Vuno, Inc.

Samsung Electronics Co., Ltd.

Hyewon Medical Foundation

Yeha Lee

Hyun-Jun Kim

Sangki Kim

Heeyoul Choi

Kyuhwan Jung

Jihyun Lee

Hoshik Lee

Sang Hyun Yoo

Hodong Lee

Woong Bae

YoungSang Choi

Oyeon Kwon

Yunhong Min

Taesup Moon

Joonmyoung Kwon

Yeongnam Lee

Disclosed is a portable electrocardiogram measuring device for calculating one or more electrocardiogram leads according to an embodiment of the present disclosure. The device may include: a main measurement unit comprising a first electrode, a second electrode, and one or more processors; and a sub measurement unit comprising a third electrode, in which the one or more processors measure an electrocardiogram, by receiving electrical signals from at least two electrodes in a measurable state and by calculating different types of electrocardiogram leads based on the number of electrodes in the measurable state and an attachment position of electrodes.

Portable ECG measuring device

The present invention relates to a method for generating a prediction result for predicting an occurrence of fatal symptoms of a subject in advance, a method for performing data classification by using data augmentation in mechanical learning for the same, and a computing device using the same. Particularly, the computing device according to the present invention acquires vital signs of the subject, converts the same into individuated data, generates analysis information from the individuated data on the basis of a machine learning model, generates a prediction result by referring to the analysis information, and provides the prediction result to an external entity.

Method for generating prediction result for predicting occurrence of fatal symptoms of subject in advance and device using same

Provided are a method for determining whether an examinee is infected by microorganism, and a determination apparatus using the same. Specifically, the determination apparatus according to the present invention obtains an microphotographed image of a biological sample of the examinee; receives the obtained microphotographed image and generates analysis information on the microorganism based on a deep learning model of the examinee; visualize the generated analysis information to provide it, so as to perform at least one of (i) a process of supporting a remote reading on whether the microorganism corresponding to the analysis information exists or not, and (ii) a process of supporting a user of the computing apparatus to read whether the microorganism corresponding to the analysis information exists or not; and provides a final result as its result.

Method for determining whether examinee is infected by microoganism and apparatus using the same

A neural network training method based on training data, includes receiving training data including sequential data, and selecting a reference hidden node from hidden nodes in a neural network. The method further includes training the neural network based on remaining hidden nodes obtained by excluding the reference hidden node from the hidden nodes, and based on the training data, the remaining hidden nodes being connected with hidden nodes in a different time interval, and a connection between the reference hidden node and the hidden nodes in the different time interval being ignored.

Neural network training method and apparatus, and data processing apparatus

According to one embodiment, a method for increasing the reading efficiency of a medical image is provided. The method of increasing the reading efficiency of a medical image comprises of: receiving the gaze information of a user, acquiring a gaze tracking device, during a medical image reading process; determining a region of interest of the user with respect to the medical image by using the gaze information; determining a type of service corresponding to the region of interest; and providing the determined service.

Method for increasing reading efficiency in medical image reading process using gaze information of user and apparatus using the same

A method and apparatus for training a language model, include generating a first training feature vector sequence and a second training feature vector sequence from training data. The method is configured to perform forward estimation of a neural network based on the first training feature vector sequence, and perform backward estimation of the neural network based on the second training feature vector sequence. The method is further configured to train a language model based on a result of the forward estimation and a result of the backward estimation.

Method and apparatus for training language model, and method and apparatus for recognizing language

According to an embodiment, a method of providing a medical information service is provided. The method for providing a medical information service comprises the steps of: receiving a target image; extracting feature data of the target image; discovering a relative position of the feature data in a disease classification map in which a pre-trained reference image has been quantified; and providing a user with the disease classification map in which the relative position of the feature data has been discovered.

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Gyeonggi-do, South Korea

Yeha Lee