Abstract: A method, a computer program product, and a computer system predict medication adherence of a patient. The method includes identifying risk factors associated with medication adherence of the patient. The method includes determining a likely behaviour for medication adherence of the patient based on the identified risk factors and a temporal causal model. The temporal causal model is based on features of a patient cluster to which the patient belongs. The features are nodes in the temporal causal model. The likely behaviour is based on causality measures for each identified risk factor to the nodes. The method includes determining a current medication adherence value of the patient. The current medication adherence value is indicative of a ratio between an actual medication regiment and an expected medication regiment. The method includes determining a future medication adherence value of the patient based on the current medication adherence value and the causality measures.

Abstract: A mechanism is provided for implementing a dynamic context-based collaborative medical concept interpreter for automatically generating and presenting summarized explanations of medical concepts. The dynamic context-based collaborative medical concept interpreter performs natural language processing on a real-time patient-provider communication to identify one or more medical concepts referred to in the communication. The dynamic context-based collaborative medical concept interpreter adjusts one or more previous explanations for the one or more medical concepts referred to in the communication using a set of contextual factors. The dynamic context-based collaborative medical concept interpreter generates an abstractive summary that summarizes ranked explanations of the one or more medical concepts based on an original language used in the one or more previous explanations.

Abstract: A mechanism is provided in a data processing system to implement a personalized patient engagement engine.

Abstract: An aspect includes querying, by a processor, a plurality of model data from a distributed data source based at least in part on one or more user characteristics. A plurality of sensor data is gathered associated with a condition of a user. A policy is generated including an end goal and one or more sub-goals based at least in part on the model data and the sensor data. The policy is iteratively adapted based at least in part on one or more detected changes in the sensor data collected over a period of time to adjust at least one of the one or more sub-goals. The policy and the one or more sub-goals are provided to the user.

Abstract: A mechanism is provided in a data processing system to implement a personalized patient engagement engine.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: A mechanism is provided for implementing a dynamic context-based collaborative medical concept interpreter for automatically generating and presenting summarized explanations of medical concepts. The dynamic context-based collaborative medical concept interpreter performs natural language processing on a real-time patient-provider communication to identify one or more medical concepts referred to in the communication. The dynamic context-based collaborative medical concept interpreter adjusts one or more previous explanations for the one or more medical concepts referred to in the communication using a set of contextual factors. The dynamic context-based collaborative medical concept interpreter generates an abstractive summary that summarizes ranked explanations of the one or more medical concepts based on an original language used in the one or more previous explanations.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: A method, a computer program product, and a computer system predict medication adherence of a patient. The method includes identifying risk factors associated with medication adherence of the patient. The method includes determining a likely behaviour for medication adherence of the patient based on the identified risk factors and a temporal causal model. The temporal causal model is based on features of a patient cluster to which the patient belongs. The features are nodes in the temporal causal model. The likely behaviour is based on causality measures for each identified risk factor to the nodes. The method includes determining a current medication adherence value of the patient. The current medication adherence value is indicative of a ratio between an actual medication regiment and an expected medication regiment. The method includes determining a future medication adherence value of the patient based on the current medication adherence value and the causality measures.

Abstract: A computer-implemented method, computer program product, and system for determination of critical parts and component correlations in a circuit using a correlation graph and centrality analysis including; receiving a circuit layout portion of a larger circuit layout, converting the circuit layout portion into a correlation graph representing components as nodes and connecting wires as edges, determining, using ground truth and Naïve Bayes to determine correlation weighting, scaling the correlation graph to represent the larger circuit, and presenting the larger correlation graph on a graphical user interface (GUI).

Abstract: An aspect includes querying, by a processor, a plurality of model data from a distributed data source based at least in part on one or more user characteristics. A plurality of sensor data is gathered associated with a condition of a user. A policy is generated including an end goal and one or more sub-goals based at least in part on the model data and the sensor data. The policy is iteratively adapted based at least in part on one or more detected changes in the sensor data collected over a period of time to adjust at least one of the one or more sub-goals. The policy and the one or more sub-goals are provided to the user.

Abstract: A computer-implemented method, computer program product, and system for determination of critical parts and component correlations in a circuit using a correlation graph and centrality analysis including; receiving a circuit layout portion of a larger circuit layout, converting the circuit layout portion into a correlation graph representing components as nodes and connecting wires as edges, determining, using ground truth and Naïve Bayes to determine correlation weighting, scaling the correlation graph to represent the larger circuit, and presenting the larger correlation graph on a graphical user interface (GUI).

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: A method, system, and/or computer program product automatically abstracts and selects an optimal set of variance-related features that are indicative of an individual outcome and personalized plan selection in health care. An abstracted set of candidate variance-related patient features, which comprise temporally heteroskedastic features, is generated. Each patient feature from the abstracted set of candidate variance-related patient features is optimized by identifying a time period in which variances and heteroskedasticity of each patient feature are maximized, where the optimizing creates an optimal abstracted set of variance-related patient features from the time period in which the variances and heteroskedasticity of each patient feature are maximized. The optimal abstracted set of variance-related patient features is then used for a current patient to predict a particular outcome and/or to create a personalized health care treatment plan.