US 12,481,882 B2
Superclass-conditional gaussian mixture model for personalized prediction on dialysis events
Jingchao Ni, Princeton, NJ (US); Wei Cheng, Princeton Junction, NJ (US); and Haifeng Chen, West Windsor, NJ (US)
Assigned to NEC Corporation, Tokyo (JP)
Filed by NEC Laboratories America, Inc., Princeton, NJ (US)
Filed on Sep. 19, 2023, as Appl. No. 18/370,129.
Application 18/370,129 is a continuation of application No. 17/950,203, filed on Sep. 22, 2022.
Claims priority of provisional application 63/397,060, filed on Aug. 11, 2022.
Claims priority of provisional application 63/247,335, filed on Sep. 23, 2021.
Prior Publication US 2024/0005155 A1, Jan. 4, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06N 3/044 (2023.01); G06N 7/01 (2023.01); G16H 20/10 (2018.01); G16H 50/70 (2018.01)
CPC G06N 3/08 (2013.01) [G06N 3/044 (2023.01); G06N 7/01 (2023.01); G16H 20/10 (2018.01); G16H 50/70 (2018.01)] 13 Claims
OG exemplary drawing
 
1. A computer-implemented method for model building, comprising:
receiving a training set of medical records and model hyperparameters;
initializing an encoder, as a Dual-Channel Combiner Network (DCCN), and distribution related parameters;
building a model using a forward computation to (1) the encoder to obtain embeddings of the medical records by using a multilayer perceptron (MLP) of the DCCN to encode static patient profiles of the medical records and one or more Long Short-Term Memories (LSTMs) of the DCCN to encode temporal patient status features of the medical records, and (2) the distribution related parameters to obtain membership probabilities of a plurality of classes;
manipulating the embeddings of the medical records with the distribution related parameters to enable adaptation of the model to a fine-grained multi-class task;
performing iterative optimization of the model between (1) a step for obtaining posterior probabilities indicative of membership probabilities of the embeddings in one or more subclasses of each class of the plurality of classes, and (2) a step for obtaining updated encoder and distribution related model parameters while fixing one or more of the obtained posterior probabilities;
personalizing the model responsive to convergence of the iterative optimization with a final set of updated encoder and distribution related model parameters by encoding support data of a new patient to obtain new embeddings and using the new embeddings to train a personalized classifier for the new patient that is configured to perform the fine-grained multi-class task; and
performing model testing by encoding test data of the new patient to obtain test embedding and using the test embeddings and the personalized classifier to predict event subtypes.