Abstract: A system and method for providing a temporally dynamic model parameter include building a model parameter by minimizing a loss function based on patient measurements taken at a plurality of time points. Temporally related values of the model parameter are identified, using a processor, having a same type of patient measurement taken at different time points. At least one value of the model parameter and temporally related values of the at least one value are selected to provide a temporally dynamic model parameter.

Abstract: A system and method for providing a temporally dynamic model parameter include building a model parameter by minimizing a loss function based on patient measurements taken at a plurality of time points. Temporally related values of the model parameter are identified, using a processor, having a same type of patient measurement taken at different time points. At least one value of the model parameter and temporally related values of the at least one value are selected to provide a temporally dynamic model parameter.

Abstract: A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries.

Abstract: The present invention provides a single-stage double-acting traveling-wave thermoacoustic system, comprising three elementary units; each unit comprises a linear motor and a thermoacoustic conversion device. The linear motor comprises a cylinder and a piston; the piston can perform straight reciprocating motion in the cylinder. The thermoacoustic conversion device comprises a first heat exchanger, a heat regenerator, a second heat exchanger, a thermal buffer tube and a third heat exchanger connected in sequence, the first heat exchanger and the third heat exchanger of each thermoacoustic conversion device are connected to cylinder cavities of different linear motors respectively, forming a loop structure of a gas medium flow. The system comprises a thermal buffer tube and a third heat exchanger, making the temperature of gas medium given feedback to the cylinder cavity of another linear motor be close to the room temperature, ensuring the piston and cylinder working at room temperature.

Abstract: The present invention provides a multi-stage double-acting traveling-wave thermoacoustic system, comprising three elementary units, each elementary unit comprises a linear motor and a thermoacoustic conversion device; the linear motor comprises a piston and a cylinder, the piston can perform a straight reciprocating motion in the cylinder; each thermoacoustic conversion device comprises a main heat exchanger and a heat regenerator connected in sequence, and the heat regenerator is of a ladder structure; a set of a non-normal-temperature heat exchanger, a thermal buffer tube and an auxiliary heat exchanger is connected at each ladder of the heat regenerator; and the main heat exchanger and the auxiliary heat exchanger of each thermoacoustic conversion device are connected to cylinder cavities of different linear motors respectively forming a loop structure for flow of a gas medium.

Abstract: A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations are solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.

Abstract: The present invention discloses a heat-actuated double-acting traveling-wave thermoacoustic refrigeration system, comprising at least three elementary units; each elementary unit comprises a thermoacoustic engine, a thermoacoustic refrigerator, and a resonance device; the thermoacoustic engine and the thermoacoustic refrigerator comprise a main heat exchanger, a heat regenerator, a non-normal-temperature heat exchanger, a thermal buffer tube, and an auxiliary heat exchanger connected in sequence; the resonance device comprises a sealed housing, a moving part is provided in the housing for a reciprocating motion; the moving part separates the housing into at least two chambers; the main heat exchanger and auxiliary heat exchanger of each thermoacoustic engine and thermoacoustic refrigerator respectively connects to chambers of different housing, forming dual-loop structure of gas medium flow.

Abstract: A method of mapping a care plan template to a case model includes receiving a care plan template, extracting elements from the care plan template, wherein the elements correspond to a phase comprising at least one task and data attributes corresponding to the task, mapping the task of the care plan template to a task of the case model, mapping a precedence relationship of the task of the care plan template to preconditions of the task of the case model, mapping the data attributes of the care plan template to properties of the case model, wherein the properties are associated with the task of the case model, mapping the task of the care plan template to a role of the case model, and generating the case model including the mapped task, the mapped precedence relationship, the mapped data attributes, and the mapped role.

Abstract: A method of mapping a care plan template to a case model includes receiving a care plan template, extracting elements from the care plan template, wherein the elements correspond to a phase comprising at least one task and data attributes corresponding to the task, mapping the task of the care plan template to a task of the case model, mapping a precedence relationship of the task of the care plan template to preconditions of the task of the case model, mapping the data attributes of the care plan template to properties of the case model, wherein the properties are associated with the task of the case model, mapping the task of the care plan template to a role of the case model, and generating the case model including the mapped task, the mapped precedence relationship, the mapped data attributes, and the mapped role.

Abstract: Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

Abstract: Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

Abstract: A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries.

Abstract: A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.

Abstract: A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations are solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.

Abstract: Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.

Abstract: Systems and methods for risk factor identification include identifying a first set of risk factors from personal data. A second set of risk factors is identified from at least one of a user input and a knowledge source. The first set is combined with the second set, using a processor, by selecting a number of risk factors from the first set that augment the second set of risk factors to determine a combined list of risk factors that predict a condition of interest.

Abstract: Systems and methods for risk factor identification include identifying a first set of risk factors from personal data. A second set of risk factors is identified from at least one of a user input and a knowledge source. The first set is combined with the second set, using a processor, by selecting a number of risk factors from the first set that augment the second set of risk factors to determine a combined list of risk factors that predict a condition of interest.

Abstract: In a computerized social network, expert and user chat sessions are stored and rated probabilistically. Later user requests for information are met with an expert ranking, based on a balance of similarities between expert profile and questions; similarity between expert profile and prior chat sessions, and dynamically updated chat session ratings. New sessions can be rated automatically with reference to keywords distilled from past sessions responsive to user ratings—and based on session length.

Abstract: A system, method and program product for matching members of a population, e.g., patients, based on member similarities. Patients are mapped to a bipartite graph with patient nodes connected by weighted edges to clustered factor nodes, are clustered categorically. As a new patient query is received, a similarity measure for each other patient is generated for each cluster by comparing cluster edges. The cluster similarity measures are aggregated for each patient to provide a global closeness measure to every other patient. Based on the global closeness measure, a list of the closest patients is displayed and measurement feedback may be provided.

Abstract: A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations arc solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.