Method for structuring unstructured data using artificial intelligence

Abstract

A method performed by a computing device according to some embodiments of the present disclosure comprises the steps of: acquiring first text data including unstructured data extracted from a medical record database; acquiring summary information about the first text data from the first text data by inputting the first text data and a predetermined first prompt to a first summary information generation model based on artificial intelligence; and using regular expressions or a pre-trained information classification model based on artificial intelligence to acquire, from the summary information about the first text data, a dataset in which one or more pieces of text included in the summary information about the first text data are classified into predetermined categories, wherein the dataset has a structured format including columns composed of the predetermined categories and rows composed of the one or more pieces of text corresponding to the respective columns.

Description

A method for formalizing unstructured data using artificial intelligence

[0001] The present disclosure relates to a technology for processing data, and more specifically, to a method for using artificial intelligence technology to format unstructured data and store the formatted data in a database.

[0002] Currently, medical records contain diverse information, encompassing procedures, treatments, prescriptions, and questionnaires. Furthermore, the same medical terminology is expressed differently across hospitals and medical professionals. Consequently, the conversion rate of medical records into standardized data is significantly lower when conducting clinical trials or retrospective studies utilizing a Clinical Data Warehouse (CDW).

[0003] Therefore, in order to smoothly use medical record information when conducting retrospective studies, research is required to standardize medical record information, which is unstructured data.

[0004] Korean Patent Publication No. 10-2006-0062680 (published on June 12, 2006) discloses a method for converting medical data.

[0005] The present disclosure is conceived in response to the aforementioned background technology, and aims to provide a method for using artificial intelligence technology to standardize unstructured data and store the standardized data in a database.

[0006] The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0007] According to some embodiments of the present disclosure for solving the above-described problem, a method performed on a computing device may include: obtaining first text data including unstructured data extracted from a medical record database; obtaining summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model; and obtaining a dataset in which at least one text included in the summary information of the first text data is classified by predetermined item from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or a regular expression, wherein the dataset is formed in a structured format including columns composed of the predetermined items and rows composed of the at least one text corresponding to each column.

[0008] Alternatively, the predetermined first prompt may include a request sentence for requesting the first summary information generation model to output summary information of the first text data.

[0009] Alternatively, the first summary information generation model can be trained to generate summary information for input text data using second text data including unstructured data extracted from a medical record database, summary information of the second text data, and the predetermined first prompt.

[0010] Alternatively, the first summary information generation model may be trained in a direction in which the first output information becomes similar to the summary information of the second text data by comparing the first output information output by inputting the second text data and the predetermined first prompt with the summary information of the second text data.

[0011] Alternatively, the summary information of the second text data may be output by the pre-trained second summary information generation model in response to inputting the second text data and the predetermined first prompt into the artificial intelligence-based pre-trained second summary information generation model.

[0012] Alternatively, the pre-trained second summary information generation model may be pre-trained to output summary information including text corresponding to the predetermined item from the third text data using third text data including unstructured data extracted from a medical record database and the predetermined first prompt.

[0013] Alternatively, the summary information of the text data output by the second summary information generation model in response to inputting text data and a prompt to the second summary information generation model can be used as ground truth data in the learning process of the first summary information generation model.

[0014] Alternatively, the predetermined items may include personal information including at least one of age or gender; physical condition information related to the body; and disease condition information related to a disease.

[0015] Alternatively, the pre-trained information classification model may include a natural language processing model that is pre-trained to process natural language contained in text data to classify the text into predetermined categories.

[0016] Alternatively, the step of obtaining the dataset may include: determining whether a pattern exists in the summary information of the first text data; and obtaining the dataset by applying a different methodology for obtaining the dataset based on whether the pattern exists.

[0017] Alternatively, the step of acquiring the dataset may include, if a pattern exists in the summary information of the first text data, a step of acquiring the dataset in which text included in the summary information of the first text data is classified into predetermined items using a regular expression corresponding to the pattern.

[0018] Alternatively, the step of obtaining the dataset may include a step of obtaining the dataset in which text included in the summary information of the first text data is classified by predetermined items using the pre-learned information classification model, if a pattern does not exist in the summary information of the first text data.

[0019] Alternatively, the method may further include: comparing the reference information determined for each of the predetermined items with the at least one text classified for each of the predetermined items; if, as a result of comparing the at least one text, there is at least one erroneous text among the at least one text that does not conform to the reference information determined for each of the predetermined items, removing the at least one erroneous text from the dataset; and storing the dataset from which the at least one erroneous text has been removed.

[0020] Alternatively, a computing device may include a processor; and a memory, wherein the processor may perform the following operations: obtaining first text data including unstructured data extracted from a medical record database; obtaining summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model; and obtaining a dataset in which at least one text included in the summary information of the first text data is classified by predetermined items from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or a regular expression, wherein the dataset is in a structured format including columns composed of the predetermined items and rows composed of the at least one text corresponding to each column.

[0021] Alternatively, a computer program stored in a computer-readable storage medium, which when executed by a computing device, causes the computing device to perform operations, the operations including: obtaining first text data including unstructured data extracted from a medical record database; obtaining summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model; and obtaining a dataset in which at least one text included in the summary information of the first text data is classified by predetermined items from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or a regular expression, the dataset being in a structured format including columns including the predetermined items and rows including the at least one text corresponding to each column.

[0022] This disclosure can provide faster and more accurate results to patients by reducing the burden of work required for conversion or interpretation previously performed by medical staff.

[0023] The present disclosure can increase the accuracy of diagnosis and data reliability by reducing human errors or inconsistencies that may occur during data entry and conversion processes.

[0024] This disclosure can contribute to improving the quality of medical care by enabling smooth information sharing among various medical institutions and medical professionals by standardizing medical records.

[0025] The present disclosure can reduce computing resources generated from unstructured data, and the technology for converting big data is expected to have a cost-saving effect on medical research and artificial intelligence-based research.

[0026] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains from the description below.

[0027] Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to similar components generally. In the following examples, for purposes of explanation, numerous specific details are set forth to provide a comprehensive understanding of one or more aspects. However, it will be apparent that such aspects may be practiced without these specific details.

[0028] FIG. 1 is a diagram illustrating a computing device for performing formalization of unstructured data using artificial intelligence according to some embodiments of the present disclosure.

[0029] FIG. 2 is a diagram illustrating an exemplary structure of an artificial intelligence-based model according to some embodiments of the present disclosure.

[0030] FIG. 3 is a diagram illustrating a method for formalizing unstructured data using artificial intelligence, performed on a computing device according to some embodiments of the present disclosure.

[0031] FIG. 4 is a schematic diagram illustrating a process of formalizing unstructured data using artificial intelligence, performed in a computing device according to some embodiments of the present disclosure.

[0032] FIG. 5 illustrates a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0033] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0034] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0035] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the terms "or" and "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0036] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0037] And, the term "at least one of A or B" should be interpreted to mean "if it includes only A", "if it includes only B", or "if it is combined in the composition of A and B".

[0038] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0039] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present invention is not limited to the embodiments disclosed herein. The present invention is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0040] In the present disclosure, terms expressed as N, such as first, second, or third, are used to distinguish at least one entity. For example, entities expressed as first and second may be the same or different from each other.

[0041] FIG. 1 is a diagram illustrating a computing device for performing formalization of unstructured data using artificial intelligence according to some embodiments of the present disclosure.

[0042] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0043] A computing device (100) according to some embodiments of the present disclosure may be a device for performing formalization of unstructured data. For example, the computing device (100) may be a device that utilizes artificial intelligence to formalize unstructured data and performs a process of storing the formalized data. The computing device (100) may include any type of server or user terminal.

[0044] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0045] The processor (110) may be composed of one or more cores and may include a processor for performing operations related to data processing, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU) of the computing device (100).

[0046] According to one embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. For example, the processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from the input data, calculating errors, and updating weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process learning of a network function. For example, the CPU and GPGPU may together process learning of a network function and classification of data using the network function. Furthermore, in one embodiment of the present disclosure, processors of a plurality of computing devices may be used together to process learning of a network function and classification of data using the network function.

[0047] The processor (110) can typically control the overall operation of the computing device (100). The processor (110) can process signals, data, information, etc. input or output through components included in the computing device (100) or run application programs stored in the memory (130), thereby providing or processing appropriate information or functions to the user.

[0048] In one embodiment of the present disclosure, the memory (130) may store any form of information generated or determined by the processor (110) and / or any form of information received by the network unit (150). In one embodiment, the memory (130) may store a database. The database may be a collection of data stored in a form that can be processed by the computing device (100).

[0049] In one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD 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, and / or an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the Internet. The description of the memory described above is merely an example, and the present disclosure is not limited thereto. The memory (130) may be operated by the processor (110).

[0050] According to one embodiment of the present disclosure, the network unit (150) may include any wired or wireless communication network capable of transmitting and receiving any type of data and signals, etc., as represented in the network of the present disclosure. The technologies described herein may be used not only in the networks mentioned above, but also in other networks.

[0051] FIG. 2 is a diagram illustrating an exemplary structure of an artificial intelligence-based model according to some embodiments of the present disclosure.

[0052] Throughout this specification, the terms artificial intelligence-based model (e.g., summary information generation model, information classification model, etc.), computational model, neural network, network function, and neural network may be used with the same meaning (interchangeably).

[0053] A neural network can be composed of a set of interconnected computational units, generally referred to as nodes. These nodes can also be referred to as neurons. A neural network consists of at least one node. The nodes (or neurons) that make up a neural network can be interconnected by one or more links.

[0054] Within a neural network, one or more nodes connected via links can form a relationship between input nodes and output nodes. The concept of input nodes and output nodes is relative, meaning that any node that is in an output node relationship with one node can also be in an input node relationship with another node, and vice versa. As described above, the relationship between input nodes and output nodes can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0055] In a relationship between input nodes and output nodes connected through a single link, the data of the output node can have its value determined based on the data input to the input node. Here, the link interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by the user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set on the link corresponding to each input node.

[0056] As described above, a neural network is a network in which one or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​for the links, the two neural networks can be perceived as different from each other.

[0057] A neural network can be composed of a set of one or more nodes. A subset of the nodes comprising the neural network can form a layer. Some of the nodes comprising the neural network can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links required to reach the node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a different way than described above. For example, a layer of nodes can be defined by its distance from the final output node.

[0058] In one embodiment of the present disclosure, a set of neurons or nodes may be defined as a layer.

[0059] An initial input node may refer to one or more nodes within a neural network into which data is directly input without going through links with other nodes. Alternatively, within a neural network, it may refer to nodes that do not have other input nodes connected by links in the relationship between nodes based on links. Similarly, a final output node may refer to one or more nodes within a neural network that do not have output nodes in their relationship with other nodes. Furthermore, a hidden node may refer to nodes that constitute a neural network other than the initial input node and the final output node.

[0060] A neural network according to one embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be the same as the number of nodes in an output layer, and the number of nodes decreases and then increases as it progresses from the input layer to a hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be less than the number of nodes in an output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be greater than the number of nodes in an output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the neural networks described above.

[0061] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using a deep neural network, one can identify latent structures in data. A deep neural network can include a convolutional neural network (CNN), a recurrent neural network (RNN), an autoencoder, a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a Q network, a U network, a Siamese network, and the like. The description of the above-described deep neural network is merely an example, and the present disclosure is not limited thereto.

[0062] For example, the artificial intelligence-based model of the present disclosure may include a transformer, a generative pre-trained transformer (GPT), a bidirectional encoder representation from transformers (BERT), etc.

[0063] The artificial intelligence-based model of the present disclosure can be represented by a network structure of any structure described above, including an input layer, a hidden layer, and an output layer.

[0064] The neural network that can be used in the artificial intelligence-based model of the present disclosure may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, federated learning for distributed deep learning, and incremental learning. Training of the neural network may be a process of applying knowledge to the neural network to perform a specific operation.

[0065] Neural networks can be trained to minimize output errors. This process involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error. In supervised learning, training data with the correct answer for each training data is used (i.e., labeled training data). In unsupervised learning, the correct answer may not be labeled for each training data. For example, in supervised learning for data classification, the training data may be data with each category labeled. Labeled training data is input into the neural network, and the error can be calculated by comparing the output (category) of the neural network with the labels of the training data.

[0066] As another example, in unsupervised learning for data classification, errors can be calculated by comparing input training data with the output of a neural network. The calculated errors are backpropagated through the neural network (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated based on the learning rate. The amount of change in the updated connection weights of each node can be determined by the learning rate. The neural network's calculation of the input data and backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used early in the neural network's training process to quickly achieve a certain level of performance, thereby improving efficiency. A lower learning rate can be used later in the training process to improve accuracy.

[0067] In neural network training, training data can typically be a subset of real-world data (i.e., the data to be processed using the trained neural network). Therefore, there can be a learning cycle where errors on the training data decrease but errors on the real-world data increase. Overfitting is a phenomenon where excessive training on the training data leads to increased errors on the real-world data. For example, a neural network trained on yellow cats may fail to recognize cats when shown non-yellow colors, a type of overfitting. Overfitting can increase errors in machine learning algorithms. Various optimization methods can be used to prevent overfitting. These methods include increasing the training data, regularization, dropout, which disables some nodes in the network during the learning process, and utilizing batch normalization layers.

[0068] A computer-readable medium storing a data structure according to one embodiment of the present disclosure is disclosed. The aforementioned data structure can be stored in a storage unit in the present disclosure, executed by a processor, and transmitted and received by a communication unit.

[0069] A data structure can refer to the organization, management, and storage of data to enable efficient access and modification. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, or data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements can include connections between user-defined data elements. Physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0070] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one data item is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets with an internal order. Lists can also include linked lists. A linked list is a data structure in which data is linked in a single line, each item having a pointer. In a linked list, a pointer can contain information about the next or previous item. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data listing structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0071] A nonlinear data structure can be a structure in which multiple pieces of data are connected behind a single piece of data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. A graph data structure can include a tree data structure. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0072] The data structure may include a neural network. The data structure including the neural network may be stored on a computer-readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, neural network weights, neural network hyperparameters, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for neural network training, etc. The data structure including the neural network may include any of the components described above. That is, the data structure including the neural network may be configured to include all or any combination of the following: preprocessed data for processing by the neural network, data input to the neural network, neural network weights, neural network hyperparameters, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for neural network training, etc. In addition to the aforementioned components, the data structure including the neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include any form of data used or generated in the computational process of the neural network, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. The neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. The neural network is composed of at least one node.

[0073] The data structure may include data input to a neural network. The data structure including the data input to the neural network may be stored on a computer-readable medium. The data input to the neural network may include training data input during the neural network training process and / or input data input to the neural network after training has been completed. The data input to the neural network may include preprocessed data and / or data to be preprocessed. Preprocessing may include a data processing process for inputting data to the neural network. Accordingly, the data structure may include data to be preprocessed and data generated by the preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0074] The data structure may include weights of a neural network (in this specification, weights and parameters may be used interchangeably). And the data structure including the weights of the neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on the values ​​input to the input nodes connected to the output node and the weights set for the links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0075] By way of example and not limitation, the weights may include weights that vary during the neural network training process and / or weights that have completed neural network training. The weights that vary during the neural network training process may include weights at the start of the training cycle and / or weights that vary during the training cycle. The weights that have completed neural network training may include weights that have completed the training cycle. Accordingly, a data structure including the weights of a neural network may include a data structure including weights that vary during the neural network training process and / or weights that have completed neural network training. Therefore, the above-described weights and / or combinations of each weight are included in the data structure including the weights of a neural network. The above-described data structures are merely examples and the present disclosure is not limited thereto.

[0076] A data structure including neural network weights can be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed and used. A computing device can serialize the data structure to transmit and receive data over a network. The serialized data structure including neural network weights can be reconstructed on the same computing device or another computing device through deserialization. The data structure including neural network weights is not limited to serialization. Furthermore, the data structure including neural network weights can include a data structure that increases computational efficiency while minimizing the use of computing device resources (e.g., a B-Tree, an R-Tree, a Trie, an m-way search tree, an AVL tree, a Red-Black Tree in nonlinear data structures). The foregoing is merely an example, and the present disclosure is not limited thereto.

[0077] The data structure may include hyperparameters of a neural network. Furthermore, the data structure including the hyperparameters of the neural network may be stored on a computer-readable medium. The hyperparameters may be variables that can be changed by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle iterations, weight initialization (e.g., setting a range of weight values ​​to be initialized), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layer). The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0078] A transformer may be considered as a network function for an AI-based model (e.g., a summary information generation model, an information classification model, etc.) according to one embodiment of the present disclosure. For example, the AI-based model may operate based on a transformer. Such an AI-based model may be operated using, for example, a recurrent neural network with an attention algorithm or a transformer with an attention algorithm.

[0079] In one embodiment, a transformer may be composed of an encoder that encodes embedded data and a decoder that decodes the encoded data. The transformer may have a structure that receives a series of data, performs encoding and decoding steps, and outputs a series of data of different types. In one embodiment, the series of data may be processed into a form operable by the transformer. The process of processing the series of data into a form operable by the transformer may include an embedding process. Expressions such as data tokens, embedding vectors, and embedding tokens may refer to data embedded in a form operable by the transformer.

[0080] To encode and decode a series of data, a transformer can process the encoders and decoders within the transformer using an attention algorithm. An attention algorithm can refer to an algorithm that, for a given query, calculates the similarity for one or more keys, reflects this similarity in the values ​​corresponding to each key, and then weights and adds the values ​​to calculate an attention value.

[0081] Depending on how the query, key, and value are set, various types of attention algorithms can be categorized. For example, if attention is obtained by setting the query, key, and value all to the same value, this could be a self-attention algorithm. If attention is obtained by reducing the dimensionality of the embedding vector to process a series of input data in parallel and then generating individual attention heads for each segmented embedding vector, this could be a multi-head attention algorithm.

[0082] In one embodiment, the transformer may be composed of modules that perform multiple multi-head self-attention algorithms or multi-head encoder-decoder algorithms. In one embodiment, the transformer may also include additional components other than attention algorithms, such as embedding layers, normalization layers, and softmax layers. Methods for constructing a transformer using attention algorithms may include methods disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

[0083] A transformer can be applied to various data domains, such as embedded natural language, embedded sequence information, segmented image data, and audio waveforms, to transform a series of input data into a series of output data. To transform data with various data domains into a series of data that can be input to a transformer, the transformer can embed the data. The transformer can process additional data expressing the relative positional relationship or phase relationship between a series of input data. Alternatively, vectors expressing the relative positional relationship or phase relationship between the input data can be additionally reflected in the series of input data to embed the series of input data. In one example, the relative positional relationship between a series of input data may include, but is not limited to, word order within a natural language sentence, the relative positional relationship between each segmented image, and the time order of segmented audio waveforms. The process of adding information expressing the relative positional relationship or phase relationship between a series of input data may be referred to as positional encoding.

[0084] An example of how to embed data and transform it into a transformer is disclosed in Dosovitskiy, et al., AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE, which is incorporated herein by reference.

[0085] FIG. 3 is a diagram illustrating a method of managing a hospitalized patient performed on a computing device according to some embodiments of the present disclosure.

[0086] Embodiments of the present disclosure may include embodiments for training an artificial intelligence-based model (e.g., a summary information generation model, an information classification model, etc.) and embodiments for actually obtaining an inference result (e.g., summary information of data, a dataset, etc.) through the trained artificial intelligence-based model.

[0087] In this disclosure, the operation of an AI-based model may be described as either a process of training an AI-based model or a process of performing inference using the AI-based model (e.g., a process of generating data summaries, datasets, etc.). This description is provided for convenience of explanation, and even if it is described as either a process of performing learning or a process of performing inference, it should be interpreted as encompassing either the learning or inference process.

[0088] In one embodiment, the steps illustrated in FIG. 3 may be performed by a computing device (100). In a further embodiment, the steps illustrated in FIG. 3 may be implemented by multiple entities, such as some of the steps illustrated in FIG. 3 being performed on a user terminal and others being performed on a server.

[0089] In one embodiment, the processor (110) of the computing device (100) may obtain first text data including unstructured data. For example, the processor (110) may obtain first text data including unstructured data extracted from a medical record database (S310). As another example, the processor (110) may obtain first text data including unstructured data input by a user from a user terminal (or server) different from the computing device (100).

[0090] In one embodiment, a medical records database may be a collection of data related to a specific person's medical condition stored in a form that can be processed by a computing device (100). The processor (110) may obtain unstructured medical data from the medical records database.

[0091] In one embodiment, unstructured data may be data that is not structured and has no defined structure. For example, unstructured data may include video files, audio files, photo files, document files, email files, etc. In one embodiment, unstructured data may include unstructured medical data.

[0092] In one embodiment, the first text data may be unstructured data (e.g., medical data, etc.) expressed in text form. In one embodiment, the first text data may include unstructured data extracted from an online and / or offline undisclosed medical record database. The text may include words, sentences, and / or paragraphs. The medical data may include information related to a specific person's medical care. For example, the medical data may include variables related to at least one disease in the medical field. For example, the medical data may include data including physical information, health information, and treatment information of a specific person, and may include CT image data, MRI test result data, electrocardiogram measurement result data, and Electronic Medical Record (EMR) data of a specific person. The variables may be related to diseases, such as lifestyle habits (drinking, smoking), family history, age, gender, cholesterol level, and genetic variables.

[0093] The processor (110) can obtain summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model (S320).

[0094] The predetermined first prompt may include a request sentence (or, a question sentence) for requesting the first summary information generation model to output summary information of the first text data. For example, the first prompt may include a request sentence in natural language, such as “Generate summary information of the text data that summarizes the text data.” As another example, the first prompt may include a request sentence in an artificial language, such as a programming language (e.g., a command sentence composed of commands).

[0095] In one embodiment, the summary information of the first text data may include text corresponding to each predetermined item in the first text data.

[0096] In one embodiment, the predetermined item may include at least one of personal information including at least one of age or gender, physical condition information related to the body, and / or disease condition information related to a disease.

[0097] In one embodiment, personal information may be information about an individual that can identify the individual, either alone or in combination, such as name, resident registration number, age, gender, etc.

[0098] In one embodiment, the physical state information may be information about an individual's body, such as height, weight, breathing condition, blood pressure, etc.

[0099] In one embodiment, the disease status information may be information about the individual's disease, such as the name of the disease, the staging of the disease, whether surgery was performed, the name of the surgery, the details of the surgery, etc.

[0100] In one embodiment, the first summary information generation model may include a large language model (LLM). A large language model (LLM) may refer to a language model with billions to hundreds of billions of parameters. A large language model (LLM) can achieve relatively higher performance in various aspects of natural language processing, including translation and summarization, compared to existing small-scale language models. In one embodiment, the first summary information generation model may be tuned and used using various fine-tuning techniques, such as Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA).

[0101] In one embodiment, the first summary information generation model can be distributedly trained using multiple computing devices. For example, the computing device (100) can pipeline the training process of the first summary information generation model and distribute and transmit the multiple training stages divided through the pipeline to external computing devices different from the computing device (100), thereby enabling the training process of the first summary information generation model to be processed in parallel using multiple computing devices. Pipelining can mean dividing a task into multiple stages and sequentially connecting each stage to organize the entire task.

[0102] In one embodiment, the first summary information generation model can be trained to generate summary information for input text data using second text data including unstructured data extracted from a medical record database, summary information of the second text data, and a predetermined first prompt.

[0103] In one embodiment, the first summary information generation model can be trained to receive second text data and a predetermined first prompt as input and compare the first output information output by the model with the summary information of the second text data, so that the first output information becomes similar to the summary information of the second text data.

[0104] In one embodiment, the second text data may be unstructured data (e.g., medical data) expressed in text form. In one embodiment, the second text data may include unstructured data extracted from a medical record database freely available online and / or offline.

[0105] In one embodiment, the summary information of the second text data may include text corresponding to each predetermined item in the second text data.

[0106] In one embodiment, the summary information of the second text data can be output by the pre-trained second summary information generation model in response to inputting the second text data and the predetermined first prompt into the artificial intelligence-based pre-trained second summary information generation model.

[0107] In one embodiment, the pre-trained second summary information generation model can be pre-trained to output summary information including text corresponding to predetermined items from the third text data using third text data including unstructured data extracted from a medical record database and a predetermined first prompt.

[0108] In one embodiment, the second summary information generation model may include a pre-trained, large-scale language model. For example, the second summary information generation model may use a Transformer (GPT series) model, such as Google's Bard, Chat-GPT, XLNet, Microsoft's Turing-NLG, CLIP, etc. However, the second summary information generation model is not limited to this, and various language models (LMs) may be used.

[0109] In one embodiment, the third text data may be unstructured data (e.g., medical data) expressed in text form. In one embodiment, the third text data may include unstructured data extracted from a medical record database freely available online and / or offline.

[0110] In one embodiment, the summary information of the third text data may include text corresponding to each predetermined item in the third text data.

[0111] In one embodiment, the summary information of the text data output by the second summary information generation model in response to inputting text data and a prompt to the second summary information generation model may be used as ground truth data during the training process of the first summary information generation model. For example, the summary information of the second text data output by the second summary information generation model may be used as ground truth data to be compared with the first output information of the first summary information generation model.

[0112] In one embodiment, the predetermined first prompt may be updated through a fine-tuning process performed on the second summary information generation model. Therefore, the predetermined first prompt may be the final updated prompt through the fine-tuning process during the training process of the second summary information generation model.

[0113] In one embodiment, the processor (110) may obtain a dataset in which at least one text included in the summary information of the first text data is classified by predetermined items from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or regular expression (S330).

[0114] In one embodiment, a dataset may refer to a collection and / or file of related data for a specific task. A dataset may be structured in a table format, containing rows and columns. For example, a dataset may be structured in a structured format, with columns consisting of predetermined items and rows consisting of at least one text corresponding to each column.

[0115] In one embodiment, the pre-trained information classification model may include a natural language processing model that is pre-trained to process natural language contained in text data to classify the text into predetermined categories.

[0116] In one embodiment, a natural language processing model may refer to an artificial intelligence-based model that mechanically understands and processes natural language. For example, a natural language processing model may utilize BERT (Bidirectional Encoder Representations from Transformers) and NER (Named Entity Recognition). However, the natural language processing model is not limited to this, and various natural language processing models may be used, such as a statistical-based natural language processing model that processes natural language by finding statistical patterns in given text data, a rule-based natural language processing model that processes text based on predefined rules, and a word embedding model that preserves semantic similarity by representing words as vectors.

[0117] In one embodiment, a regular expression may refer to a formal language used to represent specific strings. Regular expressions can express specific patterns using predetermined metacharacters and symbols.

[0118] In one embodiment, the processor (110) can determine whether a pattern exists in the summary information of the first text data.

[0119] In one embodiment, a pattern may be a series of text strings representing a specific rule or structure. The pattern may include a structural pattern having a predetermined structure (e.g., format, etc.) within the text, a grammatical pattern having a predetermined grammatical rule within the text, etc. Accordingly, the processor (110) may determine whether a pattern exists in the summary information of the first text data by comparing the predetermined pattern with the summary information of the first text data. In one embodiment, the summary information of the first text data may be configured with a structure corresponding to the predetermined pattern. In one embodiment, the summary information of the first text data may be configured with an unpatterned structure rather than a predetermined pattern.

[0120] In one embodiment, the processor (110) can obtain the dataset by applying different methodologies for obtaining the dataset based on whether a pattern exists.

[0121] In one embodiment, if a pattern exists in the summary information of the first text data, the processor (110) may obtain a dataset in which the text included in the summary information of the first text data is classified into predetermined items by using a regular expression corresponding to the pattern. For example, if the summary information of the first text data corresponds to the format of a medical record, the processor (110) may obtain a dataset in which the text included in the summary information of the first text data is classified into predetermined items by using a regular expression corresponding to the format of the medical record.

[0122] In one embodiment, if there is no pattern in the summary information of the first text data, the processor (110) may obtain a dataset in which text included in the summary information of the first text data is classified by predetermined items using a pre-learned information classification model.

[0123] In one embodiment, the processor (110) may compare reference information determined for each predetermined item with at least one text classified for each predetermined item. The reference information serves as a reference for verifying each text classified for each predetermined item, and may include information regarding columns determined for each predetermined item, data type information, data range information, data length information, and the like.

[0124] In one embodiment, the processor (110) may remove at least one erroneous text from the dataset if, as a result of comparing at least one text, there is at least one erroneous text that does not match the criteria information determined for each predetermined item among the at least one text.

[0125] In one embodiment, the processor (110) may delete a first column corresponding to the first item in the dataset if there is no text classified as the first item among the predetermined items in the dataset (e.g., if the classified text is determined to be an error text and is removed, if there is no text corresponding to the first item in the text data or summary information of the text data, etc.).

[0126] In one embodiment, the processor (110) may organize at least one error text removed from the dataset into a separate table (e.g., a temporary table, etc.) and store it in memory (130).

[0127] In one embodiment, the processor (110) may store a dataset with at least one erroneous text removed in memory (130).

[0128] For example, if the dataset from which at least one error text has been removed corresponds to a predetermined structure (e.g., a common data model (CDM)), the processor (110) may store the dataset from which at least one error text has been removed in the memory (130) without a separate conversion.

[0129] As another example, the processor (110) can transform and store a dataset with at least one erroneous text removed into a predetermined structure.

[0130] In one embodiment, a common data model may be a standard established for converting data of different structures into data of the same structure. For example, a common data model may be a standard for converting electronic medical record data of different structures stored in different hospitals into data of the same structure.

[0131] FIG. 4 is a schematic diagram illustrating a process of formalizing unstructured data using artificial intelligence, performed in a computing device according to some embodiments of the present disclosure.

[0132] Referring to FIG. 4, in one embodiment, the processor (110) may obtain a prompt (411) and text data (412), which are input data (410) to be input into artificial intelligence-based models (420, 440). For example, the processor (110) may obtain a predetermined first prompt (411) and second text data (412).

[0133] In one embodiment, the processor (110) may input the first prompt (411) and the second text data (412) into an artificial intelligence-based pre-learned second summary information generation model (420), thereby obtaining summary information (430) of the second text data from the second summary information generation model (420).

[0134] In one embodiment, the processor (110) may obtain first output information (450) from the first summary information generation model (440) by inputting the first prompt (411) and the second text data (412) into the artificial intelligence-based pre-learned first summary information generation model (440).

[0135] In one embodiment, the processor (110) may train the first summary information generation model (440) in a direction in which the summary information (430) of the second text data and the first output information (450) become similar. That is, the processor (110) may train the first summary information generation model (440) in a direction in which the error between the summary information (430) of the second text data and the first output information (450) is reduced. Accordingly, the summary information (430) of the second text data may be used as correct data (i.e., a label) in the training process of the first summary information generation model (440).

[0136] In one embodiment, the processor (110) may input input data including a prompt and text data into a first summary information generation model (440) learned in the manner described above, thereby obtaining summary information of the text data from the first summary information generation model (440).

[0137] The processor (110) can obtain a dataset (470) in which at least one text included in the summary information of the text data is classified by predetermined items from the summary information of the text data using a classification technique (460) including an artificial intelligence-based pre-learned information classification model (461) or a regular expression (462).

[0138] The processor (110) compares the reference information determined for each predetermined item with at least one text classified for each predetermined item, and if there is at least one error text among the at least one text that does not match the reference information determined for each predetermined item, the processor (110) can remove the at least one error text from the data set (470). The processor (110) can store the data set (480) from which the at least one error text has been removed in the memory (130).

[0139] A specific description of the process of standardizing unstructured data using artificial intelligence can be replaced with the content described above through Figures 1 to 3, and overlapping content has been omitted.

[0140] As described above with reference to FIGS. 1 to 3, the computing device (100) according to one embodiment of the present disclosure can provide faster and more accurate results to patients by reducing the burden of work required for conversion or interpretation previously performed by medical staff.

[0141] In one embodiment, the computing device (100) can increase diagnostic accuracy and data reliability by reducing human errors or inconsistencies that may occur during data entry and conversion processes.

[0142] In one embodiment, the computing device (100) can contribute to improving the quality of medical care by enabling seamless information sharing among various medical institutions and medical professionals by standardizing medical records.

[0143] In one embodiment, the computing device (100) can reduce computing resources generated from unstructured data, and the technology for converting big data can be expected to have a cost-saving effect in medical research and artificial intelligence-based research.

[0144] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure pertains from the contents described in the present disclosure.

[0145] FIG. 5 illustrates a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0146] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0147] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0148] The described embodiments of the present disclosure can also be practiced in distributed computing environments, where certain 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 memory storage devices.

[0149] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0150] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0151] An exemplary environment for implementing various aspects of the present disclosure is illustrated, including a computer (1102), which includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0152] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0153] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0154] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of media readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0155] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0156] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0157] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0158] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0159] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0160] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0161] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0162] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0163] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0164] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0165] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0166] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0167] As described above, the relevant contents have been described in the best form for carrying out the invention.

[0168] It can be used in devices and systems that utilize artificial intelligence technology to format unstructured data and store formatted data in a database.

Claims

1. A method for standardizing unstructured data performed on a computing device, A step of obtaining first text data including unstructured data extracted from a medical record database; A step of obtaining summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model; A step of obtaining a dataset in which at least one text included in the summary information of the first text data is classified by predetermined items from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or a regular expression, which is a formal language used to express a specific string, wherein the dataset is in a structured format including columns composed of the predetermined items and rows composed of the at least one text corresponding to each column; A step of comparing the reference information determined for each of the above predetermined items with at least one text classified for each of the above predetermined items; A step of removing at least one erroneous text from the dataset, if, as a result of comparing each of the at least one text, there is at least one erroneous text among the at least one text that does not conform to the standard information determined for each of the predetermined items; A step of storing a dataset from which at least one error text has been removed; and A step of configuring and storing at least one error text in a temporary table; Including, The steps for obtaining the above dataset are: A step of determining whether a pattern exists in the summary information of the first text data; and A step of obtaining the dataset by applying different methodologies for obtaining the dataset for cases where the pattern exists and cases where the pattern does not exist; Including, The above pre-learned information classification model is, A natural language processing model that is pre-trained to process natural language contained in text data and classify the text into predetermined items, The above first summary information generation model is, It is learned to generate summary information for input text data using second text data including unstructured data extracted from a medical record database, summary information of the second text data, and the predetermined first prompt, and The summary information of the above second text data is: In response to inputting the second text data and the predetermined first prompt into the artificial intelligence-based pre-trained second summary information generation model, the second summary information generation model outputs the second summary information generation model. method.

2. In paragraph 1, The above predetermined first prompt is, A request sentence for requesting the output of summary information of the first text data to the first summary information generation model, method.

3. In paragraph 1, The above first summary information generation model is, By comparing the first output information output by inputting the second text data and the predetermined first prompt and the summary information of the second text data, learning is performed in a direction to reduce the error between the first output information and the summary information of the second text data. method.

4. In paragraph 1, The above pre-learned second summary information generation model is, A third text data including unstructured data extracted from a medical record database and a pre-trained system for outputting summary information including text corresponding to the predetermined item from the third text data using the pre-determined first prompt. method.

5. In paragraph 1, In response to inputting text data and a prompt to the second summary information generation model, the summary information of the text data output by the second summary information generation model is used as ground truth data in the learning process of the first summary information generation model. method.

6. In paragraph 1, The above predetermined items are, Personal information including at least one of age or gender; Physical condition information related to the body; and Disease status information related to the disease; including, method.

7. In paragraph 1, The steps for obtaining the above dataset are: A step of obtaining a dataset in which text included in the summary information of the first text data is classified by predetermined items using a regular expression corresponding to the pattern when a pattern exists in the summary information of the first text data; including, method.

8. In paragraph 1, The steps for obtaining the above dataset are: If there is no pattern in the summary information of the first text data, a step of obtaining the dataset in which the text included in the summary information of the first text data is classified by predetermined items using the pre-learned information classification model; including, method.

9. In a computing device for performing standardization of unstructured data, processor; and Includes memory, The above processor, An act of obtaining first text data comprising unstructured data extracted from a medical record database; An operation of obtaining summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model; An operation of obtaining a dataset in which at least one text included in the summary information of the first text data is classified by predetermined items from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or a regular expression, which is a formal language used to express a specific string, wherein the dataset is in a structured format including columns composed of the predetermined items and rows composed of the at least one text corresponding to each column; An operation of comparing the reference information determined for each of the above predetermined items with at least one text classified for each of the above predetermined items; An operation of removing at least one erroneous text from the dataset if, as a result of comparing each of the at least one text, there is at least one erroneous text among the at least one text that does not conform to the standard information determined for each of the predetermined items; The operation of saving a dataset from which at least one error text has been removed; and An action of configuring and storing at least one error text in a temporary table; , and The action of obtaining the above dataset is: An operation for determining whether a pattern exists in the summary information of the first text data; and An operation of obtaining the dataset by applying different methodologies for obtaining the dataset for cases where the pattern exists and cases where the pattern does not exist; Including, The above pre-learned information classification model is, A natural language processing model that is pre-trained to process natural language contained in text data and classify the text into predetermined items, The above first summary information generation model is, It is learned to generate summary information for input text data using second text data including unstructured data extracted from a medical record database, summary information of the second text data, and the predetermined first prompt, and The summary information of the above second text data is: In response to inputting the second text data and the predetermined first prompt into the artificial intelligence-based pre-trained second summary information generation model, the second summary information generation model outputs the second summary information generation model. Computing device.

10. A computer program stored in a computer-readable storage medium, wherein the computer program, when executed by a computing device, causes the computing device to perform operations for formatting unstructured data, the operations including: An act of obtaining first text data comprising unstructured data extracted from a medical record database; An operation of obtaining summary information of the first text data from the first text data by inputting the first text data and a predetermined first prompt into an artificial intelligence-based first summary information generation model; An operation of obtaining a dataset in which at least one text included in the summary information of the first text data is classified by predetermined items from the summary information of the first text data using an artificial intelligence-based pre-learned information classification model or a regular expression, which is a formal language used to express a specific string, wherein the dataset is in a structured format including columns composed of the predetermined items and rows composed of the at least one text corresponding to each column; An operation of comparing the reference information determined for each of the above predetermined items with at least one text classified for each of the above predetermined items; An operation of removing at least one erroneous text from the dataset if, as a result of comparing each of the at least one text, there is at least one erroneous text among the at least one text that does not conform to the standard information determined for each of the predetermined items; The operation of saving a dataset from which at least one error text has been removed; and An action of configuring and storing at least one error text in a temporary table; Including, The action of obtaining the above dataset is: An operation for determining whether a pattern exists in the summary information of the first text data; and An operation of obtaining the dataset by applying different methodologies for obtaining the dataset for cases where the pattern exists and cases where the pattern does not exist; Including, The above pre-learned information classification model is, A natural language processing model that is pre-trained to process natural language contained in text data and classify the text into predetermined items, The above first summary information generation model is, It is learned to generate summary information for input text data using second text data including unstructured data extracted from a medical record database, summary information of the second text data, and the predetermined first prompt, and The summary information of the above second text data is: In response to inputting the second text data and the predetermined first prompt into the artificial intelligence-based pre-trained second summary information generation model, the second summary information generation model outputs the second summary information generation model. A computer program stored on a computer-readable storage medium.

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