公开(公告)号：US20240161930A1

公开(公告)日：2024-05-16

申请号：US18231110

申请日：2023-08-07

Applicant: VUNO Inc.

Inventor： Yunseob SHIN ,  Yunwon TAE ,  Kyungjae CHO ,  Jaewoo CHOI

IPC: G16H50/30 ,  G16H50/50 ,  G16H50/70

CPC classification number: G16H50/30 ,  G16H50/50 ,  G16H50/70

Abstract: Disclosed is a method for generating a prediction result by using static data and dynamic data according to an exemplary embodiment of the present disclosure. Specifically, according to the present disclosure, a computing device generates an integrated feature vector from static data and dynamic data of input data by using an artificial neural network model. The computing device generates a dynamic feature vector from the dynamic data of the input data by using the artificial neural network model. The computing device generates a final prediction result of the artificial neural network model based on the integrated feature vector and the dynamic feature vector.

公开(公告)号：US20220084681A1

公开(公告)日：2022-03-17

申请号：US17474059

申请日：2021-09-14

Applicant: VUNO INC.

Inventor： Kyungjae CHO ,  Yunseob SHIN ,  Woong BAE

IPC: G16H50/30 ,  G16H50/20 ,  G16H10/60 ,  G16H40/67 ,  G06N3/02 ,  G06N7/00

Abstract: A method of predicting a medical event based on a pre-trained artificial neural network by a computing apparatus, and an apparatus therefor are disclosed. The method includes receiving an electronic medical record vector including a plurality of vital sign components, and outputting the medical event corresponding to the electronic medical record vector using the acritical neural network. The artificial neural network is pre-trained based on learning data, and the learning data includes augmentation electronic medical record vectors which are reconstructed using original electronic medical record vectors pre-acquired at an earlier time point than a first time point based on a mask vector for losing at least one of the plurality of vital sign components of the first time point.