Patent ID: 11875277
Assignee: KONINKLIJKE PHILIPS N.V.
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 6:
7. A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
analyzing electronic medical records stored in a retrospective patient database, wherein the electronic medical records are associated with a plurality of entities;
generating one or more context training data sets from a training database, wherein the one or more context training data sets include individual training examples, and wherein the individual training examples are labeled with one or more context labels;
generating a plurality of template similarity functions, wherein the plurality of template similarity functions are generated at least partially based on the individual training examples;
providing one or more template similarity functions of the plurality of template similarity functions, wherein each template similarity function of the one or more template similarity functions compares a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector;
providing a composite similarity function as a weighted combination of respective outputs of the one or more template similarity functions;
providing a first plurality of labeled entity vectors as first context training data;
applying an approximation function to approximate, for each respective labeled entity vector of the first context training data, a first context label for the respective labeled entity vector data based on an output of the composite similarity function and respective first context labels of the other labeled entity vectors of the first context training data; and
training a first context specific composite similarity function of a context-specific machine learning model based on the composite similarity function, wherein training the first context specific composite similarity function includes learning first context weights for the plurality of template similarity functions using a first loss function based on an output of application of the approximation function to the first context training data, wherein the first context weights are stored for use as part of the first context-specific composite similarity function;
receiving a query entity, wherein the query entity is associated with one or more entity feature vectors; and
applying the trained first context-specific similarity function to the query entity, wherein the trained first context-specific similarity function filters a plurality of candidate entity feature vectors to identify one or more candidate entity feature vectors at least meeting a similarity threshold with the one or more entity feature vectors.