multiplied by a forgetting factor ftTo realize that: f. oft×Ct-1＝σ(Wf,[ht-1,xt])×Ct-1. Wherein σ is Sigmoid function; wfIs the weight matrix of the forgetting gate, ht-1Representing the output of the network at the last moment, xtRepresents the output of the network at the current time, [ h ]t-1,xt]Denotes a reaction oft-1And xtThe concatenation is a vector.
Step 2, inputting the current information and updating the cell state by the input gate, wherein the updated cell state is
Wherein it＝σ(Wt,[ht-1,xt])，
Wherein, tanh WCAnd WtAlso the weight matrix of the forgetting gate.
And 3, determining hidden variable output. Output h of the latent variablet＝ot×tanh(Ct)＝σ(Wo，[ht-1,xt])×tanh(Ct)，WoIs another weight matrix. The weight matrix Wf、WC、WtAnd/or WoMay be set based on empirical values.
Preferably, the dictionary is a dictionary generated according to the aural vertigo questionnaire, and the dictionary contains words and phrases related to the questionnaire content and answers of the aural vertigo questionnaire. For example, if the questionnaire information includes "whether to rotate around the sky or rotate around the object at the time of onset", the dictionary may include "rotate around the sky", "rotate around the object", and related words thereof.
And the natural language processing unit 112 is connected to the speech recognition unit 111, and is configured to perform natural language processing on the word sequence to obtain a key text in the word sequence. The processing of the word sequence by the natural language processing unit 112 includes word segmentation, part of speech tagging, relationship analysis, and key text recognition, and the specific scheme can be implemented by the existing natural language processing technology.
A questionnaire matching unit 113, connected to the natural language processing unit 112 and the vertigo questionnaire generating module 15, configured to match the key texts with the aural vertigo questionnaire to obtain vertigo information of the patient. Wherein the matching comprises: matching the key texts with the questions, verifying and modifying the matching results by utilizing relational analysis, and obtaining the answer results of the patient to the questions in the questionnaire through the matching. For example, if the key text includes "skywarding", and the ear-derived vertigo questionnaire also includes "skywarding", the judgment result obtained after matching the key text and the question is defaulted to "yes"; thereafter, the relation analysis process will determine whether the word sequence contains "no occurrence of skywarding" and similar words, if yes, the determination result will be changed to "no", otherwise, the determination result will be maintained as "yes".
The present embodiment provides an implementation of obtaining vertigo information of a patient according to questionnaire information in a form of voice, thereby allowing the patient to complete the aural vertigo questionnaire by dictating or the like.
In an embodiment of the present invention, the diagnosis and treatment device for aural vertigo further includes a vertigo detecting module; the vertigo detection module is connected with the vertigo information acquisition module and is used for carrying out vertigo detection on the patient so as to acquire an vertigo detection result of the patient; the vertigo detection result of the patient comprises a vestibular evoked myogenic potential, an acoustic immittance test result, a pure tone audiometric result, a video seismogram, an otolith inspection result, a head-flicking test result and/or a dynamic and static balance table test result; the vertigo information acquisition module acquires vertigo information of the patient according to the vertigo detection result of the patient.
Specifically, the vestibular evoked myogenic potential is an evoked potential generated by stimulating otolith through bone conduction vibration or air conduction sound, and the functional states of otolith apparatus and nerve conduction thereof can be probed through the relative amplitude and latency of the evoked potential. The vestibular evoked myogenic potential can be recorded on the sternocleidomastoid muscle of the patient, and at the moment, the vestibular evoked myogenic potential can detect the functional states of the saccule and the anterior sublateral nerve. In addition, the vestibular evoked myogenic potential can also be recorded on the eye muscle, and at this time, the vestibular evoked myogenic potential can detect the functional states of the utricule and the nerves on the vestibule. According to the state of vestibular evoked myogenic potential of the patient, the auxiliary diagnosis of aural vertigo can be realized. For example, if the vestibular evoked myogenic potentials recorded on the sternocleidomastoid muscle of the patient are normal, the vestibular evoked myogenic potentials recorded on the eye muscle are abnormal, and the anterior and lateral semicircular canals of the patient are normal, it is indicated that the damage to the patient may be primarily to the supraventral nerve.
The acoustic immittance test results comprise a tympanogram of the patient, and the information related to the functions of the middle ear and the eustachian tube of the patient and the information related to the acoustic reflex path of the patient can be obtained through the acoustic immittance test results. The pure tone audiometry result can be obtained by carrying out pure tone audiometry on the patient and is used for reflecting the hearing level of the patient. The video seismogram can reflect central or vestibular lesions through saccades, starings, visual motor tracking, and/or hot and cold water tests. The otolith examination result is used to reflect the condition of otolith of a patient, wherein otolith is an important organ for controlling the balance of the human body, and normal otolith is in three semicircular canals. The dynamic and static balance table test results may be used to assess the vestibular system, proprioceptive system, and/or visual system of a patient.
The head swinging test result is used for evaluating whether vestibulo-ocular reflex at two sides of the testee is symmetrical, and further judging whether function of a single-side vestibule is reduced. Specifically, the patient is required to fixate his eyes in front of his eyes, look at the patient's nose, and, in the case where the patient cannot predict the swing direction of his head and the test start time, swing his head in a continuous and sudden manner to both sides at an angle of about 15 ° to 30 °. And after the swing is stopped, observing the eye shake condition of the testee to obtain the swing test result. Preferably, the vertigo detecting module can be realized by one or more intelligent diagnosis and treatment devices, and the intelligent diagnosis and treatment devices can finish the original project needing manual examination by a doctor by using artificial intelligence auxiliary devices, so that the workload of the doctor is reduced, and the examination efficiency and accuracy are improved. The detection result of the intelligent diagnosis and treatment equipment can be in forms of tables, numbers, images and/or characters and the like.
The vertigo information acquiring module may acquire vertigo information of the patient according to a vertigo detection result of the patient. At this time, the vertigo information includes an acoustic immittance test result, a pure tone audiometric result, a video seismogram, an otolith examination result, a head swing test result and/or a dynamic and static balance table test result of the patient. In addition, the vertigo information acquiring module may further acquire vertigo information of the patient based on questionnaire information of the patient and the vertigo detection result of the patient.
Referring to fig. 4, in one embodiment of the present invention, the diagnosis model and/or the treatment plan generation model are generated by a model construction module 14. The model building module 14 may be included in the diagnosis and treatment device for aural vertigo 1, and used as a functional module of the diagnosis and treatment device for aural vertigo 1; the model building module 14 may also be a functional module in another device or equipment other than the aural vertigo treating device 1. The model building module 14 comprises:
a data acquiring unit 141, configured to acquire historical clinical data of the multiple patients. Wherein, the historical diagnosis and treatment data of the patient comprises vertigo information of the patient and the sick condition of the patient.
And the feature extraction unit 142 is connected to the data acquisition unit 141 and is configured to extract feature data from the historical clinical data of the plurality of patients. In a specific application, the feature vectors obtained from the historical diagnosis and treatment data of the multiple patients generally belong to a high-dimensional space, the feature vectors of the high-dimensional space are mapped to a low-dimensional space to extract the feature data, and the mapping process can be regarded as mapping or transformation from a measurement space to the feature space. The implementation of mapping the feature vector from the high-dimensional space to the low-dimensional space can be implemented by using the existing scheme, which is not described herein again. In addition, the feature extraction unit 142 may further select one or more representative features from the feature vectors of the low-dimensional space as the feature data, and the selection process may be implemented by using the existing Wilcoxon test, Mann-Whitney U test, Shapiro-Wilk W test, and the like in a specific application.
And the model training unit 143 is connected to the feature extraction unit 142, and is configured to train a big data model according to the feature data to obtain the diagnosis model. The big data model is, for example, an Adaboost model, a decision tree model, a neural network model, a classifier model, or the like. Specifically, the model training unit 143 trains the big data model using the feature data as training data, and the stable big data model obtained after training is the diagnosis model. And after training is finished, the vertigo information of the patient is used as the input of the diagnosis model, and the output of the diagnosis model is the sick probability of the patient. The model training unit 143 may implement training of the big data model by using the prior art, which is not described herein again.
Next, an Adaboost model is taken as an example to introduce a training process of the big data model, wherein the basic principle of the Adaboost model is to reasonably combine a plurality of weak classifiers into a strong classifier, and the strong classifier is the diagnostic model. Referring to FIG. 5A, the training is describedThe process comprises the following steps: and S41, initializing weight distribution of the training data. The training data may be represented as (x)1,y1),(x2,y2),...,(xN,yN) Wherein x isiDenotes the ith training sample, yiE { -1,1} is a class label used to represent the ith training sample, i ═ 1, 2. The initial weight distribution of the data may be represented as D1＝(w1，1,w1，2,...,w1，N) (ii) a Wherein, w1,iRepresenting the initial weight value of the ith training sample. In the initial state, the weight of each training sample may be set to the same value, for example: 1/N, at this time, the initial weight distribution D of the data1＝(1/N,1/N,...,1/N)。
S42, a weight updating sub-process is repeatedly executed until the termination condition is satisfied. Referring to fig. 5B, the weight update sub-process includes:
s421, selecting a weak classifier as the t basic classifier H according to the error rate of the weak classifiertFor example: one weak classifier with the lowest error rate can be selected as the basic classifier Ht. Wherein t is a positive integer and its initial value is 1.
S422, calculating HtIn weight distribution DtThe specific calculation formula of the error is as follows:
wherein Ht(xi) To utilize basic classifier HtFor xiThe classification result of (2); w is at,iRepresents x when the weight updating sub-process is executed for the t timeiThe weight value of (1); i (H)t(xi)≠yi) To indicate the function, when Ht(xi)≠yiWhen it is 1; when H is presentt(xi)＝yiIts value is 0.
S423, calculating HtThe weight occupied in the final classifier is calculated by the formula:
s424, updating weight distribution D of training samplest+1＝(wt+1，1,wt+1，2,...,wt+1，N) (ii) a Wherein,
and is
Representing a normalization constant. Adding one to the value of T, wherein the termination condition is that the value of T reaches a threshold T, for example: when T is less than or equal to T, jumping to step S421; when T > T, step S43 is executed.
S43, combining the weak classifiers according to the weights of the weak classifiers to obtain the diagnosis model. Specifically, the classifier H (x) obtained after combination is the diagnosis model, and the combination mode is
Wherein sign represents a sign function; t is a threshold in the termination condition, which is also the number of weak classifiers.
In this embodiment, the model construction module 14 may also be used to construct a treatment plan generation model. Specifically, the historical diagnosis and treatment data of the patient acquired by the data acquisition unit 141 includes vertigo information of the patient, a disease condition of the patient, and a treatment plan of the patient. At this time, the feature extraction unit 142 obtains the feature data according to the historical clinical data of the plurality of patients, and the model training unit 143 trains a big data model according to the feature data to obtain the treatment plan generation model. In essence, the therapy plan generation module processes vertigo information and illness probability of a large number of patients according to the vertigo information, illness condition and big data of adopted therapy plans of the patients to generate corresponding therapy plans. The construction process of the treatment plan generation model is similar to that of the diagnosis model, and is not described herein again.
In an embodiment of the present invention, the model training unit includes a model training subunit and a model selecting subunit. The model training subunit is connected to the feature extraction unit and configured to train at least 2 big data models according to the feature data to obtain at least 2 candidate models. The model selecting subunit is connected with the model training subunit and is used for selecting the diagnosis model from the alternative models according to the performance of each alternative model.
In this embodiment, the 2 candidate models obtained by the model training subunit may be classifiers. At this time, the model selection subunit selects 1 classifier from the candidate models as the diagnosis model according to the classification performance of each classifier. Wherein the classification performance can be represented by a diagnosis rate and a misdiagnosis rate of the classifier; the diagnosis rate refers to the probability that the aural vertigo patients are diagnosed as aural vertigo, and the misdiagnosis rate refers to the probability that the non-aural vertigo patients are misdiagnosed as aural vertigo. Specifically, for any classifier in the candidate model, the model selection subunit obtains the diagnosis rate and the misdiagnosis rate of the classifier under a plurality of classification thresholds respectively, obtains a plurality of points corresponding to different classification thresholds by using the misdiagnosis rate as an abscissa and using the diagnosis rate as an ordinate, and can obtain a classification performance curve by connecting the plurality of points, thereby obtaining the area of the region enclosed by the classification performance curve and the abscissa. And the model selection subunit respectively acquires the classification performance curves corresponding to the classifiers and selects the diagnosis model according to the area of the region enclosed by the classification performance curves and the abscissa. For example, the classifier corresponding to the classification performance curve with the largest area may be selected as the diagnostic model. The classification threshold refers to a threshold adopted by the classifier when the classification is finished.
In an embodiment of the present invention, the model construction module is included in the diagnosis and treatment device for aural vertigo, and is connected to the prevalence probability obtaining module and the treatment plan generating module. Preferably, the aural vertigo diagnosis and treatment device further comprises a big data storage module, and the big data storage module stores a vertigo special disease database and other disease databases. Wherein the vertigo-specific database comprises historical diagnosis and treatment data of a large number of patients; the construction module can implement construction and/or training of the diagnosis model and the treatment plan generation model according to data in the vertigo special disease database.
In an embodiment of the present invention, the diagnosis and treatment device for aural vertigo further includes a diagnosis suggestion generation module. The visit suggestion generation module is connected with the treatment scheme generation module and is used for generating a visit suggestion according to the treatment scheme of the patient. Specifically, the clinic suggestion generation module can acquire the department selection suggestions of the hospital for different diseases and the specific doctor's visit conditions, and give out the clinic suggestions according to the patient's disease information and the disease probability; wherein the visit advice comprises advice of departments and doctors.
The invention also provides a big data analysis platform. Referring to fig. 6, in an embodiment of the present invention, the big data analysis platform includes a storage module and a model building module. The storage module stores historical diagnosis and treatment data of a plurality of patients; the model building module is connected with the storage module and used for carrying out big data analysis on the historical diagnosis and treatment data of the patients so as to obtain a diagnosis model and/or a treatment scheme generation model.
Based on the above description of the diagnosis and treatment device for aural vertigo and the big data analysis platform, the invention also provides a diagnosis and treatment system for aural vertigo. The aural vertigo diagnosis and treatment system comprises a big data analysis platform and an aural vertigo diagnosis and treatment device which is in communication connection with the big data analysis platform. The big data analysis platform is used for acquiring a diagnosis model and/or a treatment scheme generation model; the diagnosis and treatment device for the aural vertigo of the patient is used for diagnosing and treating the aural vertigo of the patient according to the diagnosis model and/or the treatment scheme generation model. It should be noted that the diagnosis and treatment model and/or the treatment plan generation model adopted by the diagnosis and treatment device for aural vertigo may be constructed by the big data analysis platform on the server, or may be constructed locally by the diagnosis and treatment device for aural vertigo.
The diagnosis and treatment device for aural vertigo can process vertigo information of a patient by utilizing two big data models, namely a diagnosis model and a treatment scheme generation model, so that the sickness probability and the treatment scheme of the patient are obtained. The process basically does not need manual participation, so the diagnosis of the aural vertigo is not limited by the level of medical staff and the diagnosis and treatment process is simple.
In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. An aural vertigo diagnosis and treatment device, comprising:
the vertigo information acquisition module is used for acquiring vertigo information of a patient;
the sickness probability acquisition module is connected with the vertigo information acquisition module and is used for processing the vertigo information of the patient by using a diagnosis model so as to acquire the sickness probability of the patient;
the treatment scheme generation module is connected with the vertigo information acquisition module and the illness probability acquisition module and is used for processing the vertigo information of the patient and the illness probability of the patient by using a treatment scheme generation model so as to generate a treatment scheme of the patient;
wherein the diagnosis model and the treatment plan generation model are big data models constructed according to historical clinical data of a plurality of patients.
2. The diagnosis and treatment device for aural vertigo according to claim 1, further comprising:
the vertigo questionnaire generating module is used for generating an aural vertigo questionnaire; the otogenic vertigo questionnaire is used for assisting the patient to input questionnaire information;
the vertigo information acquisition module acquires vertigo information of the patient according to the questionnaire information.
3. The aural vertigo diagnosis and treatment device according to claim 2, wherein said questionnaire information is voice-form questionnaire information; the vertigo information obtaining module comprises:
the voice recognition unit is used for converting the questionnaire information in the voice form into a character sequence;
the natural language processing unit is connected with the voice recognition unit and used for performing natural language processing on the character sequence to acquire a key text in the character sequence;
and the questionnaire matching unit is connected with the natural language processing unit and the vertigo questionnaire generating module and is used for matching the key texts with the aural vertigo questionnaire to obtain the vertigo information of the patient.
4. The aural vertigo diagnosis and treatment device according to claim 1, wherein: the aural vertigo diagnosis and treatment device further comprises a vertigo detection module;
the vertigo detection module is connected with the vertigo information acquisition module and is used for carrying out vertigo detection on the patient so as to acquire an vertigo detection result of the patient; the vertigo detection result of the patient comprises a vestibular evoked myogenic potential, an acoustic immittance test result, a pure tone audiometric result, a video seismogram, an otolith inspection result, a head-flicking test result and/or a dynamic and static balance table test result;
the vertigo information acquisition module acquires vertigo information of the patient according to the vertigo detection result of the patient.
5. The diagnosis and treatment device for aural vertigo according to claim 1, wherein said diagnosis model and/or said treatment plan generation model is generated by a model construction module, and said model construction module comprises:
the data acquisition unit is used for acquiring historical diagnosis and treatment data of the plurality of patients;
the characteristic extraction unit is connected with the data acquisition unit and is used for extracting characteristic data from the historical diagnosis and treatment data of the patients;
and the model training unit is connected with the characteristic extraction unit and used for training a big data model according to the characteristic data so as to obtain the diagnosis model and/or the treatment scheme generation model.
6. The diagnosis and treatment device for aural vertigo according to claim 5, wherein said model training unit comprises:
the model training subunit is connected with the feature extraction unit and used for training at least 2 big data models according to the feature data to obtain at least 2 alternative models;
and the model selecting subunit is connected with the model training subunit and used for selecting the diagnosis model from the alternative models according to the performance of each alternative model.
7. The aural vertigo diagnosis and treatment device according to claim 5, wherein: the model construction module is contained in the aural vertigo diagnosis and treatment device and is connected with the sickness probability acquisition module and the treatment scheme generation module.
8. The diagnosis and treatment device for aural vertigo according to claim 1, further comprising:
and the visit suggestion generation module is connected with the treatment scheme generation module and is used for generating a visit suggestion according to the treatment scheme of the patient.
9. A big data analytics platform, comprising:
the storage module is used for storing historical diagnosis and treatment data of a plurality of patients;
and the model building module is connected with the storage module and is used for carrying out big data analysis on the historical diagnosis and treatment data of the patients so as to obtain a diagnosis model and/or a treatment scheme generation model.
10. An aural vertigo therapy system, wherein the aural vertigo therapy system comprises:
the big data analytics platform of claim 9, for obtaining a diagnostic model and/or a treatment plan generation model;
the diagnosis and treatment device for aural vertigo of any one of claims 1 to 8, which is communicatively connected to said big data analysis platform and is used for diagnosing aural vertigo of the patient.
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