公开(公告)号：US20210065066A1

公开(公告)日：2021-03-04

申请号：US17008338

申请日：2020-08-31

Applicant: Google LLC

Inventor： Yuan Xue ,  Dengyong Zhou ,  Nan Du ,  Andrew Mingbo Dai ,  Zhen Xu ,  Kun Zhang ,  Yingwei Cui

IPC: G06N20/20 ,  G06N3/02 ,  G06F17/18 ,  G06F17/16

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

公开(公告)号：US20230334306A1

公开(公告)日：2023-10-19

申请号：US16794087

申请日：2020-02-18

Applicant: Google LLC

Inventor： Kun Zhang ,  Andrew M. Dai ,  Yuan Xue ,  Alvin Rishi Rajkomar ,  Gerardo Flores

IPC: G16H10/60 ,  G06N3/08

CPC classification number: G06N3/08 ,  G16H10/60

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using a recurrent neural network. In particular, at each time step, a network input for the time step is processed using a recurrent neural network to update a hidden state of the recurrent neural network. Specifically, the hidden state of the recurrent neural network is partitioned into a plurality of partitions and the plurality of partitions comprises a respective partition for each of a plurality of possible observational features.

公开(公告)号：US20250118401A1

公开(公告)日：2025-04-10

申请号：US17143083

申请日：2021-01-06

Applicant: Google LLC

Inventor： Edward Choi ,  Andrew M. Dai ,  Gerardo Flores ,  Yuan Xue ,  Michael Ward Dusenberry ,  Zhen Xu ,  Yujia Li

IPC: G16H15/00 ,  G06N3/08 ,  G16H50/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing data about a medical encounter using neural networks. One of the methods includes obtaining features for a medical encounter associated with the patient, each feature representing a corresponding health event associated with the medical encounter and each of the plurality of features belonging to a vocabulary of possible features that each represent a different health event; and generating respective final embeddings for each of the features for the medical encounter by applying a sequence of one or more self-attention blocks to the features for the medical encounter, wherein each of the one or more self-attention blocks receives a respective block input for each of the features and applies self-attention over the block inputs to generate a respective block output for each of the features.

公开(公告)号：US20250045577A1

公开(公告)日：2025-02-06

申请号：US18697304

申请日：2021-10-05

Applicant: Google LLC

Inventor： Bo Dai ,  Hanjun Dai ,  Yuan Xue ,  Zia Syed ,  Dale Eric Schuurmans

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing stochastic optimization using machine learning. One of the methods includes obtaining data defining a multi-stage stochastic optimization (MSSO) problem instance, the data characterizing an observation distribution, an action space, and a cost function; generating a neural network input characterizing the MSSO problem instance from the data; providing the neural network input as input to a neural network that generates, from the network input, a neural network output characterizing parameters of a value function corresponding to the MSSO problem instance; processing the neural network input using the neural network to generate the neural network output; obtaining a new observation determined according to the observation distribution for the MSSO problem instance; determining, using the value function characterized by the network output, an optimal action to take in response to the new observation; and executing the optimal action.

公开(公告)号：US12217144B2

公开(公告)日：2025-02-04

申请号：US17008338

申请日：2020-08-31

Applicant: Google LLC

Inventor： Yuan Xue ,  Dengyong Zhou ,  Nan Du ,  Andrew Mingbo Dai ,  Zhen Xu ,  Kun Zhang ,  Yingwei Cui

IPC: G06N20/20 ,  G06F17/16 ,  G06F17/18 ,  G06N3/02

Abstract: A deep state space generative model is augmented with intervention prediction. The state space model provides a principled way to capture the interactions among observations, interventions, critical event occurrences, true states, and associated uncertainty. The state space model can include a discrete-time hazard rate model that provides flexible fitting of general survival time distributions. The state space model can output a joint prediction of event risk, observation and intervention trajectories based on patterns in temporal progressions, and correlations between past measurements and interventions.

公开(公告)号：US20240112013A1

公开(公告)日：2024-04-04

申请号：US17951889

申请日：2022-09-23

Applicant: Google LLC

Inventor： Hanjun Dai ,  Bo Dai ,  Mengjiao Yang ,  Yuan Xue ,  Dale Eric Schuurmans

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0472

Abstract: The present disclosure is directed to generative models for datasets constrained by marginal constraints. One method includes receiving a request to generate a target dataset based on a marginal constraint for a source dataset. A first object occurs at a source frequency in the source dataset. The marginal constraint indicates a target frequency for the first object. The source dataset encodes a set of co-occurrence frequencies for a plurality of object pairs. A source generative model is accessed. The source generative model includes a first module and a second module that are trained on the source dataset. The second module is updated based on the marginal constraint. An adapted generative model is generated that includes the first module and the updated second module. The target dataset is generated based on the adapted generative model. The first object occurs at the target frequency in the target dataset. The target dataset encodes the set of co-occurrence frequencies for the plurality of object pairs.