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: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.

Abstract: Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.

Abstract: A mapping matrix, which maps from original features of an electronic health record database to higher level latent factors, is initialized. For each of one or more target diseases, regression coefficients are updated over the higher level latent factors, based on said initialized mapping matrix, a data matrix containing said original features, and a label vector of corresponding responses. Said mapping matrix is updated based on said updated regression coefficients. Said steps of updating said regression coefficients and updating said mapping matrix are repeated until convergence is achieved, to obtain a final mapping matrix and a final set of regression coefficients.

Abstract: A processor-implemented method, computer program product and system are provided for predicting drug-drug interactions based on clinical side effects. The method includes constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs. The method also includes constructing side effect features for each of the drugs from side effects associated with the drugs. The method further includes building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the drugs.

Abstract: A system and method for patient stratification include determining a first set of patient groups from patients in a patient similarity graph based on a similarity structure of the patient similarity graph. A second set of patient groups is identified based on expert domain knowledge associated with the patients. Patients in the first set and the second set are aligned using a processor to stratify patients.

Abstract: Systems and methods for data densification include representing patient data as a sparse patient matrix for each patient. The sparse patient matrix is decomposed into a plurality of matrices including a concept matrix indicating medical concepts of the patient data and an evolution matrix indicating a temporal relationship of the medical concepts. Missing information in the sparse patient matrix is imputed using a processor based on the plurality of matrices to provide a densified patient matrix.

Abstract: Systems and methods for patient identification include identifying a set of mature drugs similar to a target drug using a processor based on a drug similarity measure. A plurality of outcome models are constructed for each mature drug in the set based on real-world evidence, the plurality of outcome models representing a patient response to each mature drug. A patient response to the target drug is predicted based on the outcome models to identify patients for the target drug.

Abstract: Systems and methods for patient identification include identifying a set of mature drugs similar to a target drug using a processor based on a drug similarity measure. A plurality of outcome models are constructed for each mature drug in the set based on real-world evidence, the plurality of outcome models representing a patient response to each mature drug. A patient response to the target drug is predicted based on the outcome models to identify patients for the target drug.

Abstract: A computer-implemented method and apparatus for assessing treatment adherence by patients, the method comprising: receiving a model providing statistical significance of patients' response to treatment, the model based on treatment assigned to the patients, wherein the patients are diagnosed with a disease; computing by the computerized device a p-value for a result received for a patient diagnosed with the disease and being treated by the treatment, by applying the model to at least one patient; and issuing an alert responsive to the p-value being indicative of the result being unexpected beyond a threshold.

Abstract: A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.

Abstract: A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.

Abstract: Techniques are presented for analyzing audio-video segments, usually from multiple sources. A combined similarity measure is determined from text similarities and video similarities. The text and video similarities measure similarity between audio-video scenes for text and video, respectively. The combined similarity measure is then used to determine similar scenes in the audio-video segments. When the audio-video segments are from multiple audio-video sources, the similar scenes are common scenes in the audio-video segments. Similarities may be converted to or measured by distance. Distance matrices may be determined by using the similarity matrices. The text and video distance matrices are normalized before the combined similarity matrix is determined. Clustering is performed using distance values determined from the combined similarity matrix.

Abstract: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Systems and methods for data analysis include determining a patient trace as a set of medical events for a patient. Medical events of the patient trace are grouped into subsets of medical events using a processor according to a temporal relationship between the medical events. Co-occurring events are identified from the subsets of medical events as event clusters. A plurality of medical events in one or more of the subsets of the patient trace is represented using the event clusters to condense the patient trace.

Abstract: Systems and methods for data analysis include determining a patient trace as a set of medical events for a patient. Medical events of the patient trace are grouped into subsets of medical events using a processor according to a temporal relationship between the medical events. Co-occurring events are identified from the subsets of medical events as event clusters. A plurality of medical events in one or more of the subsets of the patient trace is represented using the event clusters to condense the patient trace.

Abstract: Systems and methods for data analysis include determining medical events co-occurring within a time period from a patient record database. The medical events are grouped into sets of medical events such that a number of sets of medical events is minimized based upon medical event cardinality. Patterns from the sets of medical events are identified, using a processor, to provide relationships between the patterns and patient outcomes.

Abstract: Systems and methods for data analysis include determining medical events co-occurring within a time period from a patient record database. The medical events are grouped into sets of medical events such that a number of sets of medical events is minimized based upon medical event cardinality. Patterns from the sets of medical events are identified, using a processor, to provide relationships between the patterns and patient outcomes.