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公开(公告)号：US11354321B2
公开(公告)日：2022-06-07
申请号：US16556680
申请日：2019-08-30
IPC分类号： G06F16/245 , G06F16/248 , G06F16/2457 , G16H10/60 , G16H15/00 , G16H50/20
摘要： A mechanism is provided for re-ranking search results based on a personal medical condition. One or more medical conditions associated with a patient are identified based on an analysis of patient information associated with the patient. The one or more medical conditions are correlated with a set of medical condition content indicator data structures. A search query corresponding to the patient is processed to generate initial search results that are ranked with an initial ranking. The initial search results are analyzed based on a patient specific dictionary data structure corresponding to the one or more medical conditions associated with the patient. The content of the initial search results are re-ranked to generate re-ranked search results having a modified ranking that is based on the one or more medical conditions of the patient. The re-ranked search results are output to the patient in accordance with the modified ranking.
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公开(公告)号：US11322234B2
公开(公告)日：2022-05-03
申请号：US16521985
申请日：2019-07-25
IPC分类号： G16H10/60 , G06N20/00 , G16H30/40 , G06N7/00 , G06F40/205 , G06F40/242
摘要： A medical condition based content filter mechanism is provided that analyzes patient information associated with a patient to identify medical condition(s) associated with the patient and correlating the medical condition(s) with one or more medical condition content indicator data structures specifying negative content indicators and/or positive content indicators. A user specific content indicator dictionary data structure (USCID) is generated based on the correlation and used to process received content to filter out portions of the content matching the negative content indicators or present portions of the content matching the positive content indicators, thereby generating modified content that is output to the patient via a content access application executing on a computing device associated with the patient.
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公开(公告)号：US20210089616A1
公开(公告)日：2021-03-25
申请号：US16576906
申请日：2019-09-20
IPC分类号： G06F17/22 , G06F17/27 , G06F17/24 , G06N5/04 , G06F16/332
摘要： Mechanisms are provided to perform selective deep parsing of natural language content. A targeted deep parse natural language processing system is configured to recognize one or more triggers that specify elements within natural language content that indicate a portion of natural language content that is to be targeted with a deep parse operation. A portion of natural language content is received and a pre-deep parse scan operation is performed on the natural language content based on the one or more triggers to identify one or more sub-portions of the natural language content that contain at least one of the one or more triggers. A deep parse is performed on only the one or more sub-portions of the portion of natural language content that contain at least one of the one or more triggers, while other sub-portions of the portion of natural language content are not deep parsed.
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公开(公告)号：US20210027868A1
公开(公告)日：2021-01-28
申请号：US16521985
申请日：2019-07-25
摘要： A medical condition based content filter mechanism is provided that analyzes patient information associated with a patient to identify medical condition(s) associated with the patient and correlating the medical condition(s) with one or more medical condition content indicator data structures specifying negative content indicators and/or positive content indicators. A user specific content indicator dictionary data structure (USCID) is generated based on the correlation and used to process received content to filter out portions of the content matching the negative content indicators or present portions of the content matching the positive content indicators, thereby generating modified content that is output to the patient via a content access application executing on a computing device associated with the patient.
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公开(公告)号：US10380251B2
公开(公告)日：2019-08-13
申请号：US15261328
申请日：2016-09-09
摘要： A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a cognitive natural language processing system. The cognitive natural language processing (NLP) system analyzes a portion of natural language text to identify an attribute specified in the natural language text. The cognitive NLP system analyzes the portion of natural language text to determine whether a known negation trigger is present in the natural language text in association with the attribute. In response to determining that the natural language text does not contain a known negation trigger in association with the attribute, the cognitive NLP system determines whether the attribute is negated based on instances of the attribute in other natural language content similar to the natural language text. In response to determining that the attribute is negated, the cognitive NLP system identifies a new negation trigger associated with the attribute in the natural language text. The cognitive NLP system stores the new negation trigger in association with the attribute in a negation trigger dictionary data structure.
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公开(公告)号：US20190042559A1
公开(公告)日：2019-02-07
申请号：US15666694
申请日：2017-08-02
CPC分类号： G06F17/278 , G06F16/3344 , G06F16/9024 , G06F17/2705 , G06N5/02 , G06N20/00
摘要： The program directs a computer processor to resolve an anaphor in electronic natural language text. The program detects a plurality of entities and an anaphor in a span of parsed natural language text comprising one or more sentences, and extracts pairs of related entities based on domain knowledge. The program constructs a set of tuples, wherein each tuple is a data type comprising an anaphor, an antecedent entity (AE) appearing before the anaphor in the span of parsed natural language text, and an entity (E) appearing after the anaphor in the span of parsed natural language text, wherein the anaphor refers to the AE and relates the AE to the E. The program resolves the anaphor by determining which entity in the plurality of entities the anaphor references, using the constructed set of tuples, and selecting an AE among one or more candidate AEs.
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公开(公告)号：US20180075011A1
公开(公告)日：2018-03-15
申请号：US15263889
申请日：2016-09-13
CPC分类号： G16H10/60 , G06F17/2735 , G06F17/2755 , G06F17/2775 , G16H50/20 , G16H50/30 , G16H50/50 , G16H50/70 , G16H70/20
摘要： Mechanisms are provided for processing natural language content. The mechanisms receive natural language content and analyze the natural language content to generate a parse tree data structure. The mechanisms process the parse tree data structure to identify one or more instances of hypothetical spans in the natural language content. The hypothetical spans are terms or phrases indicative of a hypothetical statement. The mechanisms perform an operation based on the natural language content. The operation is performed with portions of the natural language content corresponding to the one or more identified instances of hypothetical spans being given different relative weights within portions of the natural language content than other portions of the natural language content.
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公开(公告)号：US11423223B2
公开(公告)日：2022-08-23
申请号：US16699996
申请日：2019-12-02
IPC分类号： G06F40/242 , G06F40/30 , G06N20/00 , G06N5/04
摘要： A mechanism is provided to implement a cognitive dictionary builder. The mechanism configures the cognitive dictionary builder with a set of selection criteria comprising a set of rules. The mechanism performs natural language processing on an input document in a corpus of information to analyze a context for each term or phrase in the input document and applies the set of rules to each term or phrase in the input document with respect to its context. The mechanism adds a term or phrase to at least one corresponding dictionary data structure based on a result of applying the set of rules.
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公开(公告)号：US11113418B2
公开(公告)日：2021-09-07
申请号：US16205423
申请日：2018-11-30
摘要： A method for de-identifying protected health information (PHI) associated with electronic medical records (EMRs) based on a common analysis structure (CAS) is provided. The method may include detecting a system event associated with a system comprising the EMRs. The method may further include in response to detecting the system event, detecting a first CAS associated with the EMRs. The method may further include extracting first CAS data associated with the first CAS, wherein the first CAS data comprises unstructured data associated with the EMRs and normalized annotations based on CAS objects that are associated with the unstructured data. The method may further include obfuscating the unstructured data associated with the first CAS. The method may also include generating a second CAS comprising the obfuscated unstructured data and a copied version of the normalized annotations, wherein the copied version of normalized annotations are correlated with the obfuscated unstructured data.
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公开(公告)号：US10937551B2
公开(公告)日：2021-03-02
申请号：US15822651
申请日：2017-11-27
IPC分类号： G16H50/70 , G16H50/20 , G06F40/295
摘要： Mechanisms are provided for performing entity differentiation. A cognitive medical system ingests a corpus of medical content having references to medical entities, and performs entity recognition on the medical content to identify the medical entities. Responsive to the cognitive medical system identifying a medical entity having a plurality of annotations for a same medical entity attribute, an entity differentiation component executes an ordered set of entity differentiation algorithms, corresponding to the medical entity, for differentiating medical entity attribute values. The entity differentiation component runs the ordered set of entity differentiation algorithms, in order, on the plurality of annotations for the attribute to generate a ranked list of medical entity attribute values corresponding to the annotations in the plurality of annotations. The cognitive medical system performs a cognitive operation on the medical entity based on the ranked list of medical entity attribute values.
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