CN111739600A - Information processing method and device, computer equipment and readable storage medium - Google Patents
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Abstract
The invention discloses an information processing method, an information processing device, computer equipment and a readable storage medium, wherein the method comprises the following steps: acquiring a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period; aiming at user disease information, converting each disease category information in the user disease information into a corresponding One-Hot code, and arranging the One-Hot codes of all the disease category information according to the illness time information in the user disease information to form an original disease matrix; performing dimensionality reduction processing on the original disease matrix to obtain a user disease matrix used for representing the user disease information; the invention can process the unstructured user disease information into the structured user disease matrix so as to better use the user disease information.
Description
Technical Field
The present invention relates to the field of computers, and in particular, to an information processing method, apparatus, computer device, and readable storage medium.
Background
In the application scenario of medical insurance, it is necessary to acquire the disease information of a user, and perform, for example: user classification, preset index prediction, health state evaluation and other operations so as to facilitate the subsequent work of insurance auditors; however, since the diseased information of the user is unstructured data information, it is not beneficial to use; therefore, how to convert the unstructured diseased information of the user into structured information convenient to use becomes a technical problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention aims to provide an information processing method, an information processing device, a computer device and a readable storage medium, which can process unstructured user disease information into a structured user disease matrix so as to better use the user disease information.
According to an aspect of the present invention, there is provided an information processing method, specifically including the steps of:
acquiring a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
aiming at user disease information, converting each disease category information in the user disease information into a corresponding One-Hot code, and arranging the One-Hot codes of all the disease category information according to the illness time information in the user disease information to form an original disease matrix;
and performing dimensionality reduction processing on the original disease matrix to obtain a user disease matrix for representing the user disease information.
Optionally, the converting, for One user disease information, each disease category information in the user disease information into a corresponding One-Hot code specifically includes:
aiming at one kind of disease category information, converting the disease category information into a corresponding disease category code by using an international disease classification table;
determining One-Hot codes uniquely corresponding to the disease type codes from a preset One-Hot code set; wherein, the One-Hot code is a vector with dimension of 1 xM, and M is the total amount of disease category information contained in the international disease classification table.
Optionally, the performing dimension reduction processing on the original disease matrix to obtain a user disease matrix for characterizing the user disease information specifically includes:
multiplying an N multiplied by M dimensional original disease matrix and an M multiplied by J dimensional first conversion matrix to obtain an N multiplied by J dimensional first result matrix, wherein N is the total quantity of disease category information in the user disease information, and J is a positive integer smaller than M;
multiplying the first result matrix of the NxJ dimension with the second conversion matrix of the JxK dimension to obtain a second result matrix of the NxK dimension, wherein K is a positive integer smaller than J;
and multiplying the second result matrix of the dimension N multiplied by K by a third conversion matrix of the dimension K multiplied by L to obtain a user disease matrix of the dimension N multiplied by L, wherein L is a positive integer smaller than K.
Optionally, the first transformation matrix, the second transformation matrix, and the third transformation matrix are obtained as follows:
acquiring historical sample data of a set amount; wherein the historical sample data comprises: all historical illness time information, historical disease category information and user health scores of the user in the set time period;
aiming at one historical sample data, converting all historical disease time information and historical disease category information in the historical sample data into corresponding reference disease matrixes;
training a preset machine learning algorithm by using the reference disease matrix of each historical sample data and the corresponding user health score to obtain a health evaluation model capable of calculating the health score according to the disease matrix; wherein the health assessment model comprises: a first conversion function for obtaining a first conversion matrix, a second conversion function for obtaining a second conversion matrix, a third conversion function for obtaining a third conversion matrix, and an evaluation function for obtaining a health score;
inputting an N x M dimensional original disease matrix into the first conversion function to obtain a first conversion matrix, inputting an N x J dimensional first result matrix into the second conversion function to obtain a second conversion matrix, and inputting an N x K dimensional second result matrix into the third conversion function to obtain a third conversion matrix.
Optionally, after performing dimension reduction processing on the original disease matrix to obtain a user disease matrix for characterizing the user disease information, the method further includes:
and inputting the user disease matrix into a pre-trained prediction model to obtain a prediction result of a preset index.
According to another aspect of the present invention, there is also provided an information processing apparatus, specifically including the following components:
the acquisition module is used for acquiring the user disease information with set quantity; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
the conversion module is used for converting each disease type information in the user disease information into a corresponding One-Hot code according to the user disease information, and arranging the One-Hot codes of all the disease type information according to the illness time information in the user disease information to form an original disease matrix;
and the dimension reduction module is used for performing dimension reduction processing on the original disease matrix to obtain a user disease matrix used for representing the user disease information.
Optionally, the conversion module is specifically configured to:
aiming at one kind of disease category information, converting the disease category information into a corresponding disease category code by using an international disease classification table;
determining One-Hot codes uniquely corresponding to the disease type codes from a preset One-Hot code set; wherein, the One-Hot code is a vector with dimension of 1 xM, and M is the total amount of disease category information contained in the international disease classification table.
Optionally, the dimension reduction module is specifically configured to:
multiplying an N multiplied by M dimensional original disease matrix and an M multiplied by J dimensional first conversion matrix to obtain an N multiplied by J dimensional first result matrix, wherein N is the total quantity of disease category information in the user disease information, and J is a positive integer smaller than M;
multiplying the first result matrix of the NxJ dimension with the second conversion matrix of the JxK dimension to obtain a second result matrix of the NxK dimension, wherein K is a positive integer smaller than J;
and multiplying the second result matrix of the dimension N multiplied by K by a third conversion matrix of the dimension K multiplied by L to obtain a user disease matrix of the dimension N multiplied by L, wherein L is a positive integer smaller than K.
According to another aspect of the present invention, there is also provided a computer device, specifically including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above information processing method when executing the program.
According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described information processing method.
According to the information processing method, the device, the computer equipment and the readable storage medium provided by the invention, firstly, the One-Hot coding of each disease is used for converting each user disease information into the multidimensional original disease matrix, and because the original disease matrix has larger dimensionality and is inconvenient for model use, in the embodiment, the original disease matrix is subjected to dimensionality reduction treatment, so that the user disease matrix with smaller dimensionality is obtained, the diseased information of the user can be reflected through the user disease matrix, and the unstructured user disease information is converted into the structured user disease matrix, so that the user disease matrix can be conveniently used for example: user classification, preset index prediction, health assessment and other subsequent processing operations.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is an alternative flow chart of an information processing method according to an embodiment;
fig. 2 is a schematic diagram of an optional program module of the information processing apparatus according to the second embodiment;
fig. 3 is a schematic diagram of an alternative hardware architecture of the computer device according to the third embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
An embodiment of the present invention provides an information processing method, as shown in fig. 1, the method specifically includes the following steps:
step S101: acquiring a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in the set time period.
Specifically, before step S101, the method further includes:
aiming at a target user, acquiring all case information of the target user in the set time period;
and identifying the suffering time information and the disease type information from each case information by using a preset natural language processing algorithm.
For example, for a case information, performing word segmentation processing on the case information by using a natural language processing algorithm, and performing part-of-speech analysis on each word after word segmentation processing to determine proper nouns and time-like words contained in the case information; since the disease category information is a medical proper noun and the disease duration information is a number formed in a certain format, the disease duration information and the disease category information included in the case information can be determined by part-of-speech analysis.
Step S102: aiming at One user disease information, converting each disease category information in the user disease information into a corresponding One-Hot code, and arranging the One-Hot codes of all the disease category information according to the illness time information in the user disease information to form an original disease matrix.
Specifically, the converting, for a user disease information, each disease category information in the user disease information into a corresponding One-Hot code includes:
step A1: aiming at one kind of disease category information, converting the disease category information into a corresponding disease category code by using an international disease classification table;
in the present embodiment, the first three digits in the international disease classification table ICD-10 are used to represent various types of disease information, for example, the disease category code corresponding to diabetes is E14, and the disease category code corresponding to hypertension is I10.
Step A2: determining One-Hot codes uniquely corresponding to the disease type codes from a preset One-Hot code set; wherein, the One-Hot code is a vector with dimension of 1 xM, and M is the total amount of disease category information contained in the international disease classification table.
Further, the method further comprises:
and setting a corresponding One-Hot code for each disease type code in the international disease classification table ICD-10 to obtain a One-Hot code set.
The One-Hot coding set comprises One-Hot codes which are uniquely corresponding to the codes of each disease category; for example, the international disease classification table ICD-10 includes 2100 diseases, and One-Hot encoding is performed on the 2100 diseases, respectively, to obtain a vector matrix of 1 × 2100 dimensions corresponding to each disease; the value of each dimension of the vector matrix can only be 0 or 1; if the disease E14 appears at the 3 rd position in the international disease classification table ICD-10, the vector matrix of E14 is a1 × 2100-dimensional vector matrix with the third dimension of 1 and the other dimensions of 0 (E14 ═ 0,0,1, 0., 0).
Step S103: and performing dimensionality reduction processing on the original disease matrix to obtain a user disease matrix for representing the user disease information.
Wherein the original disease matrix is an N x M dimensional matrix, and N is the total amount of disease category information in the user disease information;
the user disease matrix is an N × L dimensional matrix, and L is a positive integer less than N.
Specifically, step S103 includes:
step B1: multiplying an N multiplied by M dimensional original disease matrix and an M multiplied by J dimensional first conversion matrix to obtain an N multiplied by J dimensional first result matrix, wherein N is the total quantity of disease category information in the user disease information, and J is a positive integer smaller than M;
step B2: multiplying the first result matrix of the NxJ dimension with the second conversion matrix of the JxK dimension to obtain a second result matrix of the NxK dimension, wherein K is a positive integer smaller than J;
step B3: and multiplying the second result matrix of the dimension N multiplied by K by a third conversion matrix of the dimension K multiplied by L to obtain a user disease matrix of the dimension N multiplied by L, wherein L is a positive integer smaller than K.
Further, the first transformation matrix, the second transformation matrix and the third transformation matrix are obtained as follows:
step C1: acquiring historical sample data of a set amount; wherein the historical sample data comprises: all historical illness time information, historical disease category information and user health scores of the user in the set time period;
the user health score is a standard health score calculated in advance according to all the illness information of the user in a set time period, and the user health score can truly reflect the real health condition of each historical sample data.
Step C2: aiming at one historical sample data, converting all historical disease time information and historical disease category information in the historical sample data into corresponding reference disease matrixes;
specifically, a corresponding reference disease matrix is formed according to each historical sample data in the manner from step S102 to step S103. Wherein each reference disease matrix is an N × M dimensional matrix.
Step C3: training a preset machine learning algorithm by using the reference disease matrix of each historical sample data and the corresponding user health score to obtain a health evaluation model capable of calculating the health score according to the disease matrix; wherein the health assessment model comprises: a first conversion function for obtaining a first conversion matrix, a second conversion function for obtaining a second conversion matrix, a third conversion function for obtaining a third conversion matrix, and an evaluation function for obtaining a health score;
in the present embodiment, the first conversion function is used to obtain a first conversion matrix from the N × M-dimensional reference disease matrix, thereby obtaining an N × J-dimensional reference disease matrix from the N × M-dimensional reference disease matrix and the first conversion matrix; the second conversion function is used for obtaining a second conversion matrix according to the reference disease matrix of the NxJ dimension, so that the reference disease matrix of the NxK dimension is obtained according to the reference disease matrix of the NxJ dimension and the second conversion matrix; the third conversion function is used for obtaining a third conversion matrix according to the reference disease matrix of the NxK dimension, so that a reference disease matrix of the NxL dimension is obtained according to the reference disease matrix of the NxK dimension and the third conversion matrix; the evaluation function is used to calculate a health score from an N × L dimensional reference disease matrix.
Preferably, the first and second transfer functions are ReLU linear rectification functions and the third transfer function is a Softmax loss function.
In the embodiment, the health assessment model is trained through historical sample data, so that a model for assessing the health condition of a user can be obtained; when the health assessment model is used, dimension reduction processing is carried out on the reference disease matrix through the first conversion function, the second conversion function and the third conversion function respectively, and the reference disease matrix after dimension reduction is input to the assessment function to obtain the health score of the user. It should be noted that, in this embodiment, the health assessment model is trained to obtain the first conversion function, the second conversion function, and the third conversion function for subsequent use.
Step C4: inputting an N x M dimensional original disease matrix into the first conversion function to obtain a first conversion matrix, inputting an N x J dimensional first result matrix into the second conversion function to obtain a second conversion matrix, and inputting an N x K dimensional second result matrix into the third conversion function to obtain a third conversion matrix.
Further, after the dimension reduction processing is performed on the original disease matrix to obtain a user disease matrix for characterizing the user disease information, the method further includes:
and inputting the user disease matrix into a pre-trained prediction model to obtain a prediction result of a preset index.
For example, inputting the user disease matrix into a pre-trained expense prediction model to obtain an annual average medical expense prediction result; namely, the medical expense situation after the user is predicted according to the historical sick information of the user; wherein the expense prediction model is a prediction model trained based on a logistic regression algorithm; training a logistic regression algorithm through a training sample set to obtain a prediction model for predicting the annual average medical expense; the training sample set includes: and obtaining a reference disease matrix according to the historical disease information and the historical annual average medical expense.
In this embodiment, an original disease matrix of each user disease information is formed by One-Hot coding of each disease, and since the original disease matrix has a large dimension and is inconvenient to use, in this embodiment, dimension reduction processing is performed on the original disease matrix, so that a user disease matrix with a small dimension is obtained, and the disease information of a user can be reflected by the user disease matrix, so that unstructured user disease information is converted into a structured user disease matrix, thereby facilitating the use of the user disease matrix for example: user classification, preset index prediction, health assessment and other subsequent processing operations.
Example two
An embodiment of the present invention provides an information processing apparatus, as shown in fig. 2, the apparatus specifically includes the following components:
an obtaining module 201, configured to obtain a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
the conversion module 202 is configured to convert each piece of disease category information in the user disease information into a corresponding One-Hot code for One piece of user disease information, and arrange the One-Hot codes of all pieces of disease category information according to the illness time information in the user disease information to form an original disease matrix;
and the dimension reduction module 203 is configured to perform dimension reduction processing on the original disease matrix to obtain a user disease matrix used for representing the user disease information.
Specifically, the conversion module 202 is configured to:
aiming at one kind of disease category information, converting the disease category information into a corresponding disease category code by using an international disease classification table;
determining One-Hot codes uniquely corresponding to the disease type codes from a preset One-Hot code set; wherein, the One-Hot code is a vector with dimension of 1 xM, and M is the total amount of disease category information contained in the international disease classification table.
Further, the dimension reduction module 203 is configured to:
multiplying an N multiplied by M dimensional original disease matrix and an M multiplied by J dimensional first conversion matrix to obtain an N multiplied by J dimensional first result matrix, wherein N is the total quantity of disease category information in the user disease information, and J is a positive integer smaller than M;
multiplying the first result matrix of the NxJ dimension with the second conversion matrix of the JxK dimension to obtain a second result matrix of the NxK dimension, wherein K is a positive integer smaller than J;
and multiplying the second result matrix of the dimension N multiplied by K by a third conversion matrix of the dimension K multiplied by L to obtain a user disease matrix of the dimension N multiplied by L, wherein L is a positive integer smaller than K.
Further, the apparatus further comprises:
the processing module is used for acquiring historical sample data with set quantity; wherein the historical sample data comprises: all historical illness time information, historical disease category information and user health scores of the user in the set time period;
aiming at one historical sample data, converting all historical disease time information and historical disease category information in the historical sample data into corresponding reference disease matrixes;
training a preset machine learning algorithm by using the reference disease matrix of each historical sample data and the corresponding user health score to obtain a health evaluation model capable of calculating the health score according to the disease matrix; wherein the health assessment model comprises: a first conversion function for obtaining a first conversion matrix, a second conversion function for obtaining a second conversion matrix, a third conversion function for obtaining a third conversion matrix, and an evaluation function for obtaining a health score;
inputting an N x M dimensional original disease matrix into the first conversion function to obtain a first conversion matrix, inputting an N x J dimensional first result matrix into the second conversion function to obtain a second conversion matrix, and inputting an N x K dimensional second result matrix into the third conversion function to obtain a third conversion matrix.
Still further, the apparatus further comprises:
and the prediction module is used for inputting the user disease matrix into a pre-trained prediction model so as to obtain a prediction result of a preset index.
EXAMPLE III
The embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers) capable of executing programs, and the like. As shown in fig. 3, the computer device 30 of the present embodiment includes at least but is not limited to: a memory 301, a processor 302 communicatively coupled to each other via a system bus. It is noted that FIG. 3 only shows the computer device 30 having components 301 and 302, but it is understood that not all of the shown components are required and that more or fewer components may be implemented instead.
In this embodiment, the memory 301 (i.e., the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 301 may be an internal storage unit of the computer device 30, such as a hard disk or a memory of the computer device 30. In other embodiments, the memory 301 may also be an external storage device of the computer device 30, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 30. Of course, the memory 301 may also include both internal and external storage devices for the computer device 30. In this embodiment, the memory 301 is generally used for storing an operating system installed in the computer device 30 and various types of application software, such as a program code of the information processing apparatus of the second embodiment. In addition, the memory 301 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 302 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 302 generally serves to control the overall operation of the computer device 30.
Specifically, in the present embodiment, the processor 302 is configured to execute a program of an information processing method stored in the processor 302, and the program of the information processing method implements the following steps when executed:
acquiring a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
aiming at user disease information, converting each disease category information in the user disease information into a corresponding One-Hot code, and arranging the One-Hot codes of all the disease category information according to the illness time information in the user disease information to form an original disease matrix;
and performing dimensionality reduction processing on the original disease matrix to obtain a user disease matrix for representing the user disease information.
The specific embodiment process of the above method steps can be referred to in the first embodiment, and the detailed description of this embodiment is not repeated here.
Example four
The present embodiments also provide a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., having stored thereon a computer program that when executed by a processor implements the method steps of:
acquiring a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
aiming at user disease information, converting each disease category information in the user disease information into a corresponding One-Hot code, and arranging the One-Hot codes of all the disease category information according to the illness time information in the user disease information to form an original disease matrix;
and performing dimensionality reduction processing on the original disease matrix to obtain a user disease matrix for representing the user disease information.
The specific embodiment process of the above method steps can be referred to in the first embodiment, and the detailed description of this embodiment is not repeated here.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An information processing method, characterized in that the method comprises:
acquiring a set number of user disease information; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
aiming at user disease information, converting each disease category information in the user disease information into a corresponding One-Hot code, and arranging the One-Hot codes of all the disease category information according to the illness time information in the user disease information to form an original disease matrix;
and performing dimensionality reduction processing on the original disease matrix to obtain a user disease matrix for representing the user disease information.
2. The information processing method according to claim 1, wherein the converting, for One user disease information, each disease category information in the user disease information into a corresponding One-Hot code specifically includes:
aiming at one kind of disease category information, converting the disease category information into a corresponding disease category code by using an international disease classification table;
determining One-Hot codes uniquely corresponding to the disease type codes from a preset One-Hot code set; wherein, the One-Hot code is a vector with dimension of 1 xM, and M is the total amount of disease category information contained in the international disease classification table.
3. The information processing method according to claim 2, wherein the performing dimension reduction processing on the original disease matrix to obtain a user disease matrix for characterizing the user disease information specifically includes:
multiplying an N multiplied by M dimensional original disease matrix and an M multiplied by J dimensional first conversion matrix to obtain an N multiplied by J dimensional first result matrix, wherein N is the total quantity of disease category information in the user disease information, and J is a positive integer smaller than M;
multiplying the first result matrix of the NxJ dimension with the second conversion matrix of the JxK dimension to obtain a second result matrix of the NxK dimension, wherein K is a positive integer smaller than J;
and multiplying the second result matrix of the dimension N multiplied by K by a third conversion matrix of the dimension K multiplied by L to obtain a user disease matrix of the dimension N multiplied by L, wherein L is a positive integer smaller than K.
4. The information processing method according to claim 3, wherein the first conversion matrix, the second conversion matrix, and the third conversion matrix are acquired as follows:
acquiring historical sample data of a set amount; wherein the historical sample data comprises: all historical illness time information, historical disease category information and user health scores of the user in the set time period;
aiming at one historical sample data, converting all historical disease time information and historical disease category information in the historical sample data into corresponding reference disease matrixes;
training a preset machine learning algorithm by using the reference disease matrix of each historical sample data and the corresponding user health score to obtain a health evaluation model capable of calculating the health score according to the disease matrix; wherein the health assessment model comprises: a first conversion function for obtaining a first conversion matrix, a second conversion function for obtaining a second conversion matrix, a third conversion function for obtaining a third conversion matrix, and an evaluation function for obtaining a health score;
inputting an N x M dimensional original disease matrix into the first conversion function to obtain a first conversion matrix, inputting an N x J dimensional first result matrix into the second conversion function to obtain a second conversion matrix, and inputting an N x K dimensional second result matrix into the third conversion function to obtain a third conversion matrix.
5. The information processing method of claim 1, wherein after the dimension reduction processing is performed on the original disease matrix to obtain a user disease matrix for characterizing the user disease information, the method further comprises:
and inputting the user disease matrix into a pre-trained prediction model to obtain a prediction result of a preset index.
6. An information processing apparatus characterized in that the apparatus comprises:
the acquisition module is used for acquiring the user disease information with set quantity; wherein the user disease information comprises: all the illness time information and the illness type information of the user in a set time period;
the conversion module is used for converting each disease type information in the user disease information into a corresponding One-Hot code according to the user disease information, and arranging the One-Hot codes of all the disease type information according to the illness time information in the user disease information to form an original disease matrix;
and the dimension reduction module is used for performing dimension reduction processing on the original disease matrix to obtain a user disease matrix used for representing the user disease information.
7. The information processing apparatus according to claim 6, wherein the conversion module is specifically configured to:
aiming at one kind of disease category information, converting the disease category information into a corresponding disease category code by using an international disease classification table;
determining One-Hot codes uniquely corresponding to the disease type codes from a preset One-Hot code set; wherein, the One-Hot code is a vector with dimension of 1 xM, and M is the total amount of disease category information contained in the international disease classification table.
8. The information processing apparatus according to claim 7, wherein the dimension reduction module is specifically configured to:
multiplying an N multiplied by M dimensional original disease matrix and an M multiplied by J dimensional first conversion matrix to obtain an N multiplied by J dimensional first result matrix, wherein N is the total quantity of disease category information in the user disease information, and J is a positive integer smaller than M;
multiplying the first result matrix of the NxJ dimension with the second conversion matrix of the JxK dimension to obtain a second result matrix of the NxK dimension, wherein K is a positive integer smaller than J;
and multiplying the second result matrix of the dimension N multiplied by K by a third conversion matrix of the dimension K multiplied by L to obtain a user disease matrix of the dimension N multiplied by L, wherein L is a positive integer smaller than K.
9. A computer device, the computer device comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 5 are implemented when the processor executes the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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