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: 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: 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: 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: 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: Techniques are described that facilitate determining potential cancer gene therapy targets by joint modeling of cancer survival events. In one embodiment, a computer-implemented comprises employing, by a device operatively coupled to a processor, a multi-task learning model to determine active genetic factors respectively associated with different types of cancer based on cancer survival data and patient genomic data for groups of patients that respectively survived the different types of cancer. The computer-implemented method further comprises, determining, by the device, common active genetic factors of the active genetic factors that are shared between two or more types of cancer of the different types of cancer.

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: 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 (or method) for generation and employment of disease progression model(s) that facilitates identifying and indexing discriminative features for disease progression in observational data. The disease progression prediction system comprises a processor that executes computer executable components stored in memory. A receiving component receives and learns observational patient data. A model generation component builds a preliminary disease progression model. An identification component identifies discriminative clinical features for different disease stages. A ranking component ranks discriminative powers of clinical features for respective pairs of disease stages; wherein the model generation component employs the ranked features to generate a final disease progression model.

Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.

Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.

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: A method for predicting adverse drug reactions (ADRs). Structures represented in three-dimensions were prepared for small drug molecules and unique human proteins and binding scores between them were generated using molecular docking. Machine learning models were developed using the molecular docking features to predict ADRs. Using the machine learning models, it can successfully predict a drug-induced ADR based on drug-target interaction features and known drug-ADR relationships. By further analyzing the binding proteins that are top ranked or closely associated with the ADRs, there may be found possible interpretation of the ADR mechanisms. The machine learning ADR models based on molecular docking features not only assist with ADR prediction for new or existing known drug molecules, but also have the advantage of providing possible explanation or hypothesis for the underlying mechanisms of ADRs.

Abstract: A system framework and method for predicting adverse drug reactions (ADRs). Structures represented in three-dimensions were prepared for small drug molecules and unique human proteins and binding scores between them were generated using molecular docking. Machine learning models were developed using the molecular docking features to predict ADRs. Using the machine learning models, it can successfully predict a drug-induced ADR based on drug- target interaction features and known drug-ADR relationships. By further analyzing the binding proteins that are top ranked or closely associated with the ADRs, there may be found possible interpretation of the ADR mechanisms. The machine learning ADR models based on molecular docking features not only assist with ADR prediction for new or existing known drug molecules, but also have the advantage of providing possible explanation or hypothesis for the underlying mechanisms of ADRs.

Abstract: Embodiments describing an approach to receiving patient registry data and creating at least one control model based on the patient registry data. Transforming patient registry data into at least one prediction confident interval based on the at least one control model. Transforming the at least one prediction confident interval into at least one robust assessment score, and outputting the at least one robust assessment score for measuring disease progression indicators.

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 of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.