Nihon Kohden Corporation

Hiroto Sano

Wataru Matsuzawa

Takuya Kawashima

Naoki Kobayashi

Takashi Mori

Katsuyoshi Suzuki

Mitsuhiro Oura

Kazuya Nagase

Yoshihiro Takayanagi

Sou Kumagai

Nobuyuki Yasumaru

A patient monitor () acquires a vital sign that is based on a physiological signal of a subject, and an ultrasonic image that is based on a reflected wave of an ultrasonic wave which is irradiated onto the subject. A display section () displays information of the subject. A control section () switches between a first mode in which a screen containing information of the vital sign is displayed on the display section (), and a second mode in which a screen containing the ultrasonic image is displayed on the display section ().

Patient monitor, physiological information measurement system, program to be used in patient monitor, and non-transitory computer readable medium in which program to be used in patient monitor is stored

A fluid balance management system includes: a detection device configured to output a detection signal corresponding to a load applied to a bed in which a subject is present; a prediction device configured to acquire load variation information indicating a variation over time of the load based on the detection signal, and predict an event that causes variation in fluid balance of the subject from the load variation information; and an output device configured to output prediction information corresponding to a prediction result of the event.

Fluid balance management system, prediction device, learned model generation device, and learned model generation method

A method for generating a learned model applied to a prediction device that predicts a probability that a subject develops delirium based on a moving image in which the subject appears is provided. The method includes: acquiring first data corresponding to the moving image in which the subject appears; generating, based on the first data, second data corresponding to changes over time in relative positions of a plurality of feature points in a body of the subject in the moving image; generating third data indicating a determination result as to whether the subject develops delirium based on the moving image; and generating the learned model by causing a neural network to learn using the second data and the third data.

Method for generating learned model, system for generating learned model, prediction device, and prediction system

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Tokorozawa, Japan

Hiroto Sano