Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.

Abstract: The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices.

Abstract: Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined.

Abstract: A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.

Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: A method for simulating participation patterns in a plurality of events from a pool of qualified participants includes selecting an event whose preference rank r is equal to a predetermined preference rank R(i,j) for an event of type j for individual i; sampling a random number Z that represents a number of events in which to participate when a number N(j) of available events of type j is greater than 0 and an expected participation rate P(i, j) of individual i for events of type j is greater than 0; selecting a random subset S with Z elements that indicates which Z events to participate in; and looping over k=1 to N(j) and setting a participation array M(i,j,k,l) to 1 iff k is contained in S, where participation array M(i,j,k,l) indicates whether or not individual i participates in a k-th event of type j in an l-th simulation.

Abstract: Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined.

Abstract: The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices.

Abstract: A method for simulating participation patterns in a plurality of events from a pool of qualified participants includes selecting an event whose preference rank r is equal to a predetermined preference rank R(i,j) for an event of type j for individual i; sampling a random number Z that represents a number of events in which to participate when a number N(j) of available events of type j is greater than 0 and an expected participation rate P(i, j) of individual i for events of type j is greater than 0; selecting a random subset S with Z elements that indicates which Z events to participate in; and looping over k=1 to N(j) and setting a participation array M(i,j,k,l) to 1 iff k is contained in S, where participation array M(i,j,k,l) indicates whether or not individual i participates in a k-th event of type j in an l-th simulation.

Abstract: Techniques for mapping policy documents to regulatory documents to check for compliance between the policies and documents are provided. In one example, a computer-implemented method determining, by a system operatively coupled to a processor, an information input, a control framework, and a document from a first group consisting of a regulatory document and a policy document, wherein the information input is a corpora from a second group consisting of a domain corpora and a global corpora. The computer-implemented method can also comprise mapping, by the system, the received regulatory document or the received policy document to the control framework using a supervised machine learning technique.

Abstract: A method includes reading project data describing a plurality of projects in a project database. The plurality of projects are associated with a plurality of skill sets, where each project of the plurality of projects is associated with a skill set needed to complete the project. A set of principal skill sets is determined, to include a set of frequent skill subsets that each appear in the plurality of skill sets with at least a threshold frequency. A new project is received and associated with a requested skill set. The requested skill set is mapped, using a computer processor, to a corresponding principal skill set of the set of principal skill sets, where the corresponding principal skill set represents the requested skill set. The corresponding principal skill set is used for at least one of staffing the new project and updating organizational staffing plans related to the plurality of projects.

Abstract: A service engagement map may be generated based on data collected associated with the service transition and delivery processes. The service engagement map may be refined iteratively by discovering additional data associated with the service transition and delivery processes and updating the service engagement map according to the additional data. Engagement metrics may be computed based on the service engagement map and presented. The service engagement map may also be presented visually.

Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.

Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.

Abstract: An application management service account benchmarking. An account profile is generated associated with a target account. Data associated with the target account collected and prepared for benchmarking. A benchmarking pool is formed to include a set of accounts with which to compare the target account. Operational KPIs are designed for benchmarking analysis. Measurements associated with the operational KPIs are determined for the target account and the set of accounts in the benchmarking pool. Benchmarking is conducted based on the measurements. A graph of a distance map is generated and presented on a graphical user interface. Post benchmarking analysis is performed that suggests an action to be performed for the target account.

Abstract: An application management service account benchmarking. An account profile is generated associated with a target account. Data associated with the target account collected and prepared for benchmarking. A benchmarking pool is formed to include a set of accounts with which to compare the target account. Operational KPIs are designed for benchmarking analysis. Measurements associated with the operational KPIs are determined for the target account and the set of accounts in the benchmarking pool. Benchmarking is conducted based on the measurements. A graph of a distance map is generated and presented on a graphical user interface. Post benchmarking analysis is performed that suggests an action to be performed for the target account.

Abstract: A service engagement map may be generated based on data collected associated with the service transition and delivery processes. The service engagement map may be refined iteratively by discovering additional data associated with the service transition and delivery processes and updating the service engagement map according to the additional data. Engagement metrics may be computed based on the service engagement map and presented. The service engagement map may also be presented visually.

Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.