KR20210062473A - Intelligent healthcare assistance system - Google Patents
Intelligent healthcare assistance system Download PDFInfo
- Publication number
- KR20210062473A KR20210062473A KR1020190150737A KR20190150737A KR20210062473A KR 20210062473 A KR20210062473 A KR 20210062473A KR 1020190150737 A KR1020190150737 A KR 1020190150737A KR 20190150737 A KR20190150737 A KR 20190150737A KR 20210062473 A KR20210062473 A KR 20210062473A
- Authority
- KR
- South Korea
- Prior art keywords
- user
- data
- health
- assistance system
- intelligent
- Prior art date
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
Description
본 발명은 지능형 건강관리 보조 시스템에 관한 것이다.The present invention relates to an intelligent health care assistance system.
현재 점차적으로 의료 기술이 발달하게 되면서 전세계적으로 인구의 노령화가 <1> 사회적으로 문제가 되고 있으며 그로 인하여 독거노인의 수가 증가하고 있는 실정이다. 통계청의 자료에 의하면 이러한 노인들의 약 75%가 고위험군 환자이거나 성인병 및Metabolic Syndrome(대사증후군)으로 분류되고 있다. 이와 같은 질병은 우리의 나쁜 생활습관을 통하여 발생되는 질병들이며 이는 다른 질병들과 합병증으로 동반될 수 있는 질병들이다. 따라서 지속적인 관리가 필요한 질병들이다. 그러나 현재의 의료서비스로서는 사용자 및 환자를 실시간으로 모니터링 할 수 없음으로 지속적인 관리가 불가능한 실정이다. 또한 의료에서 실시하는 표준은 모든 사람의 통계적인 데이터를 이용하기 때문에 각각의 사람에게는 틀리는 경우도 발생한다. 예를 들면 노인의 경우 혈압이 140 / 100 임에도 불구하고 의사들은 어르신이니까 그럴수도 있다고 하는경우인데 이는 의료인의 관점에서 본다면 고혈압으로 판명된다. 그러나 만약 자신의 평균적인 상태를 지속적으로 학습하고 있고 만약 이 상태에서 벗어나게 된다면 건강상태가 이상하다는 것을 확인 할 수 있다면 이것은 자신에 맞는 건강상태를 보다 구체적으로 확인 할 수 있다는 결론을 얻게 된다. 그러나 지금까지의 기술에서는 많은 어려움이 존재하였으나 최근 정보 통신 기술 발달하게 되면서 점차 사람을 중심으로 한 유비쿼터스 환경에 대한 관심이 높아지고 있는 가운데 본 연구에서는 의료 도메인과 IT기술을 접목한 사람중심의 Wellbeing lifecare 서비스가 필요함을 인지하였고 그 중에서도 가정에서 자신의 생체 신호를 배경으로 한 지능형 건강관리 시스템의 필요성을 느끼게 되었다.Currently, as medical technology is gradually developed, the aging of the population worldwide is becoming a social problem. As a result, the number of elderly people living alone is increasing. According to data from the National Statistical Office, about 75% of these elderly patients are high-risk patients or are classified as adult diseases and metabolic syndrome (metabolic syndrome). Diseases such as these are diseases that occur through our bad lifestyle, and these are diseases that can accompany other diseases and complications. Therefore, they are diseases that require continuous management. However, as the current medical service, users and patients cannot be monitored in real time, so continuous management is impossible. In addition, since the standard practiced in medical care uses statistical data of everyone, there are cases where each person is different. For example, in the case of the elderly, even though the blood pressure is 140 / 100, doctors say that because they are elderly, it is possible, but from a medical professional's point of view, it turns out to be high blood pressure. However, if you are constantly learning about your average state, and if you can confirm that your health is abnormal if you get out of this state, you can come to the conclusion that you can check your health status more specifically. However, there have been many difficulties in the technology so far, but with the recent development of information and communication technology, interest in the human-centered ubiquitous environment is gradually increasing. He recognized the necessity of a health care system, and among them, he felt the need for an intelligent health management system based on his bio-signals at home.
본 발명의 목적은 사용자 자신의 생활패턴에 맞는 일반적인 건강상태 확인하기 위하여 개발된 시스템으로, 사람이 살아가면서 생체신호가 발생하게 되는데 이에 따른 데이터를 지능형 알고리즘인 신경망 알고리즘을 이용하여 학습하게 되고 이를 기반으로 자신의 생체신호에 따른 건강상태를 체크할 수 있는 지능형 건강관리 보조 시스템을 제공하는 것이다.An object of the present invention is a system developed to check a general health condition suitable for a user's own life pattern, and a bio-signal is generated while a person lives, and the data according to this is learned using a neural network algorithm, which is an intelligent algorithm, and is based on this. As a result, it provides an intelligent health management assistance system that can check health status according to one's own biometric signals.
본 발명에 따른 지능형 건강관리 보조 시스템은, 신경망 알고리즘으로 구성되어 있으며 입력단자에는 생체신호와 활동량 그리고 음식의 영양소를 입력하게 되며 학습 데이터는 의사나 혹은 전문가가 진단한 위험수위 1~3가 선정하여 이용하게 되며 사용자가 생활을 하면서 측정하게 되는 데이터는 학습시킨 데이터를 통하여 발생된 가중치를 통하여 자신의 건강상태를 확인할 수 있다.The intelligent health care assistance system according to the present invention is composed of a neural network algorithm, and biosignals, activity levels, and nutrients of food are input to the input terminal, and the learning data is selected by
본 발명에 따른 지능형 건강관리 보조 시스템에 의하면, 사용자에 일반적인 생체리듬을 학습한 모델을 이용하였기 때문에 사용자 개개인에 맞는 맞춤형 진단 지원이 가능하다는 현저한 효과가 있다.The intelligent health care assistance system according to the present invention has a remarkable effect in that it is possible to support customized diagnosis tailored to each user because a model that has learned a general physiological rhythm for a user is used.
또한, 본 발명에 따른 시스템은 자신의 생체신호 및 생활패턴 데이터를 지속적으로 획득하여 이를 학습하여 지속적으로 측정하는 데이터의 형태가 현재의 상태와 벗어나게 되면 건강상태의 이상을 확인해 줄 수 있어, 건강상태 지원 시스템이라고 명명할 수 있다.In addition, the system according to the present invention continuously acquires its own biosignal and life pattern data, learns it, and can check for abnormalities in the health state when the form of the data to be continuously measured deviates from the current state. It can be called a support system.
도 1은 본 발명을 설명하기 위한 도면.
도 2는 신경망 시스템의 예시도.1 is a view for explaining the present invention.
2 is an exemplary diagram of a neural network system.
도 1은 본 발명을 설명하기 위한 도면이며, 도 2는 신경망 시스템의 예시도이다.1 is a diagram for explaining the present invention, and FIG. 2 is an exemplary diagram of a neural network system.
본 발명에 따른 지능형 건강관리 보조 시스템은 신경망 알고리즘으로 구성되어 있으며 입력단자에는 생체신호와 활동량 그리고 음식의 영양소를 입력하게 되며 학습 데이터는 의사나 혹은 전문가가 진단한 위험수위 1~3가 선정하여 이용하게 되며 사용자가 생활을 하면서 측정하게 되는 데이터는 학습시킨 데이터를 통하여 발생된 가중치를 통하여 자신의 건강상태를 확인할 수 있다.The intelligent health management assistance system according to the present invention is composed of a neural network algorithm, and bio-signals, activity levels, and nutrients of food are input to the input terminal, and the learning data is selected and used by
본 발명은 사람의 건강상태를 생활습관을 통하여 확인할 수 있는 컴퓨터 기반의 건강상태 지원 시스템을 의사결정 AGENT를 통하여 구성하였다. 본 건강상태 지원 시스템은 신경망 알고리즘을 통하여 구축되어 있다.In the present invention, a computer-based health state support system that can check a person's health state through lifestyle habits is constructed through a decision-making AGENT. This health state support system is built through a neural network algorithm.
의료 진단 에이전트는 최초 실행 시 DB 에이전트에서 관리하고 있는 데이터베이스의 정보를 이용하여 DB 에이전트에 자료를 요청하고 요청된 자료를 이용하여 학습을 하게 된다. 이를 통해 학습된 연결강도(Weight)들은 학습 시간을 단축하기 위해 별도로 내부 컴퓨터에서 관리하도록 한다. 학습이 끝난 후, 진단 에이전트는 DB 에이전트에서 제공하는 데이터베이스의 정보를 일정한 시간마다 감시를 한다. 감시 중 환자의 측정 데이터가 입력되면 그 데이터를 요청하고 진단한 후 그 결과를 다시 데이터베이스에 입력 요청을 한다. 또한 전문의로부터 환자의 생체 데이터에 대한 진단이 이루어지면 다시 그 진단 데이터를 학습하게 된다.When the medical diagnosis agent is first executed, it requests data from the DB agent using the information in the database managed by the DB agent, and learns using the requested data. In order to shorten the learning time, the learned weights are separately managed by an internal computer. After learning is complete, the diagnostic agent monitors the database information provided by the DB agent at regular intervals. If the patient's measurement data is input during monitoring, the data is requested, diagnosed, and the result is requested to be entered back into the database. In addition, when the patient's biometric data is diagnosed by a specialist, the diagnostic data is learned again.
신경망 알고리즘 (Back Propagation)Neural Network Algorithm (Back Propagation)
역전파 알고리즘(Backpropagation)은 지도학습(Supervised learning)모델로 입력값과 목표값이 각 뉴런이 연결된 연결강도(Weight)를 조절함으로써 이루어지는데 이것은 출력값(Output value)과 목표값(Target value)을 비교하여 오차를 줄여가는 방향으로 진행함으로써 학습이 되어간다. 전방향 역전파 알고리즘은 각 학습패턴에 따른 입력벡터가 입력층에 주어지면 이들 값으로부터 은닉층의 노드값들이 구해지게 되고 다시 이들 은닉노드들의 값으로부터 출력노드들의 값이 구해지게 된다. 이후 출력값과 주어진 목표값과 비교하여 에러를 계산한다. 이러한 에러를 0에 가까운 최소값으로 만들기 위해 출력된 값과 목표 출력치와의 차이인 에러에 근거하여, 즉 출력층과 은닉층 사이의 연결강도를 수정한 다음 역방향으로 은닉층과 입력층 사이의 연결강도를 수정한다.Backpropagation is a supervised learning model, where the input value and the target value are controlled by adjusting the weight of the connection to which each neuron is connected, which compares the output value and the target value. Therefore, learning is conducted by proceeding in the direction of reducing the error. In the forward backward propagation algorithm, when an input vector according to each learning pattern is given to the input layer, the node values of the hidden layer are obtained from these values, and the values of the output nodes are obtained again from the values of these hidden nodes. Then, the error is calculated by comparing the output value with the given target value. In order to make these errors a minimum value close to zero, based on the error that is the difference between the output value and the target output value, that is, correct the connection strength between the output layer and the hidden layer, and then correct the connection strength between the hidden layer and the input layer in the reverse direction. do.
입력데이터 속성Input data attribute
1.흡연 유무 및 흡연량1. Smoking or not and the amount of smoking
2.수면시간2. Sleep time
3. 수면 만족도3. Sleep satisfaction
4. 하루 운동량 (만보계를 이용)4. Amount of exercise per day (using a pedometer)
5. 생체신호 (현재 사용자의 혈압, 혈당 등의 기초 생체 신호)5. Bio-signals (basic bio-signals such as current user's blood pressure and blood sugar)
Target 데이터Target data
위험상태 Level 1~3
Level 1: 정상Level 1: Normal
Level 2: 조심Level 2: Be careful
Level 3: 위험Level 3: Risk
Claims (4)
A user-customized health condition support system that learns the user's health condition in the u-health industry's customized health condition support service.
The system of claim 1, wherein a doctor determines a patient's health status and continuously learns and utilizes it.
A system that checks and supports the patient's basic health condition through the user's life pattern.
A system that does not support various people, but only supports the user himself.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020190150737A KR20210062473A (en) | 2019-11-21 | 2019-11-21 | Intelligent healthcare assistance system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020190150737A KR20210062473A (en) | 2019-11-21 | 2019-11-21 | Intelligent healthcare assistance system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| KR20210062473A true KR20210062473A (en) | 2021-05-31 |
Family
ID=76150251
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020190150737A KR20210062473A (en) | 2019-11-21 | 2019-11-21 | Intelligent healthcare assistance system |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR20210062473A (en) |
-
2019
- 2019-11-21 KR KR1020190150737A patent/KR20210062473A/en not_active Application Discontinuation
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5465250B2 (en) | Detection of decompensated congestive heart failure | |
| US8740789B2 (en) | Automatic etiology sequencing system and method | |
| US9968266B2 (en) | Risk stratification based heart failure detection algorithm | |
| JP5443376B2 (en) | Medical equipment | |
| US9022930B2 (en) | Inter-relation between within-patient decompensation detection algorithm and between-patient stratifier to manage HF patients in a more efficient manner | |
| US8223023B2 (en) | Within-patient algorithm to predict heart failure decompensation | |
| US10311533B2 (en) | Method and system to enable physician labels on a remote server and use labels to verify and improve algorithm results | |
| Van Loon et al. | Non-invasive continuous respiratory monitoring on general hospital wards: a systematic review | |
| Dhruba et al. | Development of an IoT-based sleep apnea monitoring system for healthcare applications | |
| US10555699B2 (en) | Method and device for the non-invasive montioring and identification of drug effects and interactions | |
| KR20110137001A (en) | Health risk warning system | |
| Shawqi Jaber et al. | Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks | |
| US20070156624A1 (en) | System and method of patient specific vital sign estimation | |
| CN109147906B (en) | Method and terminal for evaluating surgical anesthesia quality and device with storage function | |
| KR20210062473A (en) | Intelligent healthcare assistance system | |
| US20220211332A1 (en) | Medical device system for monitoring patient health | |
| Wong et al. | Probabilistic detection of vital sign abnormality with Gaussian process regression | |
| KR20090049426A (en) | User customized health decision support system service | |
| Wendelken et al. | A medical assessment algorithm for automated remote triage | |
| Agrawal et al. | Adaptive neuro-fuzzy inference system for health monitoring at home | |
| EP4287197A1 (en) | Computer implemented method for determining a drug dose, training method and system | |
| US20230395261A1 (en) | Method and system for automatically determining a quantifiable score | |
| Sharma | Body Monitoring System Using Rule Based Sensor Networks | |
| TW202238620A (en) | Self-physiological information index system applied to clinical health management and its analysis method having the self-data module including the basic value, evaluation value, and early warning report signal | |
| CN115798739A (en) | Multimode fusion intelligent perception early warning method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| E601 | Decision to refuse application |