Hitachi, Ltd.

Hiroaki Ozaki

Abhay Mehta

Hsiu-Khuern Tang

Haiyan Wang

Shuang Feng

Yoshiyuki Kobayashi

Keiro Muro

Makoto Iwayama

Song Wang

Ravigopal Vennelakanti

Anshuman Sahu

Maojing Fu

Bin Tong

Laleh Jalali

In some examples, a system may generate a plurality of care path patient profile models based on a plurality of care path patterns for a plurality of past patient admissions. For example, each care path patient profile model may include a trained classifier. Further, the system may receive information related to a new patient admission, and may generate features from the received information related to the new patient admission. The system may input the features generated from the received information related to the new patient admission into the plurality of care path patient profile models to obtain a respective probability of being classified in a respective care path based on an amount of similarity to the patients who have gone through each care path. In addition, the system may present, on a display, information related to at least one care path pattern in a graphical user interface.

Care path analysis and management platform

In some examples, a computing device may receive hierarchical data having a hierarchical structure including a plurality of levels. The computing device may determine a plurality of features based at least in part on the hierarchical data, and may select a subset of the features at a first level as candidates for consolidating to a next higher level in the hierarchical structure. The computing device may determine that a predicted loss of information from consolidating the subset of features is less than a threshold, and may revise the hierarchical structure to include a consolidated feature at the next higher level, rather than the subset of features. In some examples, a statistical model may be trained based on the revised hierarchical structure and used at least partially to make a determination, send a notification, and/or control a device.

Controlling devices based on hierarchical data

In some examples, a computing device may receive sensor data associated with a plurality of patient devices that are associated with a plurality of patients. The computing device may further receive caregiver records corresponding at least partially to the sensor data. At least two groups of indicators may be determined from the caregiver records, such a based on a selected subject. Further, the computing device may determine a plurality of clusters from the sensor data. Based on the plurality of clusters and the at least two groups, the computing device may determine an indication of a discrepancy in care for a patient of the plurality of patients. Based on the indication of the discrepancy, the computing device may send at least one of a notification to a caregiver computing device, a notification to a monitoring computing device, or a control signal to one of the patient devices.

Managing patient devices based on sensor data

In some examples, a computing device may receive sensor data for a target and at least one of: log data for the target, or historical log data and historical sensor data for a plurality of other targets. The computing device may determine at least one event classified as a non-uniform event in at least one of the log data or the historical log data, and may determine combined features, such as a feature vector, based on the sensor data and the non-uniform event(s). The computing device may determine an analysis result from the combined features. Further, based on the analysis result, the computing device may send a control signal to a device associated with the target for controlling the device, and/or may send a communication related to the target to another computing device.

Controlling a device based on log and sensor data

A series data distributed processing system including a parallel processing system and a distributed series data management unit managing distributed series data, wherein: the parallel processing system includes, on each computing device, a data block, a data block processing server for processing the data block, and a block processing integration server for processing a result from the data block processing server, the data block being formed from a plurality of values each associated with one of a plurality of sequential labels in the series data; and the distributed series data management unit includes a distributed information management database for managing data blocks, which retains management data, including sequential label ranges, which refer to ranges of sequential labels in the data blocks, series IDs corresponding to value names in the data blocks, and meta-information identifying computing devices retaining the data blocks.

System and method for parallel processing data blocks containing sequential label ranges of series data

The problem solved by this invention is to convert text information in a geology report to numerical values which reflects geological characteristics of a well's subsurface. Prior art referred above cannot be applicable to this problem. Since text information in the geology report is in the natural language form. This information is not widely used in this industry, due to the fact that the text information can be hardly extracted and summarized into numerical values and integrated into current physical geology models or statistical models. This invention makes the text information in geology report, which is often in a natural language form, easier to be integrated into current geology physical models or statistical models. Also, the numerical values extracted from the geology report can be integrated with other kinds of data, such as seismic data and well-logging data, to obtain more accurate and comprehensive analysis results.

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Tokyo, Japan

Hiroaki Ozaki