Device management apparatus, device management system, and device management method
According to one embodiment, a device management apparatus includes processing circuitry. The processing circuitry acquires information indicating occurrence of a first failure prediction of a first device. The processing circuitry adjusts detection sensitivity of a second failure prediction of a second device relating to the information based on relevance between the first device and the second device. The second device is different from the first device.
1. A device management apparatus, comprising:
processing circuitry configured to:
acquire information indicating occurrence of a first failure prediction of a first medical device related to a failure estimation portion of the first medical device; and
adjust detection sensitivity of a second failure prediction of a second medical device relating to the acquired information based on relevance between the first medical device and the second medical device by transmitting, to the second medical device, training data that was used to determine the failure estimation portion by the first medical device, the second medical device being different from the first medical device, and the second medical device applying the training data to train a machine-learning model to perform the second failure prediction of the second medical device.
2. The device management apparatus according to claim 1 , wherein the processing circuitry is further configured to
acquire information indicating the failure estimation portion of the first medical device,
set an extraction condition for determining the second medical device based on the failure estimation portion, and
determine the second medical device by the set extraction condition.
3. The device management apparatus according to claim 2 , wherein the processing circuitry is further configured to set a different extraction condition according to the failure estimation portion.
4. The device management apparatus according to claim 3 , wherein the processing circuitry is further configured to set the different extraction condition in consideration of an operation status of the first medical device.
5. The device management apparatus according to claim 2 , wherein the processing circuitry is further configured to adjust the detection sensitivity of the second failure prediction for a portion of the second medical device corresponding to the failure estimation portion.
6. The device management apparatus according to claim 5 , wherein the processing circuitry is further configured to determine that it is necessary to adjust the detection sensitivity of the second failure prediction according to the failure estimation portion.
7. The device management apparatus according to claim 5 , wherein the processing circuitry is further configured to determine that it is necessary to adjust the detection sensitivity of the second failure prediction based on an estimation cause of the failure estimation portion.
8. A device management system, comprising:
a device management apparatus comprising processing circuitry configured to:
acquire information indicating occurrence of a first failure prediction of a first medical device related to a failure estimation portion of the first medical device; and
adjust detection sensitivity of a second failure prediction of a second medical device relating to the acquired information based on relevance between the first medical device and the second medical device by transmitting, to the second medical device, training data that was used to determine the failure estimation portion by the first medical device, the second medical device being different from the first medical device, and the second medical device applying the training data to train a machine-learning model to perform the second failure prediction of the second medical device; and
an output apparatus configured to output information relating to the first failure prediction that occurred with respect to the first medical device.
9. A device management method, comprising:
acquiring information indicating occurrence of a first failure prediction of a first medical device related to a failure estimation portion of the first medical device; and
adjusting detection sensitivity of a second failure prediction of a second medical device relating to the acquired information based on relevance between the first medical device and the second medical device by transmitting, to the second medical device, training data that was used to determine the failure estimation portion by the first medical device, the second medical device being different from the first medical device, and the second medical device applying the training data to train a machine-learning model to perform the second failure prediction of the second medical device.