International Business Machines Corporation

Schlumberger Technology Corporation

Ca, Inc.

Hai Feng Liu

Jing Li

Guo Tong Xie

Shi Wan Zhao

Shi Jing Guo

Zhi Qiao

Jing Mei

Gang Hu

Yi Qin Yu

Ping Zhang

Eryu Xia

En Liang Xu

Ai Huang

Bernard Andre Montaron

Soil detector

Systems, computer-implemented methods and/or computer program products that facilitate providing treatment suggestions are described herein. In one example, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, treatment directed graphs associated with a plurality of treatment regimens, wherein, in connection with generating respective treatment directed graphs, penalties are applied during weighting of respective regimens as a function of an associated confidence level; identifying a treatment directed graph that corresponds to the patient directed graph; and outputting a set of treatment suggestions associated with the treatment directed graph that corresponds to the patient directed graph.

Facilitating health intervention suggestion for disease mitigation and/or prevention

Techniques for identifying representative patients from a patient group are provided. Based on an outcome of interest, one or more patients can be grouped according to phenotyping features associated with the outcome of interest. Additionally, in response to grouping the one or more patients, a representative patient of the one or more patients can be determined based on values associated with the phenotyping features.

Neural network based selection of representative patients

Systems, computer-implemented methods and/or computer program products that facilitate predicting personalized risks based on intrinsic factors and extrinsic factors are provided. In one example, a computer-implemented method comprises: collecting, by a system operatively coupled to a processor, intrinsic factors and extrinsic factors associated with infectious diseases; generating, by the system, a probabilistic model based on the intrinsic factors and extrinsic factors, wherein the model incorporates node characteristics into infection probability; and refining, by the system, the model through concurrently learning respective node thresholds and hidden infection network structure of the model.

Personalized risk prediction based on intrinsic and extrinsic factors

A dataset regarding a plurality of applications is obtained. A set of parameters is determined from the dataset, comprising at least a sample performance trajectory, a risk factor, and a performance outcome. A maximum likelihood of each performance outcome is determined using a likelihood function, the likelihood function being a mixture model of a trajectory model and an outcome model. The set of parameters is updated according to the maximum likelihood of each performance outcome. A performance trajectory model is built according to the updated set of parameters. The plurality of applications is then grouped into subgroups according to the performance trajectory model, each subgroup containing one or more applications, and each of the one or more applications in a given subgroup having a same or similar trajectory to each other. An alert associated with the applications in at least one of subgroups may be generated.

Outcome-driven trajectory tracking

A method, system, and computer program product for obtaining a candidate event sequence that includes at least one event for achieving a goal, obtaining a reference event sequence, the candidate event sequence comprising at least one event that is not comprised in the reference event sequence, comparing an effectiveness of the candidate event sequence on the goal and an effectiveness of the reference event sequence on the goal, and identifying the candidate event sequence as an effective sequence for achieving the goal in response to the effectiveness of the candidate event sequence being better than the effectiveness of the reference event sequence.

Event sequence management

A method and system of imputing corrupted sequential data is provided. A plurality of input data vectors of a sequential data is received. For each input data vector of the sequential data, the input data vector is corrupted. The corrupted input data vector is mapped to a staging hidden layer to create a staging vector. The input data vector is reconstructed based on the staging vector, to provide an output data vector. adjusted parameter of the staging hidden layer is iteratively trained until it is within a predetermined tolerance of a loss function. A next input data vector of the sequential data is predicted based on the staging vector. The predicted next input data vector is stored.

Missing values imputation of sequential data

Techniques for fracturing a subterranean formation penetrated by a wellbore are provided. The subterranean formation has vertical and horizontal stresses applied thereto. The wellbore has a near wellbore stress zone thereabout. The method involves drilling the wellbore along a drilling path (the wellbore having a vertical portion and a horizontal portion), creating at least one 360-degree perforation in the subterranean formation about the horizontal portions of the wellbore, and fracturing the formation by injecting a fluid into the 360-degree perforations. The 360-degree perforations extend about the wellbore a distance beyond the near wellbore stress zone and at least twice a diameter of the wellbore starting from an axis of the wellbore. A direction of the 360-degree perforation is transverse to the wellbore axis.

Method for transverse fracturing of a subterranean formation

A computer system monitors the execution time of each of a plurality of tasks over a plurality of time periods. The system receives a first input that selects a particular time period from the plurality of time periods, and further monitors the execution time of the plurality of tasks in the selected time period. The system receives a second input that selects a particular task from the particular time period, and monitors the execution time of the particular task in the particular time period.

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Beijing, China

Hai Feng Liu