CN111798944A - 基于emr/ehr和心电图的心脏异常分析多模式框架 - Google Patents
基于emr/ehr和心电图的心脏异常分析多模式框架 Download PDFInfo
- Publication number
- CN111798944A CN111798944A CN202010127654.6A CN202010127654A CN111798944A CN 111798944 A CN111798944 A CN 111798944A CN 202010127654 A CN202010127654 A CN 202010127654A CN 111798944 A CN111798944 A CN 111798944A
- Authority
- CN
- China
- Prior art keywords
- information
- learning
- representations
- signal
- generated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 50
- 230000000747 cardiac effect Effects 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 42
- 230000036541 health Effects 0.000 claims abstract description 8
- 239000013598 vector Substances 0.000 claims description 17
- 238000013527 convolutional neural network Methods 0.000 claims description 13
- 238000012706 support-vector machine Methods 0.000 claims description 12
- 238000007637 random forest analysis Methods 0.000 claims description 8
- 230000005856 abnormality Effects 0.000 claims description 7
- 238000013135 deep learning Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000002565 electrocardiography Methods 0.000 description 46
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000012549 training Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 4
- 238000004195 computer-aided diagnosis Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 208000004476 Acute Coronary Syndrome Diseases 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 206010003658 Atrial Fibrillation Diseases 0.000 description 1
- 108091005515 EGF module-containing mucin-like hormone receptors Proteins 0.000 description 1
- 238000003070 Statistical process control Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000007596 consolidation process Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002567 electromyography Methods 0.000 description 1
- 238000002079 electron magnetic resonance spectroscopy Methods 0.000 description 1
- 238000002001 electrophysiology Methods 0.000 description 1
- 230000007831 electrophysiology Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004984 smart glass Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
-
- 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
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Evolutionary Computation (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
Description
Claims (14)
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/378,772 US20200327985A1 (en) | 2019-04-09 | 2019-04-09 | Multimodal framework for heart abnormalities analysis based on emr/ehr and electrocardiography |
US16/378,772 | 2019-04-09 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111798944A true CN111798944A (zh) | 2020-10-20 |
Family
ID=72747528
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010127654.6A Pending CN111798944A (zh) | 2019-04-09 | 2020-02-28 | 基于emr/ehr和心电图的心脏异常分析多模式框架 |
Country Status (2)
Country | Link |
---|---|
US (1) | US20200327985A1 (zh) |
CN (1) | CN111798944A (zh) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113509185A (zh) * | 2021-04-25 | 2021-10-19 | 安徽十锎信息科技有限公司 | 基于多模态病人信息注意力建模的心肌梗塞分类方法 |
TWI834286B (zh) * | 2022-09-12 | 2024-03-01 | 長庚醫療財團法人林口長庚紀念醫院 | 重大心血管疾病預測系統及重大心血管疾病預測方法 |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112818315B (zh) * | 2021-02-26 | 2022-03-15 | 山东大学 | 一种融合多特征信息的心电信号身份识别方法及系统 |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106901723A (zh) * | 2017-04-20 | 2017-06-30 | 济南浪潮高新科技投资发展有限公司 | 一种心电图异常自动诊断方法 |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11514244B2 (en) * | 2015-11-11 | 2022-11-29 | Adobe Inc. | Structured knowledge modeling and extraction from images |
JP6450032B2 (ja) * | 2016-01-27 | 2019-01-09 | 日本電信電話株式会社 | 作成装置、作成方法、および作成プログラム |
-
2019
- 2019-04-09 US US16/378,772 patent/US20200327985A1/en not_active Abandoned
-
2020
- 2020-02-28 CN CN202010127654.6A patent/CN111798944A/zh active Pending
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106901723A (zh) * | 2017-04-20 | 2017-06-30 | 济南浪潮高新科技投资发展有限公司 | 一种心电图异常自动诊断方法 |
Non-Patent Citations (2)
Title |
---|
ZHEN-JIE YAO 等: "Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey", INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING, vol. 15, no. 6, 31 December 2018 (2018-12-31), pages 643 - 655, XP036635912, DOI: 10.1007/s11633-018-1136-9 * |
胡丹青: "基于电子病历的急性冠脉综合征患者主要不良心血管事件预测", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 8, 15 August 2017 (2017-08-15), pages 062 - 39 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113509185A (zh) * | 2021-04-25 | 2021-10-19 | 安徽十锎信息科技有限公司 | 基于多模态病人信息注意力建模的心肌梗塞分类方法 |
TWI834286B (zh) * | 2022-09-12 | 2024-03-01 | 長庚醫療財團法人林口長庚紀念醫院 | 重大心血管疾病預測系統及重大心血管疾病預測方法 |
Also Published As
Publication number | Publication date |
---|---|
US20200327985A1 (en) | 2020-10-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jahmunah et al. | Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals | |
Sevakula et al. | State‐of‐the‐art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system | |
CN111798944A (zh) | 基于emr/ehr和心电图的心脏异常分析多模式框架 | |
CA3069891A1 (en) | Discovering novel features to use in machine learning techniques, such as machine learning techniques for diagnosing medical conditions | |
Xiong et al. | Deep learning for detecting and locating myocardial infarction by electrocardiogram: A literature review | |
CN107714024A (zh) | 用于监测心脏活动的方法、系统及装置 | |
CN111095232A (zh) | 发掘用于机器学习技术中的基因组 | |
US11596344B2 (en) | Deep neural network on ECG Poincare plot for atrial fibrillation classification | |
Li et al. | Enabling health monitoring as a service in the cloud | |
CN111358460A (zh) | 心律失常的识别方法、装置及电子设备 | |
KR101977745B1 (ko) | 심장 부정맥을 분류하기 위한 방법 및 장치 | |
CN113693611B (zh) | 基于机器学习的心电数据分类方法及装置 | |
Ilbeigipour et al. | Real‐Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming | |
Rouhani et al. | A comparison of different feature extraction methods for diagnosis of valvular heart diseases using PCG signals | |
US11521743B2 (en) | Framework for performing electrocardiography analysis | |
US11763944B2 (en) | System and method for clinical decision support system with inquiry based on reinforcement learning | |
KR102589471B1 (ko) | 데이터 증강 장치 및 방법 | |
US11672467B2 (en) | User device based Parkinson's disease detection | |
US11986307B2 (en) | Digital signal processing assistant modeling method for biosignal analysis | |
US11450424B2 (en) | Training framework for multi-group electrocardiography (MG-ECG) analysis | |
Feng et al. | A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition | |
Gan et al. | An arrhythmia classification method based on deep learning parallel network model | |
Vu et al. | Real-time arrhythmia detection using convolutional neural networks | |
US11324457B2 (en) | Explainable artificial intelligence framework for electrocardiography analysis | |
JP2018110725A (ja) | 波形表示装置、波形表示方法、プログラム及び記憶媒体 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Du Nan Inventor after: Wang Kun Inventor after: Tu Min Inventor after: Zhang Shangqing Inventor after: Tang Hui Inventor after: Lin Siyao Inventor after: Fan Wei Inventor before: Du Nan Inventor before: Wang Kun Inventor before: Tu Min Inventor before: Zhang Shangqing Inventor before: Tan Hui Inventor before: Lin Siyao Inventor before: Fan Wei |