UNSUPERVISED LEARNING-BASED MEDICAL DATA ANALYSIS DEVICE AND METHOD
The present invention relates to an unsupervised learning-based medical data analysis device and method, in which an anomaly in medical data is detected and notified of using a generative adversarial network-based machine learning model, thereby enabling accurate and quick identification, and also e...
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| creator | KO, Jae Yeoung BAE, Hyun Jin |
| description | The present invention relates to an unsupervised learning-based medical data analysis device and method, in which an anomaly in medical data is detected and notified of using a generative adversarial network-based machine learning model, thereby enabling accurate and quick identification, and also enabling the reduction of both time and costs incurring from the identification.
La présente invention concerne un dispositif et un procédé d'analyse de données médicales reposant sur un apprentissage non supervisé, dans lesquels une anomalie dans des données médicales est détectée et notifiée à l'aide d'un modèle d'apprentissage automatique basé sur un réseau antagoniste génératif, ce qui permet de réaliser une identification précise et rapide et de réduire en outre le temps et les coûts liés à l'identification.
본 발명은 비지도 학습 기반 의료 데이터 분석 장치 및 방법에 관한 것으로, 적대적 생성 신경망을 기반으로 하는 기계학습 모델을 이용하여 의료 데이터의 이상을 탐지하여 알려줌으로써, 정확하면서도 신속한 판독이 가능하도록 하는 것은 물론 판독에 의해 발생하는 시간 및 비용을 모두 절감할 수 있도록 한다. |
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La présente invention concerne un dispositif et un procédé d'analyse de données médicales reposant sur un apprentissage non supervisé, dans lesquels une anomalie dans des données médicales est détectée et notifiée à l'aide d'un modèle d'apprentissage automatique basé sur un réseau antagoniste génératif, ce qui permet de réaliser une identification précise et rapide et de réduire en outre le temps et les coûts liés à l'identification.
본 발명은 비지도 학습 기반 의료 데이터 분석 장치 및 방법에 관한 것으로, 적대적 생성 신경망을 기반으로 하는 기계학습 모델을 이용하여 의료 데이터의 이상을 탐지하여 알려줌으로써, 정확하면서도 신속한 판독이 가능하도록 하는 것은 물론 판독에 의해 발생하는 시간 및 비용을 모두 절감할 수 있도록 한다.</description><language>eng ; fre ; kor</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230921&DB=EPODOC&CC=WO&NR=2023176992A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230921&DB=EPODOC&CC=WO&NR=2023176992A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>KO, Jae Yeoung</creatorcontrib><creatorcontrib>BAE, Hyun Jin</creatorcontrib><title>UNSUPERVISED LEARNING-BASED MEDICAL DATA ANALYSIS DEVICE AND METHOD</title><description>The present invention relates to an unsupervised learning-based medical data analysis device and method, in which an anomaly in medical data is detected and notified of using a generative adversarial network-based machine learning model, thereby enabling accurate and quick identification, and also enabling the reduction of both time and costs incurring from the identification.
La présente invention concerne un dispositif et un procédé d'analyse de données médicales reposant sur un apprentissage non supervisé, dans lesquels une anomalie dans des données médicales est détectée et notifiée à l'aide d'un modèle d'apprentissage automatique basé sur un réseau antagoniste génératif, ce qui permet de réaliser une identification précise et rapide et de réduire en outre le temps et les coûts liés à l'identification.
본 발명은 비지도 학습 기반 의료 데이터 분석 장치 및 방법에 관한 것으로, 적대적 생성 신경망을 기반으로 하는 기계학습 모델을 이용하여 의료 데이터의 이상을 탐지하여 알려줌으로써, 정확하면서도 신속한 판독이 가능하도록 하는 것은 물론 판독에 의해 발생하는 시간 및 비용을 모두 절감할 수 있도록 한다.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHAO9QsODXANCvMMdnVR8HF1DPLz9HPXdXIEcX1dXTydHX0UXBxDHBUc_Rx9IoM9gxVcXMM8nV2BfJCCEA9_Fx4G1rTEnOJUXijNzaDs5hri7KGbWpAfn1pckJicmpdaEh_ub2RgZGxobmZpaeRoaEycKgDPDSxG</recordid><startdate>20230921</startdate><enddate>20230921</enddate><creator>KO, Jae Yeoung</creator><creator>BAE, Hyun Jin</creator><scope>EVB</scope></search><sort><creationdate>20230921</creationdate><title>UNSUPERVISED LEARNING-BASED MEDICAL DATA ANALYSIS DEVICE AND METHOD</title><author>KO, Jae Yeoung ; BAE, Hyun Jin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_WO2023176992A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; kor</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>KO, Jae Yeoung</creatorcontrib><creatorcontrib>BAE, Hyun Jin</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>KO, Jae Yeoung</au><au>BAE, Hyun Jin</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>UNSUPERVISED LEARNING-BASED MEDICAL DATA ANALYSIS DEVICE AND METHOD</title><date>2023-09-21</date><risdate>2023</risdate><abstract>The present invention relates to an unsupervised learning-based medical data analysis device and method, in which an anomaly in medical data is detected and notified of using a generative adversarial network-based machine learning model, thereby enabling accurate and quick identification, and also enabling the reduction of both time and costs incurring from the identification.
La présente invention concerne un dispositif et un procédé d'analyse de données médicales reposant sur un apprentissage non supervisé, dans lesquels une anomalie dans des données médicales est détectée et notifiée à l'aide d'un modèle d'apprentissage automatique basé sur un réseau antagoniste génératif, ce qui permet de réaliser une identification précise et rapide et de réduire en outre le temps et les coûts liés à l'identification.
본 발명은 비지도 학습 기반 의료 데이터 분석 장치 및 방법에 관한 것으로, 적대적 생성 신경망을 기반으로 하는 기계학습 모델을 이용하여 의료 데이터의 이상을 탐지하여 알려줌으로써, 정확하면서도 신속한 판독이 가능하도록 하는 것은 물론 판독에 의해 발생하는 시간 및 비용을 모두 절감할 수 있도록 한다.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS |
| title | UNSUPERVISED LEARNING-BASED MEDICAL DATA ANALYSIS DEVICE AND METHOD |
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