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: Predicting beneficial drug combinations mitigating adverse drug reactions identifies drug combinations and associated target adverse drug reaction from a spontaneous reporting system containing case reports of drugs and associated adverse drug reactions. Each drug combination comprises a first drug and a second drug, and a propensity score is computed for each drug in each group. This propensity score expresses a probability of being exposed to a given drug based on other co-prescribed drugs and reported indications, which reflect patient characteristics. Associations are computed for each drug as well as drug interaction. Among the associations, the sum of the associations of the second drug and the interaction effect represents the predicted beneficial score expressing whether the second drug alters the chance of developing the target adverse drug reaction for patients on the first drug.

Abstract: Embodiments include methods, systems and computer program products for reducing feature space in analysis of medical data. Aspects include receiving patient temporal traces. Aspects also include conducting sequential pattern mining on the patient temporal traces to produce sequential features. Aspects also include clustering the sequential features with a similarity metric. Aspects also include analyzing the clustered features to predict a healthcare outcome.

Abstract: Examples of techniques for patient-level analytics with sequential pattern mining are provided. In one example implementation according to aspects of the present description, a computer-implemented method includes: constructing a patient record; transforming, by a processing system, the patient record into a bitmap representation; and analyzing, by the processing system, the bitmap to identify a sequential pattern within the patient record on a per patient basis.

Abstract: Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.

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: An apparatus, method and computer program product for proactive comprehensive generic risk screening. The method performs proactive comprehensive generic risk screening by implementing steps of training comprising steps of receiving cross domain risks and features, optimizing linkage regularization using the received features and the received cross domain risks, said linkage regularization comprising multi-task predictive model training, feature selection and ranking, risk association learning and risk association selection, and outputting patient risk scores, identified high risk patients, risk factors for risks and risk groups, and risk groups and risk associations and calculating risk score for an individual patient comprising steps of receiving individual features comprising patient information, performing said linkage regularization using the received individual features and outputting patient risk scores for said individual patient, and high risk for said individual patient.

Abstract: A method and system for time varying risk profiling from sensor data includes receiving data time series from a plurality of sensors associated with a single patient, identifying events from the data, wherein an event is a transition between two states in the data of a sensor, formulating event prediction as a discrete state transition task using Markov jump processes to handle irregular sampling rates, estimating a transition density function for time varying continuous event probability using a hierarchical Bayesian model, and predicting risk events for the single patient by applying the hierarchical Bayesian model.

Abstract: The present invention provides methods, systems and devices for drug-drug interaction and adverse event analysis. Chemical structures for one or more pairs of drugs are obtained from at least one of a user and a database. A chemical fingerprint is generated for each drug of the one or more pairs of drugs based on the chemical structure of the drug. Drug-drug interaction prediction is performed for each pair of drugs based on the chemical fingerprints. Adverse event prediction is performed based on the predicted drug-drug interaction for the one or more pairs of drugs.

Abstract: An embodiment of the invention receives input including a list of drugs, drug characteristics of each drug, and known drug-disease associations including a disease and a drug having a threshold efficacy for treating the disease. For each drug in the list of drugs, a processor predicts whether the drug meets a threshold efficacy for treating a first disease based on the drug characteristics and the drug-disease associations. For each drug in the list of drugs, the processor predicts whether the drug meets a threshold efficacy for treating a second disease based on the drug characteristics and the predicting of whether the drug meets the threshold efficacy for treating the first disease. Output is generated output based on the predictions, the output including an identified drug-disease association, an identified disease-disease association, an identified chemical fingerprint for the first disease, and an identified chemical fingerprint for the second disease.

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: In one embodiment, a computer-implemented method includes transforming a plurality of electronic health records into a plurality of temporal graphs indicating an order in which events observed in the plurality of electronic health records occur and learning a temporal pattern from the plurality of temporal graphs, wherein the temporal pattern indicates an order of events that is observed to occur repeatedly across the plurality of temporal graphs.

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: 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: Thermochromic coatings, films and materials that aid in a) reducing surface temperature of a structure or composite material when environmental temperature is relatively high and b) increasing surface temperature under relatively low environmental temperature. Surface temperature modulation is achieved using a synergistic combination of thermochromic materials and light scattering components.

Abstract: Embodiments are directed to a method of identifying individual-level risk factors. The method identifies a set of global risk factors for a risk target from population data, and identifies, based on the set of global risk factors, members from the population data having at least one clinical trait within a predetermined range of at least one clinical trait of an individual of interest. The method trains a personalized predictive model for the risk target based on the set of global risk factors and the member from the population data having at least one clinical trait within the a predetermined range. The method determines, based on a relevancy assessment of each of the set of global risk factors for the individual of interest, a subset of the set of global risk factors, wherein the subset comprises a set of individual risk factors for the individual of interest.

Abstract: Embodiments are directed to a method of identifying individual-level risk factors. The method identifies a set of global risk factors for a risk target from population data, and identifies, based on the set of global risk factors, members from the population data having at least one clinical trait within a predetermined range of at least one clinical trait of an individual of interest. The method trains a personalized predictive model for the risk target based on the set of global risk factors and the member from the population data having at least one clinical trait within the a predetermined range. The method determines, based on a relevancy assessment of each of the set of global risk factors for the individual of interest, a subset of the set of global risk factors, wherein the subset comprises a set of individual risk factors for the individual of interest.