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1. 发明授权

US11537797B2 Hierarchical entity recognition and semantic modeling framework for information extraction 有权
公开(公告)号：US11537797B2
公开(公告)日：2022-12-27
申请号：US16762684
申请日：2018-12-24
申请人： KONINKLIJKE PHILIPS N.V.
发明人： Yiyi Hu , En Ouyang , Zuofeng Li
IPC分类号： G06F40/295 , G06N20/00 , G06F40/284 , G06F40/205 , G06F40/242 , G06F40/154 , G06F40/30 , G06F40/211 , G06F40/289
摘要： Extracting entities from a document with a hierarchical entity graph of entities. Entity definitions and entity recognition definitions are customized by a user and provided. The configuration information is utilized to generate (905) an entity graph, which is then utilized to parse one or more documents. In some implementations, the resulting parse tree may be utilized, in conjunction with user feedback, to generate one or more training instances for a machine learning model assigned to one or more of the custom nodes as an entity recognition definition. Parsing of the resulting tree may be performed with a lazy parsing methodology, with only the portions of interest to the user being identified in the document.

2. 发明申请

US20210202111A1 METHOD OF CLASSIFYING MEDICAL RECORDS 有权
公开(公告)号：US20210202111A1
公开(公告)日：2021-07-01
申请号：US17273798
申请日：2019-09-03
申请人： KONINKLIJKE PHILIPS N.V.
发明人： Zuofeng Li , Dong Wen
IPC分类号： G16H50/70 , G16H10/60
摘要： A method for organizing medical record data based on classification of a set of medical records in accordance with an indexing intervention event identified for each record, associated with a medical intervention. The method is based on extracting for each of a plurality of medical records one or more candidate intervention events, and then mapping these to a dataset (or ontology) of standard intervention event names (indexing intervention events) in order to identify a closest matching indexing event for each extracted intervention event. The mapping is based on breaking down each extracted intervention event into a set of characterizing attributes of particular domains or types and then comparing these with corresponding attribute sets for each of the indexing events in the dataset. A closest match is found, and each medical record is classified according to the closest matching indexing event. Data is then aggregated based on the classifications, and also based on information about a user, e.g. a particular clinical area of expertise.

3. 发明申请

US20200342991A1 DETECTING RECURRENCE OF A MEDICAL CONDITION 审中-公开
公开(公告)号：US20200342991A1
公开(公告)日：2020-10-29
申请号：US16962088
申请日：2019-01-16
申请人： KONINKLIJKE PHILIPS N.V.
发明人： Yiyi Hu , Yan Li , Zuofeng Li , Huichuan Sun , Mao Hua Yang , Jian Zhou , Jia Fan
IPC分类号： G16H50/20 , G16H10/60 , G16H50/70 , G16H10/20 , G16H70/00 , G06F16/25 , G06F16/242 , A61B5/00 , G06F40/20 , G06F40/40
摘要： Presented are concepts for detecting recurrence of a medical condition in a subject previously treated for the medical condition. One such concept comprises, using natural language processing, extracting information from a data set comprising information relating to the subject prior to and/or during the subject's previous treatment for the medical condition. The extracted information is reformulated into structured data based on a standardized model. A data retrieval request comprising a structured query adhering to a template is obtained and, based on the structured query of the data retrieval request, data is retrieved from the structured data.

4. 发明申请

US20170357946A1 Clinical knowledge driven healthcare scheduling 审中-公开
公开(公告)号：US20170357946A1
公开(公告)日：2017-12-14
申请号：US14572984
申请日：2014-12-17
申请人： KONINKLIJKE PHILIPS N.V.
发明人： Qin Zhu , Yihui Fan , Zuofeng Li , Richard Vdovjak
IPC分类号： G06Q10/10 , G06Q50/22
CPC分类号： G06Q10/1095 , G06Q50/22
摘要： Embodiments of the present invention relate to clinical knowledge driven healthcare scheduling. There is disclosed a method of healthcare scheduling, comprising receiving a request to generate an appointment for a target user, the request at least containing identification of the target user; obtaining clinical knowledge associated with the target user by accessing a knowledge base, the clinical knowledge at least indicating time constraints and at least one required resource for the appointment; generating a group of prioritized candidate appointments by processing the request at least partially based on the obtained clinical knowledge, each of the candidate appointments being represented at least by appointment time and a priority; and selecting at least one recommended appointment from the group of prioritized candidate appointments based on availability of the at least one required resource. Corresponding apparatus, system and computer program product are also disclosed.

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