Abstract: This application discloses a data writing method, device, a storage server and a computer readable storage medium, including: writing, when a write request is received, write data corresponding to the write request to a write buffer; acquiring historical access data of a data block corresponding to to-be-flushed data in the write buffer when a data flushing operation is triggered for the write buffer; determining whether the to-be-flushed data is write-only data based on the historical access data by using a pre-trained classifier; if yes, writing the to-be-flushed data to a hard disk drive; and if no, writing the to-be-flushed data to a cache. The data writing method provided by this application can effectively reduce the traffic of writing dirty data to the cache while reserving more space in the cache for the ordinary data, thereby improving the utilization of the cache space and the read hit rate of the cache.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate employing a probabilistic model to generate a continuous disease status index based on observational data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that employs a probabilistic model to generate probability distributions of disease states of a disease of an entity based on observational data of the entity. The computer executable components can further comprise an index component that generates a disease status index of the disease based on the probability distributions of the disease states.

Abstract: A request to execute a sequentially randomized controlled trial (sRCT) that relates to a subject regarding a population of humans is received. Datapoints from the population that are needed for the sRCT and factors that define the population as fitting the sRCT are identified. It is detected that a human within an interaction satisfies the factors and therefore is part of the population. During the interaction, the datapoints from the human that are needed for the sRCT are gathered in response to detecting that the human is part of the population, and treatment is randomly assigned if the interaction is also a treatment decision point.

Abstract: In an approach for building a machine learning model with a flexible prediction horizon, a processor gathers statistical data related to a disease from one or more regional sources. A processor clusters the statistical data according to a plurality of localized regional source similarity criteria and a plurality of region criteria. A processor builds a plurality of training models based on the clustered statistical data. A processor builds a plurality of feature vectors based on the plurality of localized regional source similarity criteria and the plurality of region criteria. A processor trains the plurality of training models separately against the plurality of feature vectors. A processor selects a best performing training model for each of the plurality of localized regional source similarity criteria and the plurality of region criteria based on a performance criterion. A processor tests the best performing training model to predict one or more future outcomes.

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: 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.

Abstract: This application discloses a data writing method, device, a storage server and a computer readable storage medium, including: writing, when a write request is received, write data corresponding to the write request to a write buffer; acquiring historical access data of a data block corresponding to to-be-flushed data in the write buffer when a data flushing operation is triggered for the write buffer; determining whether the to-be-flushed data is write-only data based on the historical access data by using a pre-trained classifier; if yes, writing the to-be-flushed data to a hard disk drive; and if no, writing the to-be-flushed data to a cache. The data writing method provided by this application can effectively reduce the traffic of writing dirty data to the cache while reserving more space in the cache for the ordinary data, thereby improving the utilization of the cache space and the read hit rate of the cache.

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: 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 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: Systems, computer-implemented methods, and computer program products to facilitate employing a probabilistic model to generate a continuous disease status index based on observational data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that employs a probabilistic model to generate probability distributions of disease states of a disease of an entity based on observational data of the entity. The computer executable components can further comprise an index component that generates a disease status index of the disease based on the probability distributions of the disease states.

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: 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: 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: 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: 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: 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 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 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.