RELATED APPLICATIONS

The present application claims the benefit of Chinese Patent Application No. 201710642037.8, filed on Jul. 31, 2017, the entire disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to the field of medical technology, and more particular to a data processing method, a medical term processing system and a medical diagnostic system.

BACKGROUND ART

A huge number of people go to see doctors each year in China, so that hospitals always operate under an excessive burden, which leaves an impression of low efficiency in medical service. If an artificial intelligence system can be used to improve the efficiency and automation degree of healthcare service, such a problem can be alleviated to some extent. However, the application of an artificial intelligence system to improve healthcare efficiency may usually come across a severe problem: due to lack of medical knowledge and language complexity, patients tend to make mistakes when describing their symptoms (such as descriptive errors, misuse of words, or purposeful or unintentional omission), so pure matching of written symptoms with medical terms may give rise to misdiagnosis. Here is a simple example. Medical literature often use the term “nasal obstruction” to describe a symptom of cold, whereas patients in reality often say “my nose is stuffed up” and “my nose is bunged up”. From this example, we can see a big difference between medical terms and colloquial expressions. Misdiagnosis will result from simple morphological matching of words or characters. Thus, if a relevant medical term can be provided according to patient's information, medical service efficiency can be greatly enhanced and misdiagnosis can be prevented.

SUMMARY

According to an aspect of the present disclosure, there is provided a data processing method, comprising: acquiring statement information inputted by a user; dividing the statement information into a plurality of information segments by means of a predetermined algorithm, the plurality of information segments comprising a first information segment; establishing a Gaussian distribution of each of the information segments; calculating the similarity between the first information segment and words in a database by means of a similarity model; and obtaining, from words in the database, at least one second information segment for describing the first information segment based on the calculated similarity.

In an embodiment, the dividing the statement information into a plurality of information segments by means of a predetermined algorithm comprises: dividing the statement information into a plurality of information segments according to part of speech by means of a natural language processing algorithm.

In an embodiment, the establishing a Gaussian distribution of each of the information segments comprises: expressing an initial Gaussian distribution of each of the information segments by a random initial vector and a random initial matrix, wherein the initial vector and the initial matrix are respectively an expected value vector and a covariance matrix of the initial Gaussian distribution; solving a covariance and an expected value of the Gaussian distribution of each of the information segments by minimizing a target function; and establishing the Gaussian distribution of each of the information segments based on the calculated covariance and expected value.

In an embodiment, the solving a covariance and an expected value of the Gaussian distribution of each of the information segments by minimizing a target function comprises: iteratively solving a target function by means of a random gradient descent algorithm so as to calculate a covariance and an expected value of the Gaussian distribution of each of the information segments, wherein the target function is expressed as follows:
L(w,cp,cn)=max(0,1−S1(w,cp)+S1(w,cn));

Wherein w is the information of an information segment, cpis an information segment in the context of w in a statement, cnis a word not in the context of w in a statement, the function S1(w,cn) is a similarity function between w and cn, S1(w,cp) is a similarity function between w and cp, and max( ) is a maximum function.

In an embodiment, the Gaussian distribution is a multi-dimensional Gaussian distribution that is expressed as N(uW, Σw), wherein Σwis a covariance of the Gaussian distribution of the information segment w, uwis an expectation of the Gaussian distribution of the information segment w.

In an embodiment, the iteratively solving a target function by means of a random gradient descent algorithm comprises iterating the following processes until a predetermined condition is met:

solving a partial derivative of the target function with respect to an expected value vector and a covariance matrix:

⁢∂L∂uw⁢⁢1=-∑w⁢⁢1-1⁢(uw⁢⁢1-w2)∂L∂∑w⁢⁢1=12⁢(∑w⁢⁢1-1⁢∑w⁢⁢2⁢∑w⁢⁢1-1⁢+(∑w⁢⁢1-1⁢(uw⁢⁢1-uw⁢⁢2))⁢(∑w⁢⁢1-1⁢(uw⁢⁢1-uw⁢⁢2))T-∑w⁢⁢1-1)

wherein Σ is a covariance of the Gaussian distribution of an information segment, u is an expectation of the Gaussian distribution of an information segment, and w1and w2are two information segments;

updating the expected value vector and the covariance matrix according to the partial derivatives.

In an embodiment, the calculating the similarity between the first information segment and words in a database by means of a similarity model comprises: establishing a similarity model according to the calculated first information segment, and the covariance and the expected value of the Gaussian distribution of the words in the database; and calculating the similarity between the first information segment and the words in the database.

In an embodiment, the similarity model is expressed as

S⁡(w1,w2)=⁢∫N⁡(x;uw1,∑w1)⁢log⁢N⁡(x;uw2,∑w2)N⁡(x;uw1,∑w1)⁢dx=⁢12⁢{tr⁡(∑w1-1⁢∑w2)+(uw1-uw2)T⁢∑w1-1⁢(uw1-uw2)-log⁢det⁡(∑w2)det⁡(∑w1)-d}

wherein, d is a constant, u is the expectation of the Gaussian distribution of an information segment, Σ is the covariance of the Gaussian distribution of an information segment, w1and w2are two information segments, and N is the multi-dimensional Gaussian distribution in the form of matrix of an information segment.

In an embodiment, the obtaining at least one second information segment for describing the first information segment comprises: sorting the words in the database according to the similarity between the words in the database and the first information segment so as to obtain a predetermined number of words having highest similarity; and using the predetermined number of words as the second information segment.

In an embodiment, the method is applied in a diagnostic system, and the database comprises at least one of medical literature, medical knowledge base and electronic medical records.

According to another aspect of the present disclosure, there is provided a medical term processing system using the above method, which is to be applied in a medical information processing system, the medical term processing system comprises: a database configured to store information in relation to medical term; a data processor configured to divide statement information inputted by a user into a plurality of information segments, the plurality of information segments comprising a first information segment; a Gaussian embedder configured to establish a Gaussian distribution of each of information segments; a similarity calculator configured to calculate the similarity between the first information segment and words in the database by means of a similarity model, the words in the database comprising the plurality of information segments; and an output module configured to obtain at least one second information segment for describing the first information segment according to the calculated result of the similarity calculator.

In an embodiment, the data processor is configured to divide the statement information into a plurality of information segments according to part of speech by means of a natural language processing algorithm.

In an embodiment, the Gaussian embedder is configured to express an initial Gaussian distribution of each of the information segments by a random initial vector and a random initial matrix, wherein the initial vector and the initial matrix are respectively an expected value vector and a covariance matrix of the initial Gaussian distribution, and the Gaussian embedder is configured to solve a covariance and an expected value of the Gaussian distribution of each of the information segments by minimizing a target function, and establish the Gaussian distribution of each of the information segments based on the calculated covariance and expected value.

In an embodiment, the similarity calculator is configured to establish a similarity model according to the calculated first information segment and the covariance and the expected value of the Gaussian distribution of the words in the database; calculate the similarity between the first information segment and the words in the database; and sort the words in the database according to the similarity between the words in the database and the first information segment.

In an embodiment, the output module is configured to output a predetermined number of words as the second information segment according to the order of the words in the database.

In an embodiment, the solving a covariance and an expected value of the Gaussian distribution of each of the information segments by minimizing a target function comprises: iteratively solving a target function by means of a random gradient descent algorithm so as to calculate a covariance and an expected value of the Gaussian distribution of each of the information segments, wherein the target function is expressed as follows:
L(w,cp,cn)=max(0,1−S1(w,cp)+S1(w,cn));

Wherein w is information of an information segment, cpis an information segment in the context of w in a statement, cnis a word not in the context of w in a statement, the function S1(w,cn) is a similarity function between w and cn, S1(w,cp) is a similarity function between w and cp, and max( ) is a maximum function.

In an embodiment, the iteratively solving a target function by means of a random gradient descent algorithm comprises iterating the following processes until a predetermined condition is met:

solving a partial derivative of the target function with respect to an expected value vector and a covariance matrix:

⁢∂L∂uw⁢⁢1=-∑w⁢⁢1-1⁢(uw⁢⁢1-w2)∂L∂∑w⁢⁢1=12⁢(∑w⁢⁢1-1⁢∑w⁢⁢2⁢∑w⁢⁢1-1⁢+(∑w⁢⁢1-1⁢(uw⁢⁢1-uw⁢⁢2))⁢(∑w⁢⁢1-1⁢(uw⁢⁢1-uw⁢⁢2))T-∑w⁢⁢1-1)

wherein Σ is a covariance of the Gaussian distribution of an information segment, u is an expectation of the Gaussian distribution of an information segment, and w1and w2are two information segments;

updating the expected value vector and the covariance matrix according to the partial derivatives.

In an embodiment, the similarity model is expressed as

S⁡(w1,w2)=⁢∫N⁡(x;uw1,∑w1)⁢log⁢N⁡(x;uw2,∑w2)N⁡(x;uw1,∑w1)⁢dx=⁢12⁢{tr⁡(∑w1-1⁢∑w2)+(uw1-uw2)T⁢∑w1-1⁢(uw1-uw2)-log⁢det⁡(∑w2)det⁡(∑w1)-d}

wherein, d is a constant, u is the expectation, Σ is the covariance, w1and w2are two information segments.

According to another aspect of the present disclosure, there is provided a medical diagnostic system comprising a medical term processing system as stated above and further comprising: a receiving apparatus for receiving the statement information on disease features inputted by a user. The medical diagnostic system divides the statement information into a plurality of information segments by means of the medical term processing system, the plurality of information segments comprising a first information segment, and outputs at least one second information segment for describing the first information segment.

According to another aspect of the present disclosure, there is provided a medical intelligent triage apparatus, comprising: one or more processors; and a memory storing computer executable instructions configured to perform the method as stated above when executed by the one or more processors.

According to another aspect of the present disclosure, there is provided a computer readable storage medium, comprising computer executable instructions that causes one or more processors to perform the method as stated above when executed by the one or more processors.

Some embodiments of the present disclosure can realize at least one of the following advantageous effects and/or other advantageous effects: some embodiments can conveniently output professional terms that are close to the inputted colloquial expressions, so as to eliminate the differences between professional terms and colloquial expressions for users' easy use. For instance, some embodiments can conveniently output medical terms that are close to the inputted colloquial expressions, so as to eliminate the differences between medical terms and colloquial expressions for users' easy use.

The Summary introduces some concepts of the present invention in a simplified form, which will be further described in the following Detailed Description. The Summary is intended neither to provide essential features or substantive features of the claimed subject matter, nor to limit the scope of protection of the subject matter. Furthermore, as described herein, other various features and advantages can also be incorporated into these technologies as required.

