公开(公告)号：US11410756B2

公开(公告)日：2022-08-09

申请号：US15690714

申请日：2017-08-30

Applicant: Google LLC

Inventor： Eyal Oren ,  Yingwei Cui ,  Gerardo Flores ,  Gavin Duggan ,  Kun Zhang ,  Kurt Litsch ,  Patrik Sundberg ,  Yi Zhang

IPC: G16H50/30 ,  G16H50/20 ,  G16H10/60 ,  G06N3/08 ,  G16H15/00 ,  G06N3/04

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

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：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.