KR-20260065546-A - METHOD AND SYSTEM FOR RESPONDING TO DERIVED INDICATIONS LINKED TO PRIMARY INDICATIONS USING ARTIFICIAL INTELLIGENCE

KR20260065546AKR 20260065546 AKR20260065546 AKR 20260065546AKR-20260065546-A

Abstract

A method for responding to a derivative indication linked to a primary indication using artificial intelligence according to the present invention may include: collecting patient information including information regarding the patient's primary indication; generating a prediction prompt requesting the prediction of the probability of occurrence of a derivative indication associated with the primary indication based on the patient information; processing the generated prediction prompt as input to a pre-trained artificial intelligence model to obtain information on the probability of occurrence of at least one derivative indication corresponding to the primary indication; generating prescription information regarding the patient's primary indication and the derivative indication based on the information on the probability of occurrence of the derivative indication; and transmitting the prescription information to at least one of a pre-configured server and a terminal.

Inventors

윤찬

Assignees

에버엑스 주식회사

Dates

Publication Date: 20260508
Application Date: 20251029
Priority Date: 20241030

Claims (17)

A step of collecting patient information including information on the patient's primary indications; A step of generating a prediction prompt requesting the prediction of the likelihood of occurrence of a derivative indication associated with the primary indication based on the above patient information; A step of processing the generated prediction prompt as input to a pre-trained artificial intelligence model to obtain information on the probability of occurrence of at least one derivative indication corresponding to the primary indication; A step of generating prescription information for the patient's primary indication and the derivative indication based on information on the possibility of occurrence of the derivative indication; and A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by including the step of transmitting the above-mentioned prescription information to at least one of a pre-configured server and terminal.
In paragraph 1, The step of obtaining information on the possibility of occurrence of the above-mentioned derivative indication is, Calculate the probability values of occurrence for each of the above-mentioned derivative indications using the above-mentioned previously trained artificial intelligence model, and The information on the possibility of the above-mentioned derivative indications is, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by including probability information based on the probability value of occurrence for each of the derivative indications mentioned above.
In paragraph 2, The step of generating the above prescription information is, Based on the above probability information, generate a prescription information generation prompt requesting the generation of the prescription information such that the strength or content of the prescription for the above derivative indication changes in the prescription information, and A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by processing the above prescription information generation prompt as input to the above artificial intelligence model to obtain different prescription information according to the probability of occurrence of the above derivative indication.
In paragraph 1, The above patient information is, The medical data of the patient and the biometric information of the patient collected from at least one sensor, In the step of obtaining information on the possibility of occurrence of the above-mentioned derivative indication, After the above primary indication occurs, the pattern of change in the above biological information is analyzed, and By processing the above-mentioned bio-information change patterns and medical data related to the above-mentioned primary indication as input to the above-mentioned artificial intelligence model, the probability of occurrence of the above-mentioned derivative indication is predicted, and A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by generating information on the probability of occurrence of the derivative indication based on the predicted probability of occurrence of the derivative indication.
In paragraph 4, The above artificial intelligence model is, Analyze the correlation between the change pattern of the above biological information and the above primary indication and the above derivative indication, and A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by being configured to predict the likelihood of occurrence of the derivative indication based on the above correlation.
In paragraph 5, The above artificial intelligence model is, Reflecting the characteristics of fluctuation in the above bio-information according to the progression status or treatment response of the above primary indication, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by being configured to predict the probability of occurrence of the derivative indication by analyzing the correlation between the above-mentioned variation characteristics and the above-mentioned derivative indication.
In paragraph 1, The step of generating the above prescription information is, Update existing prescription information for the primary indication based on the possibility of the occurrence of the above derivative indication, and A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized in that the above update includes at least one of the adjustment of the type of medication, the dosage of medication, the treatment cycle, the monitoring cycle, and the exercise therapy program.
In Paragraph 7, The adjustment of the above exercise therapy program is, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by being configured to change at least one of the type, intensity, frequency, and duration of exercise.
In Paragraph 7, In the step of generating the above prescription information Adjustments to the exercise therapy program are made to add the exercise items to prevent or alleviate the predicted derivative indications, and The adjustment of the above exercise therapy program is, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by being configured to determine the exercise type, intensity, frequency, and performance area of the exercise item corresponding to the type of derivative indication.
In paragraph 1, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized in that the above artificial intelligence model is a deep learning-based neural network model trained to predict the probability of occurrence of derivative indications using the above patient information, biometric information, and medical data as inputs.
In Paragraph 10, The above artificial intelligence model is, A method for responding to a derivative indication linked to a primary indication using artificial intelligence, characterized by being a deep learning model composed of at least one of a recurrent neural network (RNN), a long short-term memory network (LSTM), and a gated recurrent unit (GRU) for learning the time-series changes of the biological information of the patient.
In Paragraph 10, The above artificial intelligence model is, It includes a Large Language Model (LLM) structure for processing the above prediction prompt expressed in natural language, and A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized in that the above LLM is a model trained with a transformer-based encoder-decoder structure.
In Paragraph 10, The above artificial intelligence model is, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by being composed of a multimodal hybrid neural network structure that processes text information of the medical data, time-series data of the biometric information, and medical image data together.
In Paragraph 10, The above artificial intelligence model is, A method for responding to derivative indications linked to a primary indication using artificial intelligence, characterized by being a reinforcement learning-based model that learns to continuously improve the prediction performance of derivative indications by using feedback on the treatment response or prediction accuracy of the patient as a reward signal.
In Paragraph 10, The above artificial intelligence model is, It consists of a hybrid structure combining a large-scale language model (LLM) and a deep learning model for time series forecasting, and The above LLM interprets the above medical data and the above prediction prompt, and A method for responding to a derivative indication linked to a primary indication using artificial intelligence, characterized in that the deep learning model analyzes the change pattern of the biological information and predicts the probability of the derivative indication occurring based on the combined result of the LLM and the deep learning model.
In electronic devices, Memory for storing instructions; and It includes at least one processor electrically connected to the memory, and When the above instructions are executed by the at least one processor, the at least one processor, Collect patient information including information on the patient's primary indications, and Based on the above patient information, generate a prediction prompt requesting a prediction of the likelihood of a derivative indication associated with the above primary indication, and The generated prediction prompt is processed as input to a pre-trained artificial intelligence model to obtain information on the probability of occurrence of at least one derivative indication corresponding to the primary indication, and Based on information on the probability of occurrence of the above derivative indication, prescription information for the patient's primary indication and the above derivative indication is generated, and A derivative indication response system linked to a primary indication using artificial intelligence, characterized by transmitting the above-mentioned prescription information to at least one of a pre-configured server and terminal.
A program that is executed by one or more processes in an electronic device and stored on a computer-readable recording medium, The above program is, A step of collecting patient information including information on the patient's primary indications; A step of generating a prediction prompt requesting the prediction of the likelihood of occurrence of a derivative indication associated with the primary indication based on the above patient information; A step of processing the generated prediction prompt as input to a pre-trained artificial intelligence model to obtain information on the probability of occurrence of at least one derivative indication corresponding to the primary indication; A step of generating prescription information for the patient's primary indication and the derivative indication based on information on the possibility of occurrence of the derivative indication; and A program stored on a computer-readable recording medium characterized by including instructions that perform the step of transmitting the above-mentioned prescription information to at least one of a pre-configured server and a terminal.

Description

Method and System for Responding to Derived Indications Linked to Primary Indications Using Artificial Intelligence The present invention relates to a method and system for generating prescription information for corresponding to derivative indications linked to a patient's primary indication using artificial intelligence. With the recent rapid advancement of artificial intelligence (AI) technology, various types of AI models capable of independently learning and reasoning from complex data have emerged. In particular, the medical field is actively developing AI-based technologies that comprehensively analyze diverse information—such as patients' biometric data, medical records, and imaging data—to predict disease risk and suggest treatment directions. These AI models are evolving beyond simple pattern recognition to a level where they can learn temporal changes and correlations to support personalized treatment. For example, generative AI models based on Large Language Models (LLMs) used in the medical field can understand medical records and prescription information in the form of natural language. Furthermore, these AI models can be extended into a multimodal structure capable of processing medical text, biometric time-series data, and medical imaging data together, enabling them to analyze potential diseases and propose response methods. With the advancement of such artificial intelligence technology, there is a growing demand in the medical field for methods to effectively manage patient health by utilizing AI to predict at an early stage the likelihood of a patient's existing indications progressing to other indications, and to suggest appropriate prescriptions and treatments based on the results. Here, "indication" refers to a symptom or clinical situation requiring specific treatment or examination, which can be understood as the user's disease or symptoms. However, existing methods for responding to derivative indications uniformly apply predefined fixed rules, so they do not adequately reflect the progression status of the primary indication or changes in biometric information for each patient, and there was a problem of low precision in prophylactic prescriptions because they failed to consider the correlation between the primary indication and derivative indications or the risk trajectory over time. Consequently, under existing methods for managing derivative indications, patients were more likely to receive prescriptions that did not align with the risks associated with their derivative indications, which could lead to reduced treatment continuation rates and satisfaction, as well as a failure to prevent preventable complications. Accordingly, there is a need for technology that responds to derivative indications linked to the primary indication, which uses artificial intelligence to probabilistically predict the probability of derivative indications occurring for the primary indication from medical data and biometric information, and recommends prescription information and exercise therapy programs based on the predicted probability. FIG. 1 is a conceptual diagram illustrating a derivative indication response system linked to a primary indication using artificial intelligence according to the present invention. FIG. 2 is a flowchart for explaining, in general, a method for responding to derivative indications linked to a primary indication using artificial intelligence according to the present invention. FIGS. 3a to 3d are conceptual diagrams illustrating patient information and the process of collecting patient information according to the present invention. FIG. 4 is a conceptual diagram illustrating the process of generating a prediction prompt according to the present invention. FIG. 5 is a conceptual diagram illustrating the process of generating information on the possibility of occurrence of derivative indications using an artificial intelligence model according to the present invention. FIGS. 6a and FIGS. 6b are conceptual diagrams illustrating the process of generating prescription information according to the present invention and transmitting it to at least one of a pre-configured server and terminal. FIGS. 7a and FIGS. 7b are conceptual diagrams for explaining the process of adjusting an exercise therapy program according to the present invention. Figure 8 is a conceptual diagram illustrating prescription information provided to a medical staff terminal. FIG. 9 is a block diagram illustrating a computing system in which the present invention can be implemented. FIGS. 10 and FIGS. 11 are block diagrams illustrating an embodiment of a computing device according to the present invention. Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Identical or similar components are assigned the same reference number regardless of the drawing symbols, and redundant descriptions thereof will be omitted. The suffixes "modu