Patent AU-2019240633-A1 (AU)
Title: System for automated analysis of clinical text for pharmacovigilance
Inventor: MAITRA ANUTOSH
IPC: G16H10/60
Filed: 2019-10-02
Abstract: A system including a server including a processor, memory coupled with the processor, a plurality of databases coupled with the processor, and a data interface, the memory having stored thereon processor executable instructions including a data virtualization and query module, an ontology framework module, a search framework module, a natural language processing (NLP) pipeline, and a rules engine, where the data virtualization and query module, when executed by the processor, is operable to enable the system to interface with the plurality of databases and to access data from multiple heterogeneous data sources, wherein the plurality of databases are updated and maintained by separate organizations and the system is operable to access the databases, and wherein the data visualization and query module enables the NLP pipeline to access the plurality of databases, the plurality of databases including a medical record database, where the medical record database contains medical information to be processed by the system, and where the medical information includes textual data and is received from different sources and in different formats, a curated reference and clinical knowledge database, a coding database, a causal and temporal model database, and a curated drug safety and case database, the ontology framework module when executed by the processor creates an initial custom ontology, the server configured to periodically scan the medical record database to detect changes or additions to the medical record database and communicate the changes or additions to the ontology framework to augment the initial custom ontology and thereby create a custom ontology, the search framework module, when executed by the processor, enables search capabilities for the system, the NLP pipeline, in connection with the ontology framework module and data virtualization and query module, when executed by the processor accesses one or more of the medical record databases, curated reference and clinical knowledge databases, and the causal and temporal model databases in performing language processing functions, and processes, by applying the custom ontology, the medical information in the medical record database to identify semantic classes for terms in the textual data by matching the terms in the textual data to attributes associated with the semantic classes, wherein the semantic classes include a medical substance class, a medical condition class, and a time class, the time class representative of a time value term included in the textual data, wherein a medical substance term associated with the medical substance class and a medical condition term associated with the medical condition class are in different sentences of the textual data, identify one or more semantic relationships in the textual data by matching the one or more identified semantic classes to a particular relationship pattern, identify one or more discourse parameters in the textual data, where the discourse parameter includes one or more temporal discourse parameters indicative of a temporal order of the medical substance term and the medical condition term in the different sentences of the textual data with respect to the time value term, wherein the discourse parameter does not include the time value term, and generate a structured representation of natural language text including a list of notation markers and temporal markers, the notation markers representative of the identified semantic relationships, the temporal markers indicative of the temporal order of the terms in the different sentences of the textual data, the temporal markers attached to the notation markers, and wherein the rules engine instructions and the data virtualization and query instructions, cause the processor to access one or more of the coding databases including, the causal and temporal model database, and the curated drug safety and case database and determine a clinical event based on execution of a plurality of rule instructions linked together, wherein each of the rule instructions access the structured representation of natural language text to identify the semantic relationships and the temporal order of the substance term and the medical term in the natural language text, and wherein the data interface includes intelligent text analysis platform instructions, stored in memory, that, when executed by the processor, cause the processor to generate, in response to determination of the clinical event, one or more intelligent text analysis user interfaces, the text analysis user interfaces including a temporal chain user interface, the temporal chain user interface including a plurality of medical event labels descriptive of the identified semantic relationships, the medical event labels sequentially arranged on the temporal chain user interface based on the temporal markers of the structured representation of natural language text. -- ------ 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