Flatiron Health‘s patent involves using machine learning to automatically extract clinical variable values from clinical record data. The method includes generating a GUI to assign values to variables, with some designated as hybrid variables predicted by machine learning. The GUI restricts user modification based on confidence scores. GlobalData’s report on Flatiron Health gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Flatiron Health, was a key innovation area identified from patents.
Machine learning for extracting clinical variable values from clinical records
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A recently granted patent (Publication Number: US11915807B1) outlines a method that utilizes machine learning to configure a graphical user interface (GUI) for extracting values of clinical variables from clinical record data for multiple subjects. The method involves generating a GUI that allows for assigning values to clinical variables, distinguishing between hybrid variables that can be assigned values through machine learning or manual extraction, and non-hybrid variables that cannot be predicted. The GUI is configured based on machine learning model predictions, with restrictions on user modifications for predicted values. The system includes a processor and computer-readable storage medium to execute the method efficiently.
The patent also details the system's ability to restrict user modifications of predicted values through the GUI, including features like removing modification interfaces, generating graphical elements for user confirmation, and preventing user input in certain interfaces. Additionally, the GUI is designed to indicate when a value is machine learning model predicted, providing transparency to users. The method and system aim to streamline the extraction of clinical data by leveraging machine learning models to predict values for hybrid variables, enhancing efficiency and accuracy in clinical data processing. This innovative approach could revolutionize the way clinical data is handled, ensuring a more seamless and reliable extraction process for healthcare professionals dealing with large datasets.
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