CPC G16H 40/20 (2018.01) [G06N 3/02 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G06V 10/774 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G06N 20/00 (2019.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30104 (2013.01); G06V 2201/03 (2022.01)] | 20 Claims |
1. A method comprising:
generating a plurality of multi-modal data archetypes using an encoder neural network that has been jointly trained along with a decoder neural network, wherein:
the encoder neural network is configured to process input multi-modal data characterizing an input patient to generate an embedding of the input multi-modal data in a multi-dimensional latent space;
the decoder neural network is configured to process the embedding of the input multi-modal data to generate a reconstruction of the input multi-modal data; and
generating the plurality of multi-modal data archetypes comprises:
processing, for each patient in a population of patients, multi-modal data characterizing the patient using the encoder neural network to generate an embedding of the multi-modal data in the latent space,
wherein the embeddings of multi-modal data characterizing the patients in the population of patients collectively define a set of embeddings in the latent space;
processing the set of embeddings in the latent space to generate a set of parameters defining a convex hull of the set of embeddings in the latent space;
processing: (i) the set of parameters defining the convex hull of the set of embeddings in the latent space, and (ii) the set of embeddings, to identify a proper subset of the embeddings in the set of embeddings as being archetype embeddings; and
identifying the respective multi-modal data represented by each archetype embedding as a respective multi-modal data archetype.
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