Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

Abstract: A method for performing dialysis event prediction by employing a meta-training strategy for model personalization includes, in a meta-training stage, generating segments from temporal records of patient dialysis data, generating, from the segments, a support set and a query set for each patient of a plurality of patients, formulating tasks for each patient in a pre-training set defined as a meta-training framework (M-DCCN), where each task includes the support set and the query set, and sending the tasks to a two-level meta-training algorithm supported training coordinator. The method further includes, in a finetuning stage, sending the M-DCCN to local machines where a finetuning dataset is collected for new patients, the finetuning dataset including a limited amount of data pertaining the new patients, fine-tuning the M-DCCN for personalization, and using the fine-tuned M-DCCN for future predictive dialysis analysis of future new patients by generating prognostic predictive scores.

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

Abstract: There is provided a system model evaluation system including a system model candidate creation part configured to create a candidate(s) of a system model by changing a pattern of selecting an inter-sensor-value relationship created by using sensor values acquired from sensors arranged in a system to which the system model is directed. This system model evaluation system further includes a system model evaluation part configured to evaluate the candidate(s) of the system model by inputting predetermined evaluation data to the created candidate(s) of the system model.

Abstract: Based on a normal model, it is detected whether or not an event signal of a computer system is anomalous. In parallel with the normal-model-based anomaly detection, it is detected based on a rule whether or not the event signal is anomalous. Then, a final anomaly detection result is generated by performing comprehensive determination based on detection results of the normal-model-based anomaly detection and the rule-based anomaly detection.

Abstract: Disclosed are an information selection device and the like that make it possible to rapidly acquire information about an event of interest. An information selection device is configured to specify target log information among log information. The log information represents that a process is executed for a processing object in a target system and the target log information represents a processing object that may affect an abnormal process executed in the target system. The information selection device is configured to calculate a frequency of the target log information for each combination of the process with the processing object, calculate an abnormality degree of the target log information based on the calculated frequency and select relevant log information with having the abnormality degree satisfying a condition for determining abnormal log information among the target log information.

Abstract: There is provided a system model evaluation system including a system model candidate creation part configured to create a candidate(s) of a system model by changing a pattern of selecting an inter-sensor-value relationship created by using sensor values acquired from sensors arranged in a system to which the system model is directed. This system model evaluation system further includes a system model evaluation part configured to evaluate the candidate(s) of the system model by inputting predetermined evaluation data to the created candidate(s) of the system model.

Abstract: A content distribution system is provided, in which time-shift viewing among a plurality of devices can be realized. The system includes, a device group management unit which manages a group to which audio-visual devices capable of realizing time-shift viewing mutually belong; a viewing state management unit which acquires viewing information of a content by the audio-visual device, and a time shift control unit which transmits permission information for viewing the content in a time-shifted manner and the viewing information of the content acquired by the viewing state management unit, to the audio-visual device included in the group managed by the device group management unit, in response to a time shift request issued by the audio-visual device.

Abstract: A content distribution system is provided, in which time-shift viewing among a plurality of devices can be realized. The system includes, a device group management unit which manages a group to which audio-visual devices capable of realizing time-shift viewing mutually belong; a viewing state management unit which acquires viewing information of a content by the audio-visual device, and a time shift control unit which transmits permission information for viewing the content in a time-shifted manner and the viewing information of the content acquired by the viewing state management unit, to the audio-visual device included in the group managed by the device group management unit, in response to a time shift request issued by the audio-visual device.

Abstract: An electronic mail transmitting and receiving system that is capable of predicting whether or not the user can access the electronic mail transmitting and receiving system, and which is also capable of automatically controlling whether or not the electronic mail addressed to the user is transferred to an auxiliary device such as facsimile equipment, predetermined by the user. The electronic mail transmitting and receiving system is provided with a mail system monitor which monitors arrival, transmission, and reception of the electronic mail addressed to the user, a transfer propriety data generator that anticipates whether or not the user can access the mail system according to the result of the monitor and which generates a transfer propriety data based on the result of the anticipation. A mail transfer device refers to the transfer propriety data periodically at a fixed time interval.

Abstract: A data sharing system in which a server comprises a connection party holding unit for holding access information, and a terminal equipment comprises a database for acquiring the corresponding data so to submit it to the application program when an application program issues a reference request of data and a connection party deciding unit for deciding a connection party calculating machine with reference to access information depending on the necessity, the database judging whether there exists a data name of the data specified by the reference request in the connection party holding unit when a reference request is issued, instructing the connection party deciding unit to decide a calculating machine of a connection party when there exits, and further issuing a data transfer request to the decided connection party, so to acquire the corresponding data.