Abstract: An embodiment collects a first set of patient data and a first set of treatment data associated with a patient population treated with spinal cord stimulation. The embodiment clusters the patient population into a plurality of cohorts. The embodiment generates a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts. The embodiment generates a plurality of actions using a second set of treatment data associated with the cohort. The embodiment determines, based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states. The embodiment generates, based on the plurality of probabilities, a stimulator action policy for a patient in the cohort.

Abstract: An embodiment collects a first set of patient data and a first set of treatment data associated with a patient population treated with neuromodulation. The embodiment clusters the patient population into a plurality of cohorts. The embodiment generates a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts. The embodiment generates a plurality of actions using a second set of treatment data associated with the cohort. The embodiment determines, based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states. The embodiment generates, based on the plurality of probabilities, a neuromodulator action policy for a patient in the cohort.

Abstract: A method for building a quantum computing circuit optimizes qubit routing in the circuit. A computer processor receives a plurality of qubits and an initial input circuit layer. Layers of quantum sub-circuits are extracted from the initial input circuit layer. Adjacency matrices are built for the layers of quantum sub-circuits. A cost function is determined for the extracted layers, based on the number of constraints violations determined by the doubly stochastic matrices. In addition, a final quantum circuit topology is selected based on the cost function of the extracted layers.

Abstract: Data associated with a patient undergoing medical treatment can be received. The data can pertain to a use of the medical treatment and a state of the patient. A subset of the data pertaining to the state of the patient can be identified as context. A function can be learned that relates the context to a reward derived from the medical treatment. The function can be used, based on a current state of the patient, to identify a type of treatment to deliver to the patient.

Abstract: Mechanisms are provided for training a machine learning computer model. The mechanisms execute a first initialization of machine learning training logic based on a determination of propensity scores for each output, of a plurality of predetermined outputs, of a machine learning computer model, the propensity scores being determined from historical data. The mechanisms execute a second initialization of the machine learning training logic by performing a trimmed optimization of the machine learning training logic, based on the historical data, to estimate initial parameters of the machine learning computer model. The resulting initialized machine learning training logic is executed on the machine learning computer model to train the machine learning computer model which is then deployed.

Abstract: Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Abstract: Embodiments for identifying stochastic models representing the individual decision makers in a computing environment by a processor. One or more non-deterministic (stochastic, probabilistic) models may be identified according to a sequence of outcomes from decisions of each of a plurality of decision makers.

Abstract: Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Abstract: Embodiments are provided for providing a continuous knowledge graph in a computing system by a processor. One or more weighted values of an edge between a pair of entities in a knowledge graph may be predicted based on one or more candidate statements. A confidence score may be generated for the one or more predicted weighted values.

Abstract: Embodiments for implementing intelligent risk detection and mitigation in a transport network by a processor. Data gathered from a plurality of data sources relating to an entity and a selected region of interest may be analyzed. Behavior of an entity, in relation to a risk event, may be learned and interpreted based on a plurality of identified contextual factors, geographical data, current data, historical data, a learned risk event model, or a combination thereof. One or more mitigation actions may be performed to mitigate risk of occurrence or a possible negative impact of the risk event caused at least in part by the behavior of the entity.

Abstract: In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

Abstract: Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

Abstract: A technique relates to coronary reconstruction. Angiogram data associated with cardiac catheterization of an artery for a subject is received, where a contrasting agent is used during the cardiac catheterization. Frames that match from the angiogram data are selected, the frames being from different views. Two-dimensional ordered point clouds for the frames are formed. A three-dimensional ordered point cloud of a reconstructed coronary artery for the subject from the two-dimensional ordered point clouds of the frames is formed. Observed contrast motion caused by the contrasting agent onto the reconstructed coronary artery is mapped, the reconstructed coronary artery thereby providing a three-dimensional model of the artery of the subject during the cardiac catheterization.

Abstract: Systems and techniques that facilitate variable structure reinforcement learning are provided. In various embodiments, a system can comprise a data component that can access state information of a machine learning environment. In various instances, the system can further comprise a selection component that can select a reinforcement learning model from a set of available reinforcement learning models based on the state information. In various embodiments, the system can further comprise a model library component, which can respectively correlate the set of available reinforcement learning models with a set of environment assumptions. In various embodiments, the selection component can perform a statistical hypothesis test based on the state information. In various aspects, the selection component can identify an environment assumption in the set of environment assumptions that is consistent with results of the statistical hypothesis test.

Abstract: Embodiments are provided for identification of a section of bodily tissue as either a candidate or a non-candidate for pathology tests. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include a feature composition component that generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue. The computer-executable components also can include a classification component that generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector. The classification attribute designates the section of bodily tissue as one of biopsy-candidate or non-biopsy-candidate.

Abstract: Embodiments for implementing intelligent boundary delineation of a region of interest of an organism in two spatial dimensions in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. One or more regions of interest having one or more perfusion patterns may be identified from the time series data. Boundaries of the one or more regions of interest may be delineated into at least two spatial dimensions, wherein the boundaries of the one or more regions of interest include one or more selected labels.

Abstract: In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

Abstract: In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

Abstract: A method for imaging a coronary arterial system of an individual includes releasing, using an actuator, pulses of a radio-opaque dye into a coronary arterial tree of the individual. The method further includes obtaining, using an image capture device, a sequence of invasive coronary x-ray angiogram images over time of the pulses of the radio-opaque dye. The method also includes tracking, using a processor, the pulses through the sequence of invasive coronary x-ray angiogram images and locating the pulses on a three dimensional (3D) structural model of the coronary arterial system to generate a three dimensional (3D) functional model of the coronary arterial system that shows a trajectory of the dye as it flows through different arterial branches.

Abstract: Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. The one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models, representing spatio-temporal behavior of the contrast agent reflected by the time series data, and by using a machine learning operation.