Tempus Labs had 11 patents in artificial intelligence during Q4 2023. Tempus Labs Inc filed patents in Q4 2023 for methods and systems involving processing unstructured data to generate structured patient records, validating the records, displaying errors, and receiving revisions. Another patent focuses on determining cardiology disease risk using electrocardiogram trace data and clinical data. Additionally, there are patents for training a machine learning model to analyze H&E slide images and for audibly broadcasting responses to user queries about patient reports. GlobalData’s report on Tempus Labs gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Tempus Labs grant share with artificial intelligence as a theme is 9% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Transforming unstructured patient data streams using schema mapping and concept mapping with quality testing and user feedback mechanisms (Patent ID: US20230360752A1)
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Tempus Labs Inc. has developed a method and system for improving automated quality assurance testing of structured patient data by transforming unstructured data streams. The method involves receiving unstructured patient data, processing it to create structured patient records through schema and concept mapping, validating these records using quality tests, displaying errors or incomplete information, and allowing for revisions to the unstructured data. This process is repeated until the structured patient records meet a specified accuracy threshold, with the option to generate a digital abstraction of a patient record if needed. The system includes processors and memory with instructions for executing these steps, ensuring data quality and accuracy in clinical trials and other medical applications.
The method also involves identifying medical ontological terms within the unstructured data using natural language processing, mapping data elements and values within a schema, and determining mappings between a data model and medical concepts. Quality tests are performed using recursive or iterative algorithms, conflict resolution procedures, and specific tests like genetic testing criteria. Trigger events prompt the method, which can include on-demand requests, software code commits, data ingestion, and completion of quality assurance activities. The system can train machine learning models based on the structured patient records, generate definitive clinical records, and continuously monitor and display errors for user review and revision. This comprehensive approach ensures the accuracy and reliability of structured patient data derived from unstructured sources, benefiting medical research, patient care, and clinical decision-making processes.
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