Systems and methods for quantifying patient improvement through artificial intelligence
AssigneesRehabilitation Institute of Chicago d/b/a Shirley Ryan AbilityLab, Northwestern University
InventorsStephen A. Antos, Konrad P. Kording, Vivek Sagar
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Abstract
Examples of a system and methods for quantifying patient improvement via artificial intelligence are disclosed. In general, via at least one processing element, a machine learning model such as a Siamese neural network is trained in view of a cost function to learn on average a maximum difference in outcomes between a patient at different points in time. Given the architecture of the neural network, a plurality of outcome measures generated for a given point in time can be condensed into a single score.
Claims15
A method of quantifying rehabilitative progress via artificial intelligence, comprising:
- accessing, by a computing device, a first dataset of input data for one or more outcome measures derived from a patient at a first point in time of rehabilitation;
- accessing, by the computing device, a second dataset of the input data for the one or more outcome measures derived from the patient at a second point in time of the rehabilitation;
- and
- generating, by the computing device applying the first dataset and the second dataset as inputs to a machine learning model, an output including a machine learning score that infers improvement of the patient from the first point in time to the second point in time, the machine learning model trained to map the inputs to the output to minimize a cost function defined by the machine learning model and maximize the dissimilarity between the patient between the first point in time and the second point in time.
Dependencies are inferred from the claim text.