Abstract: Provided is a store device for digital therapeutic object including a processor, a storage unit configured to store a digital therapeutic object, and a memory electrically connected to the processor, at least one piece of code executed by the processor being stored in the memory, in which the memory stores the code causing the processor to process an XML-based DPR message requesting any one of registration, update of registration, abandonment of registration, usage, and abandonment of usage of the digital therapeutic object from a first external device.

Abstract: Disclosed is an implantable wireless electrocardiogram sensor device, including: a cylindrical capsule provided with four recesses on a lateral surface thereof in a longitudinal direction thereof at equal intervals; electrodes installed on upper and lower surfaces of the cylindrical capsule and configured to measure electrocardiogram; a wireless electrocardiogram sensor module installed inside the capsule; a data transceiving antenna wound around two recesses symmetrical at a center; and a coil for charging wound around the remaining two recesses in a direction vertical to the antenna.

Abstract: Disclosed is an implantable wireless electrocardiogram sensor device, including: a cylindrical capsule provided with four recesses on a lateral surface thereof in a longitudinal direction thereof at equal intervals; electrodes installed on upper and lower surfaces of the cylindrical capsule and configured to measure electrocardiogram; a wireless electrocardiogram sensor module installed inside the capsule; a data transceiving antenna wound around two recesses symmetrical at a center; and a coil for charging wound around the remaining two recesses in a direction vertical to the antenna.

Abstract: Disclosed is a clinical diagnosis supporting method and system based on hierarchical fuzzy inference. The clinical diagnosis supporting method includes the steps of: (a) extracting check data of a plurality of check items for each of a plurality of patients from a respective check item database; (b) selecting a characteristic from the check data of each of the check items; (c) hierarchical fuzzy-inferring the check data from which the characteristic is selected, for each check item; (d) extracting a check opinion for each disease based on an output value of the fuzzy inference for each check item; and (e) making a synthetic clinical estimation based on the extracted check opinion.

Abstract: Disclosed is a clinical diagnosis supporting method and system based on hierarchical fuzzy inference. The clinical diagnosis supporting method includes the steps of: (a) extracting check data of a plurality of check items for each of a plurality of patients from a respective check item database; (b) selecting a characteristic from the check data of each of the check items; (c) hierarchical fuzzy-inferring the check data from which the characteristic is selected, for each check item; (d) extracting a check opinion for each disease based on an output value of the fuzzy inference for each check item; and (e) making a synthetic clinical estimation based on the extracted check opinion.

Abstract: Disclosed are a planar small electrode sensor for skin impedance measurement and a system using the same. The sensor include: a semiconductor substrate; an insulating layer formed on the substrate; and at least one pair of electrodes which are symmetrically formed on the insulating layer with respect to a vertical central line of the insulating layer, where the at least one pair of electrodes includes a reference electrode and a measuring electrode.

Abstract: A significance parameter extraction method for differential diagnosis of abnormal diseases based on entropy rough approximation technology, including the steps of: (a) calculating clinical reference values from two different groups of clinical data extracted from a database storing a plurality of clinical data for each check item using an entropy maximization measure; (b) evaluating a clinical difference between the two different groups of clinical data and extracting candidate check items; (c) based on a reference value of a check item calculated from one of the groups of clinical data, converting attribute values of the check item into nominal attribute values; and (d) extracting significance parameters for differential diagnosis from the candidate check items extracted in the step (b).