Abstract: The disclosure provides a method for data instance processing. The method includes obtaining a set of data instances collected from a plurality of organizations. Each of the data instances includes at least one record formed in an organization that stores values of a plurality of attributes of the data instance. The method also includes dividing the set of data instances into groups, wherein data instances with conflicting values for the same attribute are divided into different groups. The method further includes subdividing data instances in each of the groups into clusters.

Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: 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.

Abstract: A method for detecting deviations between an event log and a process model includes converting the process model into a probability process model, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to an activity in the process model; detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship; and identifying the differences as the deviations in response to the differences exceeding a predefined threshold; wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model.

Abstract: 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.

Abstract: A method, device, and computer program storage product for generating a query to extract clinical features into a set of electronic medical record (EMR) tables based on clinical knowledge. A knowledge tree is constructed according to a set of clinical knowledge data. An EMR graph corresponding to a set of EMR tables is obtained. The EMR graph comprises at set of table nodes and a set of attribute nodes. The set of table nodes and the set of attribute nodes represent a structure of each EMR table in the set of EMR tables and a reference relationship among attributes of set of EMR tables. A plurality of sub-queries is generated based on the knowledge tree and the EMR graph. At least one query is generated by combining the plurality of sub-queries according to the knowledge tree.

Abstract: 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.

Abstract: Methods, systems, and computer program products are provided for language entity identification. In one embodiment, a computer-implemented method is disclosed. In the method, respective pinyin codes may be determined for respective Chinese characters comprised in a string that is to be processed. Then, respective pinyin features may be generated from the respective pinyin codes. Next, a candidate language entity may be identified from the string based on the respective pinyin features and a mapping describing an association between pinyin features and language entity. In other embodiments, a computer-implemented system and a computer program product for security management are disclosed.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate automatically mapping different data types are provided. In one embodiment, a computer-implemented method comprises: constructing, by a system operatively coupled to a processor, an index from one or more classifier models for one or more data types; scoring and ranking, by the system, one or more candidate pairs for the one or more data types based on confidence score; and analyzing, by the system, how the one or more candidate pairs are scored and automatically generating the one or more classifier models used to construct the index.

Abstract: A computer implemented method identifies guidelines through use of a neural network by a remote guideline server. A client computer transmits instructions to the remote guideline server to retrieve and evaluate multiple candidate guidelines. The remote guideline server utilizes a neural network to identify a string of terms found in each of the multiple candidate guidelines that match one or more strings of terms from a model guideline; to identify a semantic concept of each of the multiple candidate guidelines that matches one or more semantic concepts from the model guideline; and to identify a structural pattern of each of the multiple candidate guidelines that matches one or more structural patterns of the model guideline. The candidate guidelines that match the model guideline are then sent from the remote guideline server to the client computer.

Abstract: Techniques are described that facilitate automatically providing entities with rephrased versions of standard answers. In one embodiment, a computer-implemented is provided that comprises determining, by a device operatively coupled to a processor, a talking style of a plurality of talking styles that an entity is associated with based on reception of natural language input from the entity proposing a question related to a defined topic. The method further comprises selecting, by the device based on the talking style, an answer rephrasing model from a plurality of answer rephrasing models respectively configured to generate different rephrased versions of a standard answer to the question, and employing, by the device, the answer rephrasing model to generate a rephrased version of the standard that corresponds to the talking style.

Abstract: Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Abstract: A method, system, and computer program product, include obtaining a plurality of training sets of samples and a plurality of testing sets of samples from a data set of clinical records, the plurality of training sets of samples and the corresponding plurality of testing sets of samples including different samples of the data set, determining statistical significance for a plurality of risk factors of a model from the plurality of training sets of samples, determining a plurality of performance metrics of the model from the plurality of testing sets of samples, and determining an evaluation metric of the plurality of risk factors for the model based on the statistical significance and the plurality of performance metrics, the evaluation metric being for improving accuracy of the model.

Abstract: The present disclosure provides a method, apparatus and system for processing a case management model (CMM). According to an embodiment, there is provided a method for processing a CMM, the method includes: obtaining an existing CMM having a plurality of elements; obtaining a new CMM having at least one element; aligning an element of the new CMM to an element of the existing CMM according to match costs between the element of the new CMM and the plurality of elements of the existing CMM; and fusing the new CMM into the existing CMM based on the match cost between the aligned elements.

Abstract: 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.

Abstract: Combination therapies are automatically discovered that provide statistically significantly greater protection against a specific outcome, compared to therapies with fewer treatments. A dataset is received that associates each of a plurality of respective individual treatments with a corresponding outcome and a corresponding set of confounding factors. A plurality of new potential combination therapies are combined iteratively. The received dataset is used to automatically identify at least one candidate combination therapy of the new potential combination therapies that provides a statistically significantly greater protection against an outcome with reference to each of the plurality of respective individual treatments.

Abstract: A method, device, and computer program storage product for generating a query to extract clinical features into a set of electronic medical record (EMR) tables based on clinical knowledge. A knowledge tree is constructed according to a set of clinical knowledge data. An EMR graph corresponding to a set of EMR tables is obtained. The EMR graph comprises at set of table nodes and a set of attribute nodes. The set of table nodes and the set of attribute nodes represent a structure of each EMR table in the set of EMR tables and a reference relationship among attributes of set of EMR tables. A plurality of sub-queries is generated based on the knowledge tree and the EMR graph. At least one query is generated by combining the plurality of sub-queries according to the knowledge tree.

Abstract: The disclosure provides a method for data instance processing. The method includes obtaining a set of data instances collected from a plurality of organizations. Each of the data instances includes at least one record formed in an organization that stores values of a plurality of attributes of the data instance. The method also includes dividing the set of data instances into groups, wherein data instances with conflicting values for the same attribute are divided into different groups. The method further includes subdividing data instances in each of the groups into clusters.

Abstract: Methods, systems, and computer program products are provided for language entity identification. In one embodiment, a computer-implemented method is disclosed. In the method, respective pinyin codes may be determined for respective Chinese characters comprised in a string that is to be processed. Then, respective pinyin features may be generated from the respective pinyin codes. Next, a candidate language entity may be identified from the string based on the respective pinyin features and a mapping describing an association between pinyin features and language entity. In other embodiments, a computer-implemented system and a computer program product for security management are disclosed.

Abstract: 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.