Method, device, and program-recorded storage medium for providing information about possibility of abdominal aortic aneurysm occurrence

Abstract

A method for providing information about the possibility of abdominal aortic aneurysm occurrence according to an embodiment of the present invention comprises the steps of: receiving an input of a plurality of pieces of demographic information and personal clinical information; generating an abdominal aortic aneurysm occurrence prediction model by performing training on the basis of the demographic information and the personal clinical information; inputting demographic information and personal clinical information about a subject to the abdominal aortic aneurysm occurrence prediction model; and outputting information about the possibility of abdominal aortic aneurysm occurrence in the subject from the abdominal aortic aneurysm occurrence prediction model.

Description

Storage medium recording a method, device and program for providing information on the possibility of occurrence of abdominal aortic aneurysm

[0001] Embodiments of the present disclosure relate to a method, device, and a storage medium storing a program for providing information on the likelihood of developing an abdominal aortic aneurysm. Specifically, the method, device, and storage medium storing the program include generating a prediction model for the likelihood of developing an abdominal aortic aneurysm based on health examination data, and inputting information on a subject to be confirmed into the model to provide information on the likelihood of developing an abdominal aortic aneurysm.

[0002] An abdominal aortic aneurysm (AAA) is characterized by an irreversible localized dilatation of the abdominal aorta. It is defined as a dilatation of the abdominal aorta greater than 1.5 times its normal diameter or greater than 3 cm.

[0003] The prevalence of AAA is reported to be approximately 2-8% worldwide, and in Korea, the prevalence is approximately 2.8%. Generally, AAAs tend to increase in size over time, and as size increases, the risk of rupture also increases. The mortality rate in patients who rupture is reported to be as high as 81%. In particular, the 30-day mortality rate for intact AAAs is 1.16% to 3.27%, but in patients receiving hospital treatment for a ruptured aortic aneurysm, the 30-day mortality rate is reported to be 30.2% to 39.6%. Various studies are being conducted to reduce the rupture rate of abdominal aortic aneurysms and aortic aneurysm-related mortality.

[0004] We propose a method to generate a prediction model for the development of abdominal aortic aneurysms that can selectively identify high-risk patients for AAA from a wider pool than existing screening approaches.

[0005] A method for providing information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment includes the steps of: receiving a plurality of demographic information and personal clinical information; performing learning based on the demographic information and personal clinical information to create an abdominal aortic aneurysm occurrence prediction model; inputting the demographic information and personal clinical information of a subject into the abdominal aortic aneurysm occurrence prediction model; and outputting information on the possibility of occurrence of an abdominal aortic aneurysm of the subject from the abdominal aortic aneurysm occurrence prediction model.

[0006] A computer-readable storage medium recording a program for providing information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment records a program that causes a computer to execute the following operations: receiving a plurality of demographic information and personal clinical information; performing learning based on the demographic information and personal clinical information to generate an abdominal aortic aneurysm occurrence prediction model; inputting demographic information and personal clinical information of a subject into the abdominal aortic aneurysm occurrence prediction model; and outputting information on the possibility of occurrence of an abdominal aortic aneurysm of the subject from the abdominal aortic aneurysm occurrence prediction model.

[0007] A device for providing information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment includes: a storage unit storing a plurality of demographic information and personal clinical information; and a control unit receiving the plurality of demographic information and personal clinical information, performing learning based on the demographic information and personal clinical information to generate an abdominal aortic aneurysm occurrence prediction model, inputting the demographic information and personal clinical information of a subject into the abdominal aortic aneurysm occurrence prediction model, and outputting information on the possibility of occurrence of an abdominal aortic aneurysm of the subject from the abdominal aortic aneurysm occurrence prediction model.

[0008] According to various embodiments of the present disclosure, information provided from an abdominal aortic aneurysm development prediction model with multiple variables as input values ​​and a high AUC value can accelerate the identification of AAA patients, enable timely intervention before complications such as rupture occur, and ultimately improve patient survival rates.

[0009] Figure 1 shows a nomogram for predicting the probability of developing an abdominal aortic aneurysm over a period of one year according to one embodiment.

[0010] Figure 2 shows the ROC curve of a risk prediction model using a development cohort and a validation cohort according to one embodiment.

[0011] Figure 3 shows the predicted incidence using a development cohort and a validation cohort according to one embodiment.

[0012] FIG. 4 illustrates a flow chart of a method for providing information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment.

[0013] FIG. 5 is a schematic diagram of a device that provides information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment.

[0014] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to exemplary drawings. When adding reference numerals to components in each drawing, identical components may have the same numerals as much as possible even if they are shown in different drawings. In addition, when describing the present embodiments, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the technical idea of ​​the present invention, the detailed description may be omitted. When "includes," "has," "consists of," etc. are used in this specification, other parts may be added unless "only" is used. When a component is expressed in the singular, it may include a plural unless there is a special explicit description.

[0015] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the present disclosure. These terms are only intended to distinguish the components from other components, and the nature, order, sequence, or number of the components are not limited by the terms.

[0016] In a description of the positional relationship of components, when it is described that two or more components are "connected," "combined," or "connected," it should be understood that the two or more components may be directly "connected," "combined," or "connected," but that the two or more components may also be further "interposed" with another component to be "connected," "combined," or "connected." Here, the other component may be included in one or more of the two or more components that are "connected," "combined," or "connected" to each other.

[0017] In the description of the temporal flow relationship related to components, operation methods, or manufacturing methods, for example, when the temporal or flow relationship is described as “after”, “following”, “next to”, “before”, etc., it may also include cases where it is not continuous, unless “immediately” or “directly” is used.

[0018] Meanwhile, when numerical values ​​or corresponding information for components are mentioned, even without separate explicit description, the numerical values ​​or corresponding information may be interpreted as including an error range that may occur due to various factors.

[0019] In one embodiment, the abdominal aortic aneurysm (AAA) group may be comprised of patients with multiple AAA diagnosis codes (I71.3-I71.6, I71.8, and I71.9) encountered in outpatient settings. In one embodiment, the abdominal aortic aneurysm (AAA) group may also include individuals who have had recurrent hospitalizations with the AAA codes or who have undergone aneurysm repair procedures, such as open surgical aneurysm repair or endovascular aneurysm repair, as indicated by codes (O0223, O0224, O0234, or M6611, M6612).

[0020] In one embodiment, the diabetes mellitus (DM) group may be comprised of patients with codes (E11-14) who are taking antidiabetic medications or have a fasting blood glucose level of 126 or higher. In one embodiment, based on the fasting blood glucose level, patients may be classified as normal if the level is less than 100, as impaired fasting glucose if the level is 100 to 125, and as DM patients if the level is 126 or higher or if the patient is already taking antidiabetic medication.

[0021] In one embodiment, the Hypertension (HTN) group may include patients with codes (I10-13 and I-15) taking antihypertensive medication, systolic blood pressure (SBP) ≥ 140, or diastolic blood pressure (DBP) ≥ 90. In one embodiment, patients with blood pressure less than 120 / 80 may be classified as normal, 120-140 / 80-90 may be classified as prehypertension (pre-HTN), and 140 / 90 or higher or taking antihypertensive medication may be classified as HTN.

[0022] In one embodiment, the dyslipidemia group may include patients taking antihyperlipidemic drugs or having a total cholesterol level of 240 or higher (code E78). In another embodiment, a total cholesterol level of less than 200 may be classified as normal, 200 to 239 as pre-dyslipidemia, and a level of 240 or higher or a patient taking antihyperlipidemic drugs may be classified as dyslipidemia.

[0023] In one embodiment, the CKD (chronic kidney disease) group may consist of patients with an eGFR < 60 when using the modified MDRD equation.

[0024] In one embodiment, the CVD (cardiocerebrovascular disease) group may include patients with cerebrovascular disease codes (I60-64) and cardiovascular disease codes (I20-25).

[0025] In one embodiment, demographic information may include at least one of gender, age, socioeconomic status, waist circumference, obesity, body mass index (BMI), smoking status, drinking status, and activity status.

[0026] In one embodiment, personal clinical information may include at least one of diabetes mellitus (DM), hypertension (HBP), hyperlipidemia, chronic kidney disease (CKD), and cardiovascular disease (CVD).

[0027] In one embodiment, patients may be classified as non-smokers, former smokers, or current smokers based on their smoking habits.

[0028] In one embodiment, physical activity can be categorized into a group that engages in moderate exercise 5 or more days a week or vigorous exercise 3 or more days a week and a group that does not.

[0029] In one embodiment, income levels can be categorized based on whether they are in the bottom 25%, and also based on whether they have access to medical support.

[0030] In one embodiment, a patient with a BMI ≥ 25 may be classified as obese.

[0031] The above classifications and divisions do not limit the features of the present disclosure, and in various embodiments of the present disclosure, the classifications and divisions may be used with different numbers or criteria, or by adding, correcting, or deleting elements.

[0032] In various embodiments of the present disclosure, the development cohort may be used synonymously with a training set, and the validation cohort may be used synonymously with a test set. In various embodiments of the present disclosure, the abdominal aortic aneurysm occurrence prediction model may be used synonymously with an abdominal aortic aneurysm risk prediction model or a risk prediction model.

[0033] The example data used below are collected from the National Health Insurance Service (NHIS) database in Korea from 2009 to 2020. Developing and validating a predictive model for AAA presence using long-term data from the NHIS database allows for the development of a model that predicts AAA occurrence based on baseline screening results, thereby broadening the screening target, reducing the likelihood of missing at-risk individuals, minimizing unnecessary testing, and ultimately improving cost-effectiveness.

[0034] In a study to develop a risk prediction model, information on 4,234,415 adults was initially registered. Patients diagnosed with AAA during a health checkup were excluded (n=2,409), as were individuals with missing data from the checkup (n=284,471). The AAA patient group was defined using diagnosis and procedure codes. To establish a clear causal relationship, patients who were lost to follow-up within 1 year after the examination or who developed AAA were also excluded (n=10,431). Of these filtered patients (the study group), 70% (2,755,973) were assigned to the development cohort for model training, and the remaining 30% (1,181,131) were assigned to the validation cohort.

[0035] Demographic data, including age, sex, smoking habit, alcohol consumption, physical activity, waist circumference, body mass index (BMI), and income level, were collected from the NHIS database. Information on underlying health conditions (personal clinical information), including history of hypertension, diabetes mellitus (DM), dyslipidemia, chronic kidney disease (CKD), and cardiovascular disease (CVD), was also collected.

[0036] Continuous variables were expressed as mean ± SD or 95% CI, and categorical variables were expressed as numbers and percentages (%). To compare characteristics between the case and control groups, Student's t-test was used for continuous variables, and Chi-squared test or Fisher's exact test was used for categorical variables. The incidence of AAA was expressed as the number of cases per 1,000 population per year. The Cox proportional hazards regression model was used as a statistical analysis method to investigate the hazard ratio (HR) of various variables for the incidence of AAA. Variables were composed of factors associated with AAA.

[0037] For each risk factor identified in the final Cox hazard regression model, a risk score was assigned based on the HR. Each of the 10 variables, including age, sex, obesity, smoking status, alcohol consumption, fasting glucose level, blood pressure, total cholesterol level, CKD, and previous CVD, was assigned a score from 0 to 100, and each variable was then mapped to a specific point on a line extending vertically along the score axis. Calibration and discrimination were performed to evaluate the performance of the model. For calibration, predicted survival was plotted against observed survival to visually inspect alignment. For discrimination, receiver operating characteristic curves were generated and the area under the curve was examined. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R Project for Statistical Computing version 3.3 (Vienna, NC).

[0038] Table 1 presents the clinical characteristics of study participants according to the occurrence of abdominal aortic aneurysm (AAA) in the development and validation cohorts of the prediction model.

[0039] [Table 1]

[0040]

[0041]

[0042]

[0043] Table 1 shows that there were no significant differences in the distribution patterns of all variables between the development and validation cohorts. The mean follow-up period was 10.11 ± 1.28 years, and among 2,755,973 participants in the development cohort, 6,514 (2.36%) developed AAA. The mean age at baseline was 47.22 ± 14.01 years, and 54.56% of patients were male. Compared with the control group, the AAA patient group was older (47.19 ± 14 vs. 62.88 ± 11.3), had a higher proportion of males (54.53% vs. 67.88%), and had a higher BMI (23.7 ± 3.22 vs. 24.25 ± 3.1). In addition, the smoking rate was higher (26.01% vs. 31.98%), and the proportion of patients who consumed alcohol was lower (48.31% vs. 38.52%). The AAA group showed a higher level of physical activity (17.96% vs. 21.08%). In terms of comorbidities, the AAA patient group had a higher incidence of hypertension (25.37% vs. 55.63%), hyperlipidemia (17.34% vs. 32.7%), chronic diseases (6.91% vs. 15.32%), and cardiovascular disease (1.87% vs. 7.52%), and a lower proportion of patients with diabetes (8.66% vs. 12.48%).

[0044] Table 2 shows the risk ratios for developing abdominal aortic aneurysm.

[0045] [Table 2]

[0046]

[0047]

[0048] Table 2 shows the variables in Table 1 that showed significant distribution differences between the AAA group and the control group. After excluding variables with similar significance, a total of 12 variables were selected. The HR for AAA occurrence was then investigated using a Cox proportional hazards regression model. In multivariate analysis, 10 statistically significant variables were ultimately selected, including age, sex, obesity, smoking, drinking, diabetes, hypertension, dyslipidemia, chronic disease, and cardiovascular disease. Older age [HR 30.43 (95% CI 26.48-34.97)], male sex [HR 2.01 (95% CI 1.88-2.16)], obesity [HR 1.06 (95% CI 1.01-1.11)], smoking [HR 2.20 (95% CI 2.05-2.36)], DM [HR 0.64 (95% CI 0.59-0.69)], HTN [HR 2.04 (95% CI 1.89-2.20)], dyslipidemia [HR 1.56 (95% CI 1.47-1.66)], CKD [HR 1.41 (95% CI 1.31-1.51)], CVD [HR 1.50 (95% CI 1.67-1.65)], and it can be seen that all 10 variables are significant predictors of AAA occurrence after adjustment.

[0049] Figure 1 shows a nomogram for predicting the probability of developing abdominal aortic aneurysm over a period of one year.

[0050] Specifically, we present a risk scoring nomogram developed from a risk prediction model to estimate the 1-year risk of AAA. The mean follow-up period was 10.11 ± 1.29 years, and AAA occurred in 2,836 (2.4%) of 1,178,295 participants in the validation cohort. The mean age at baseline was 47.24 ± 14.02 years, and 54.58% of patients were male. In addition, the area under the curve (AUC) value for predicting AAA occurrence in the prediction model was 0.807 (95% CI 0.802–0.812) when applied to the development cohort data, and 0.803 (95% CI 0.795–0.810) when applied to the validation cohort data.

[0051] Figure 2 shows the ROC curve of the risk prediction model using the development cohort and validation cohort.

[0052] In Figure 2, the left curve is from the development cohort, and the right curve is from the validation cohort. Figure 2 demonstrates that the risk prediction model of the present disclosure is effective in predicting the occurrence of AAA.

[0053] Table 3 shows examples of scores for each risk factor variable.

[0054] [Table 3]

[0055]

[0056] In the example in Table 3, the total score is calculated by adding the scores of 10 variables, ranging from 0 to a maximum of 226. For example, a male (20 points) aged 65 years or older (100 points) who smokes (23 points), is of normal weight (0 points), has diabetes (0 points), and has no other underlying diseases (0 points) would have a total score of 143 points. Another example is a 40-year-old female (57 points) who smokes (23 points), is of normal weight (0 points), does not drink (12 points), and does not have diabetes (13 points), but has hypertension (21 points), hyperlipidemia (13 points), and chronic kidney disease (10 points), would have a total score of 149 points. In this case, the probability of developing an abdominal aortic aneurysm within 5 years can be inferred to be less than 0.5%.

[0057] Figure 3 shows the predicted incidence rates using the development cohort and validation cohort.

[0058] In the prediction model in Figure 3, the incidence of AAA in the patient group with a score of 215 or higher, the highest score range, was found to be 1.194 per 1,000 people per year. To assess potential overfitting of the model, the incidence rates in the validation cohort were compared, and similar patterns were observed across each interval.

[0059] In this study, we developed and validated a simple yet effective risk prediction model for AAA occurrence using the Korean NHIS database. This model can predict the likelihood of future AAA occurrence (e.g., within 5 years) using data obtained through follow-up observations over a specified period. This model had a high area under the curve (AUC) of 0.807 (95% CI 0.802-0.812), and specifically, age, male gender, obesity, current smoking, non-drinking, absence of DM, presence of hypertension, hyperlipidemia, and CKD or CVD were identified as independent predictors of increased AAA risk.

[0060] This risk prediction model can target a wide range of patient groups and effectively identify specific individuals within that group. The variables included in this risk prediction model are easily recognizable factors, such as medical history, smoking history, age, and gender, enabling physicians to make more informed decisions and improving patient accessibility. The variables in this risk prediction model are not limited to those listed above, and additional variables can be included to further enhance the model's accuracy.

[0061] FIG. 4 illustrates a flow chart of a method for providing information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment.

[0062] In step 410, multiple demographic and personal clinical information are input. In various embodiments of the present disclosure, the demographic information may include information on age, gender, obesity, smoking, and drinking habits within the target group. In various embodiments of the present disclosure, the personal clinical information may include information on diabetes, hypertension, hyperlipidemia, chronic kidney disease, and cardiovascular disease within the target group.

[0063] In various embodiments of the present disclosure, each element information included in the demographic information and personal clinical information may have a predetermined number of options. For example, information on obesity may have three options (severe obesity / obesity / not obese) based on the BMI value, and information on diabetes may have two options (normal, abnormal) based on the fasting blood glucose level. In various embodiments of the present disclosure, each option in each element information may have a corresponding value (e.g., 0, 1, 2), which may be used as input values ​​for generating an occurrence prediction model, either as is or with modifications (e.g., weighting).

[0064] In step 420, a model for predicting the occurrence of an abdominal aortic aneurysm is generated by performing learning based on the demographic information and personal clinical information. The model for predicting the occurrence of an abdominal aortic aneurysm may be based on machine learning or deep learning. In various embodiments of the present disclosure, the model for predicting the occurrence of an abdominal aortic aneurysm may be configured to include at least one of a Naive Bayes Classification model, a Logistic Regression model, a Decision Tree model, a Random Forest model, a Boosting model, a Perceptron model, a Support Vector Machine model, and a Quadratic Classifier model. In one embodiment, the model for predicting the occurrence of an abdominal aortic aneurysm may be an Elastic-Net Regularized Generalized Linear Model.

[0065] The process of generating the above abdominal aortic aneurysm occurrence prediction model may include a process of generating the abdominal aortic aneurysm occurrence prediction model using demographic information and personal clinical information of a training set in a group of subjects, and a process of verifying the generated abdominal aortic aneurysm occurrence prediction model using demographic information and personal clinical information of a test set in a group of subjects.

[0066] In various embodiments of the present disclosure, the training data of the abdominal aortic aneurysm occurrence prediction model may further include information on whether an abdominal aortic aneurysm occurred within each target group during a predetermined period (e.g., the past 1 year or 5 years). That is, the abdominal aortic aneurysm occurrence prediction model may include, as training data, information on whether an abdominal aortic aneurysm occurred during a predetermined period corresponding to each demographic information and individual clinical information. Alternatively, the demographic information and individual clinical information in steps 410 and 420 may be defined as including information on whether an abdominal aortic aneurysm occurred during a predetermined period within each target group.

[0067] At step 430, the subject's demographic information and personal clinical information for determining the possibility of developing an abdominal aortic aneurysm can be input into the abdominal aortic aneurysm development prediction model.

[0068] In step 440, information regarding the likelihood of developing an abdominal aortic aneurysm in the subject input in step 430 may be output from the abdominal aortic aneurysm prediction model. The information regarding the likelihood of developing an abdominal aortic aneurysm may include information regarding the likelihood of developing an abdominal aortic aneurysm in the subject over a certain period of time (e.g., within the next year or five years). The information regarding the likelihood of developing an abdominal aortic aneurysm may be output as one of a predetermined number of options or as a percentage.

[0069] FIG. 5 is a schematic diagram of a device that provides information on the possibility of occurrence of an abdominal aortic aneurysm according to one embodiment.

[0070] The storage unit (510) can store various data, including multiple demographic and personal clinical information. In various embodiments of the present disclosure, demographic and personal clinical information of the training set in the research subject group can be stored separately from demographic and personal clinical information of the test set.

[0071] The storage unit (510) may include a non-transitory storage medium of at least one type among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD / XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk, and there is no limitation on the type thereof.

[0072] The control unit (520) receives a plurality of demographic information and personal clinical information, performs learning based on the demographic information and personal clinical information to create an abdominal aortic aneurysm occurrence prediction model, inputs the subject's demographic information and personal clinical information into the abdominal aortic aneurysm occurrence prediction model, and outputs information on the possibility of abdominal aortic aneurysm occurrence from the abdominal aortic aneurysm occurrence prediction model.

[0073] The control unit (520) is configured by one or more processors, such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), or a core including the same, and controls the entire device providing information by executing a computer program stored in the storage unit (510). Alternatively, the control unit (520) may control the entire device providing information by cooperation between the computer program stored in the storage unit (510) and an OS (Operating System).

[0074] In addition, the control unit (520) may generate data or signals to be transmitted in communication with other devices. In this case, although not illustrated in FIG. 5, the device providing information on the possibility of occurrence of an abdominal aortic aneurysm may further include a communication unit. The communication unit may connect to another device using a communication module such as Bluetooth or a wired / wireless Local Area Network (LAN) and transmit and receive data. In addition, the device providing information on the possibility of occurrence of an abdominal aortic aneurysm may further include a display unit. In various embodiments of the present disclosure, outputting information on the possibility of occurrence of an abdominal aortic aneurysm from the abdominal aortic aneurysm prediction model may include transmitting information on the possibility of occurrence of an abdominal aortic aneurysm to another device through the communication unit, or displaying the information on the display unit.

[0075] The embodiments described above may be implemented through various means. For example, the embodiments may be implemented through hardware, firmware, software, or a combination thereof.

[0076] In the case of hardware implementation, the method for providing information on the possibility of occurrence of abdominal aortic aneurysm according to the present embodiments may be implemented by one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), processors, controllers, microcontrollers, or microprocessors.

[0077] When implemented via firmware or software, the method for providing information on the possibility of developing an abdominal aortic aneurysm according to the present embodiments may be implemented in the form of a device, procedure, or function that performs the functions or operations described above. The software code may be stored in a memory unit and executed by a processor. The memory unit may be located within or outside the processor and may exchange data with the processor via various known means.

[0078] Additionally, terms such as "system," "processor," "controller," "component," "module," "interface," "model," or "unit" as described above may generally refer to a computer-related entity, such as hardware, a combination of hardware and software, software, or software in execution. For example, the aforementioned components may be, but are not limited to, a process driven by a processor, a processor, a controller, a control processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a controller or a processor and the controller or the processor may be components. One or more components may be within a process and / or thread of execution, and the components may be located on a single device (e.g., a system, a computing device, etc.) or distributed across two or more devices.

[0079] Meanwhile, another embodiment provides a computer program stored on a computer storage medium that performs the method for providing information on the possibility of developing an abdominal aortic aneurysm, as described above. Furthermore, another embodiment provides a computer-readable storage medium storing a program for implementing the method for providing information on the possibility of developing an abdominal aortic aneurysm, as described above. The program recorded on the storage medium can be read, installed, and executed by a computer, thereby executing the steps described above.

[0080] In this way, in order for a computer to read a program recorded on a recording medium and execute functions implemented as a program, the above-mentioned program may include code coded in a computer language such as C, C++, JAVA, or machine language that can be read by the computer's processor (CPU) through the computer's device interface.

[0081] Such code may include functional code related to functions defining the aforementioned functions, and may also include control code related to execution procedures required for the computer's processor to execute the aforementioned functions according to a predetermined procedure.

[0082] Additionally, such code may further include memory reference related code regarding where in the internal or external memory of the computer the additional information or media required for the computer's processor to execute the aforementioned functions should be referenced.

[0083] Additionally, if the computer's processor needs to communicate with another computer or server located remotely in order to execute the functions described above, the code may further include communication-related code regarding how the computer's processor should communicate with another computer or server located remotely using the computer's communication module, and what information or media should be sent and received during the communication.

[0084] The computer-readable recording medium that records the program as described above includes, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical media storage device, etc., and may also include one implemented in the form of a carrier wave (e.g., transmission via the Internet).

[0085] Additionally, computer-readable recording media can be distributed across network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner.

[0086] In addition, the functional program for implementing the present invention and the code and code segments related thereto may be easily inferred or changed by programmers in the technical field to which the present invention belongs, taking into consideration the system environment of the computer that reads the recording medium and executes the program.

[0087] The method for providing information on the possibility of developing an abdominal aortic aneurysm described above can also be implemented in the form of a recording medium containing computer-executable instructions, such as an application or program module executed by a computer. The computer-readable medium can be any available medium that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. Furthermore, the computer-readable medium can include all computer storage media. The computer storage media includes both volatile and nonvolatile, 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.

[0088] The method for providing information on the possibility of occurrence of an abdominal aortic aneurysm described above can be executed by an application installed by default on the terminal (which may include a program included in a platform or operating system installed by default on the terminal), or by an application (i.e., a program) directly installed on the master terminal by the user through an application providing server such as an application store server, an application, or a web server related to the service. In this sense, the method for providing information on the possibility of occurrence of an abdominal aortic aneurysm described above can be implemented by an application (i.e., a program) installed by default on the terminal or directly installed by the user, and recorded on a computer-readable recording medium such as the terminal.

[0089] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0090] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

[0091] The above description is merely an illustrative example of the technical idea of ​​the present disclosure, and those skilled in the art to which the present disclosure pertains will appreciate that various modifications and variations can be made without departing from the essential characteristics of the technical idea of ​​the present disclosure. In addition, the present embodiments are not intended to limit the technical idea of ​​the present disclosure but rather to explain it, and therefore the scope of the technical idea of ​​the present disclosure is not limited by these embodiments. The scope of protection of the present disclosure should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included within the scope of the rights of the present disclosure.

[0092]

[0093] CROSS-REFERENCE TO RELATED APPLICATION

[0094] This patent application claims priority under 35 USC §119(a) to Korean Patent Application No. 10-2024-0034126, filed March 11, 2024, the entire contents of which are incorporated herein by reference. Furthermore, this patent application claims priority in countries other than the United States for the same reasons, the entire contents of which are incorporated herein by reference.

Claims

1. A method for providing information on the possibility of developing an abdominal aortic aneurysm, Step of receiving multiple demographic information and personal clinical information; A step of creating a prediction model for the occurrence of abdominal aortic aneurysm by performing learning based on the above demographic information and personal clinical information; A step of inputting the subject's demographic information and personal clinical information into the abdominal aortic aneurysm occurrence prediction model; and A step of outputting information on the possibility of occurrence of an abdominal aortic aneurysm in the subject from the abdominal aortic aneurysm occurrence prediction model, A method of providing information on the possibility of developing an abdominal aortic aneurysm.

2. In claim 1, the demographic information and personal clinical information are: A method for providing information on the likelihood of developing an abdominal aortic aneurysm, including information on age, sex, obesity, smoking, drinking, diabetes, hypertension, hyperlipidemia, chronic kidney disease, and cardiovascular disease.

3. In claim 1, the abdominal aortic aneurysm occurrence prediction model is, A method for providing information on the possibility of occurrence of an abdominal aortic aneurysm, which includes information on the occurrence of an abdominal aortic aneurysm over a certain period of time as learning data corresponding to each demographic information and individual clinical information.

4. In claim 3, the information on the possibility of occurrence of abdominal aortic aneurysm is outputted as follows: A method for providing information on the possibility of developing an abdominal aortic aneurysm, comprising information on the possibility of developing an abdominal aortic aneurysm in the subject during the above-mentioned period of time in the future.

5. A computer-readable storage medium recording a program that provides information on the possibility of occurrence of an abdominal aortic aneurysm, Action to input multiple demographic information and personal clinical information; An operation of creating a prediction model for the occurrence of abdominal aortic aneurysm by performing learning based on the above demographic information and personal clinical information; An action of inputting the subject's demographic information and personal clinical information into the abdominal aortic aneurysm occurrence prediction model; and An operation of outputting information on the possibility of occurrence of abdominal aortic aneurysm in the above subject from the abdominal aortic aneurysm occurrence prediction model. A computer-readable storage medium that records a program that executes on a computer.

6. In claim 5, the demographic information and personal clinical information are: A computer-readable storage medium having recorded thereon a program containing information on age, gender, obesity, smoking, drinking, diabetes, hypertension, hyperlipidemia, chronic kidney disease, and cardiovascular disease.

7. In claim 5, the abdominal aortic aneurysm occurrence prediction model is, A computer-readable storage medium recording a program that includes, as learning data, information on the occurrence of abdominal aortic aneurysm over a certain period of time corresponding to each demographic information and individual clinical information.

8. In claim 5, the information on the possibility of occurrence of abdominal aortic aneurysm is outputted as follows: A computer-readable storage medium having recorded thereon a program including information on the possibility of occurrence of an abdominal aortic aneurysm in the above subject during the above-mentioned period of time in the future.

9. In a device that provides information on the possibility of occurrence of abdominal aortic aneurysm, A storage unit storing multiple demographic information and personal clinical information; and A control unit that receives the above-mentioned plurality of demographic information and personal clinical information, performs learning based on the above-mentioned demographic information and personal clinical information to create an abdominal aortic aneurysm occurrence prediction model, inputs the subject's demographic information and personal clinical information into the abdominal aortic aneurysm occurrence prediction model, and outputs information on the subject's possibility of developing an abdominal aortic aneurysm from the abdominal aortic aneurysm occurrence prediction model. A device that provides information on the possibility of developing an abdominal aortic aneurysm, including:

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