Dialysis event prediction
AssigneeNEC Laboratories America, Inc.
InventorsJingchao Ni, Wei Cheng, Haifeng Chen, Bo Zong
Life of this patent
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Abstract
A method for training a predictive model includes training a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information. An augmented model is trained to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.
Claims20
A method for training a predictive model, comprising:
- training, using a hardware processor, a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information;
- and
- training an augmented model to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.
A system for training a predictive model, comprising:
- a hardware processor;
- and
- a memory that stores a computer program product, which, when executed by the hardware processor, causes the hardware processor to:
- train a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information;
- and
- train an augmented model to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.
Dependencies are inferred from the claim text.