Abstract: A method includes: receiving first data including physiological information obtained from a subject and a first result that a disease is developing; specifying a first time point at which the physiological information included in the first data is obtained; receiving second data including the physiological information obtained from the subject and a second result that the disease is not developing; specifying a second time point at which the physiological information included in the second data is obtained; upon determining that a time interval between the first time point and the second time point is smaller than a first predetermined value, assigning, to the second data, a first training label indicating that the disease is developing and a weighting index that is capable of taking a plurality of values according to the time interval; and performing machine learning of a model by using the second data as training data.

Abstract: A processing device includes an interface configured to receive electrocardiographic waveform data corresponding to an electrocardiographic waveform of a subject, and a processor configured to divide the electrocardiographic waveform into a plurality of partial electrocardiographic waveforms, based on the electrocardiographic waveform data, calculate a probability that a waveform portion in which a heart disease is suspected is included in each of the plurality of partial electrocardiographic waveforms, and cause a display to display an index together with information, the index corresponding to the probability, the information corresponding to the partial electrocardiographic waveform from which the probability is calculated.

Abstract: A physiological information acquisition apparatus that acquires physiological information of a subject includes a reception device configured to receive waveform data corresponding to a measurement waveform of the physiological information from a sensor, and to acquire values of a plurality of characteristic parameters associated with the measurement waveform based on the waveform data, a processing device configured to input the values of the plurality of characteristic parameters to a machine-learned model to acquire a prediction result for at least one of a plurality of classes into which the waveform data is classified, and to specify a level of importance of each of the plurality of characteristic parameters for the prediction result, and an output device configured to output an index indicating a name of at least one of the plurality of characteristic parameters and the level of importance specified for the at least one characteristic parameter.

Abstract: A method includes: receiving first data including physiological information obtained from a subject and a first result that a disease is developing; specifying a first time point at which the physiological information included in the first data is obtained; receiving second data including the physiological information obtained from the subject and a second result that the disease is not developing; specifying a second time point at which the physiological information included in the second data is obtained; upon determining that a time interval between the first time point and the second time point is smaller than a first predetermined value, assigning, to the second data, a first training label indicating that the disease is developing and a weighting index that is capable of taking a plurality of values according to the time interval; and performing machine learning of a model by using the second data as training data.

Abstract: A patch comprising a support layer and an adhesive agent layer, wherein the adhesive agent layer comprises at least one drug selected from the group consisting of emedastine and pharmaceutically acceptable salts thereof, at least one adhesive agent selected from the group consisting of rubber-based adhesive agents and silicone-based adhesive agents, and an alkali metal fumarate as a cohesive force-improving agent for the adhesive agent layer.

Abstract: A patch comprising a support layer and an adhesive agent layer, wherein the adhesive agent layer comprises at least one drug selected from the group consisting of emedastine and pharmaceutically acceptable salts thereof, at least one adhesive agent selected from the group consisting of rubber-based adhesive agents and silicone-based adhesive agents, and an alkali metal fumarate as a cohesive force-improving agent for the adhesive agent layer.

Abstract: A GLP-1 analogue composition for a microneedle device, including a GLP-1 analogue and at least one kind of solvent selected from the group consisting of water, glycerin, propylene glycol, ethylene glycol, 1,3-butylene glycol, and polyethylene glycol.