# US 2022/0285035 A1 · Device and method of predicting disease by using elderly cohort data

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- Number: US 2022/0285035 A1
- Document ID: US20220285035A1
- Status: Published application
- Kind: A1
- Application number: 17/689,654
- Filed: 2022-03-08
- Published: 2022-09-08
- Earliest claimed: 2021-03-08
- Assignees: Electronics and Telecommunications Research Institute; Korea Research Institute of Standards and Science
- Applicants: Electronics and Telecommunications Research Institute; Korea Research Institute of Standards and Science
- Inventors: Jae Hak YU; Se Jin PARK; Soon Hyun KWON; Nae Soo KIM; Da Mee KIM; Cheol Sig PYO; Seung Hee Hong
- Web page: https://patentgrep.com/patent/US20220285035A1

## CPC

- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- A61B 5/4842: {Monitoring progression or stage of a disease}
- A61B 5/7267: {involving training the classification device}
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

The present invention relates to a device and method of predicting disease by using elderly cohort data, and more particularly, to a device and method of predicting disease by using elderly cohort data and an elderly disease prediction model applied thereto, which may predict an outbreak possibility of an elderly disease including cerebral stroke by using cohort data of 60 or more-year-old persons.

## Claims

### Claim 1

A method of predicting disease by using elderly cohort data, the method comprising:
collecting cohort data of an elderly group;
preprocessing the collected cohort data;
extracting an attribute in the collected cohort data and selecting a subset corresponding to the extracted attribute; and
analyzing a degree of risk of a disease on the basis of the selected attribute set by using a disease prediction model.

### Claim 2 (depends on 1)

The method of claim 1, wherein the preprocessing comprises generating a main data table associated with a disease which is to be predicted.

### Claim 3 (depends on 2)

The method of claim 2, wherein the preprocessing comprises constructing a data mart including a data table associated with a main disease code of the disease which is to be predicted, on the basis of joining of the generated main data table.

### Claim 4 (depends on 1)

The method of claim 1, wherein the collecting of the cohort data comprises:
periodically updating the cohort data stored in a database; and
previously teaching the disease prediction model on the basis of the updated cohort data of the database.

### Claim 5 (depends on 1)

The method of claim 1, wherein the selecting of the subset comprises performing Z-score normalization based on the following Equation on the attribute extracted from the collected cohort data.
x
i
→
=
x
i
-
μ
σ
×
α
where xi denotes each attribute, σ denotes a standard deviation of x, μ denotes an average of x, and α denotes a weight value.

### Claim 6 (depends on 1)

The method of claim 1, wherein the selecting of the subset comprises selecting a subset of attributes extracted from the cohort data by using Hall's theorem.

### Claim 7 (depends on 1)

The method of claim 1, wherein the selecting of the subset comprises evaluating a subset, where a largest value is calculated as a result of the calculation based on the following Equation, as a subset where an expression rate of all attributes is highest,
Merit
(
F
S
)
=
k
⁢
r
cf
k
+
k
⁡
(
k
-
1
)
⁢
r
ff
_
where Fs denotes a subset, k denotes the number of attributes of Fz, rcf denotes an average distribution of attributes included in Fs, and rff denotes an average correlation value of all attributes.

### Claim 8

A device for predicting disease by using elderly cohort data, the device comprising:
a data collector configured to collect cohort data of an elderly group;
a data preprocessor configured to preprocess the collected cohort data;
a subset selector configured to extract an attribute in the collected cohort data and select a subset corresponding to the extracted attribute; and
a disease analyzer configured to analyze a degree of risk of a disease on the basis of the selected attribute set by using a disease prediction model.

### Claim 9 (depends on 8)

The device of claim 8, wherein the data collector periodically updating the cohort data stored in a database to previously teach the disease prediction model on the basis of the updated cohort data.

### Claim 10 (depends on 8)

The device of claim 8, wherein the data preprocessor removes a repeated tuple and a noise tuple in each data table included in the cohort data and converts and normalizes a data format so as to enable analysis through the disease prediction model.

### Claim 11 (depends on 8)

The device of claim 8, wherein the subset selector calculates and selects a subset where a probability distribution calculated in a case which uses all attributes extracted from the cohort data and a similar probability distribution are calculated, in performing data classification.

### Claim 12 (depends on 8)

The device of claim 8, wherein the disease prediction model is constructed as a prediction model based on a 1D convolution neural network (CNN).

### Claim 13

A method of generating a disease prediction model based on a 1D convolution neural network (CNN) structure by using cohort data of an elderly group, the method comprising:
placing a pooling layer and a convolution layer extracting a feature of the cohort data preprocessed and input; and
placing a hidden layer for classifying the cohort data.

### Claim 14 (depends on 13)

The method of claim 13, wherein the placing of the pooling layer and the convolution layer comprises placing three convolution layers and three pooling layers.

### Claim 15 (depends on 13)

The method of claim 13, wherein the placing of the hidden layer comprises placing two fully connected layers where all nodes are connected to one another.

### Claim 16 (depends on 13)

The method of claim 13, wherein the placing of the hidden layer comprises placing a softmax layer which is disposed at a final position of the hidden layer and evaluates a probability value associated with target disease prediction.

### Claim 17 (depends on 13)

The method of claim 13, wherein the placing of the pooling layer and the convolution layer comprises using a rectified linear unit (ReLU) activation function between each convolution layer and each pooling layer and applying batch normalization.