Abstract: A method including: receiving multiple time series of data, each time series representing a health-related temporal impact of a historical set of elements on an adverse health-related condition; automatically deconvolving the health-related temporal impact, to determine an individual contribution of each element of each of the historical sets to the health-related temporal impact of the respective historical set, wherein the deconvolving is performed respectively of an additive impact and/or a multiplicative impact of elements; receiving a selection of a new set of elements which is different from any one of the historical sets; and based on the determined individual contributions, automatically predicting a temporal impact of the new set of elements, wherein the new set consists of elements selected from said group but which do not jointly constitute any one of the historical sets.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

Abstract: A system provides an intervention for a user and comprises at least one processor. The system monitors behavior and context of a user to generate a behavior history. One or more models are utilized to determine an intervention for the user to induce a behavior modification, wherein the one or more models map interventions to user context and behavior and utilize the behavior history to determine an effective intervention for the user. The intervention is provided to the user and feedback is received in response to the intervention. The one or more models are updated based on the feedback. Embodiments of the present invention further include a method and computer program product for providing an intervention to a user in substantially the same manner described above.

Abstract: A system provides an intervention for a user and comprises at least one processor. The system monitors behavior and context of a user to generate a behavior history. One or more models are utilized to determine an intervention for the user to induce a behavior modification, wherein the one or more models map interventions to user context and behavior and utilize the behavior history to determine an effective intervention for the user. The intervention is provided to the user and feedback is received in response to the intervention. The one or more models are updated based on the feedback. Embodiments of the present invention further include a method and computer program product for providing an intervention to a user in substantially the same manner described above.

Abstract: Characterizing subpopulations by their response to a given exposure relative to an alternative. Data for a set of subjects is received, including two exposures, two outcomes, and a set of characteristics. For a number of subsets an outcome model that estimates the probability of an outcome, given an exposure and the characteristics, is trained; an individual odds ratio (iOR), based on the outcome model, is computed; a sparse model that classifies subjects into a high-iOR group or another group is trained; the characteristics used in the sparse model are recorded; and, based on the recorded characteristics, a primary set of characteristics is selected. Another outcome model, based on the set of subjects and the characteristics, is trained. An iOR based on the other outcome model is computed. A sparse model for classifying subjects into a high-iOR group or another group, using the primary set of characteristics, is trained.

Abstract: Modelling disease progression using non-clinical information proxies for clinical information, by accessing a computer-based Bayesian model of the progression of a disease, adapting the Bayesian model to include one or more clinical factors that are believed to influence progression of the disease, adapting the Bayesian model to include one or more non-clinical proxies for one or more clinical factors that are believed to influence progression of the disease, identifying interdependencies among variables of the Bayesian model based on a meta-analysis of literature associated with any of the disease, the clinical factors, and the non-clinical proxies, providing values for any of the variables of the Bayesian model, and presenting any portion of the Bayesian model via a computer-based output device.

Abstract: A computer-implemented method and apparatus for assessing treatment adherence by patients, the method comprising: receiving a model providing statistical significance of patients' response to treatment, the model based on treatment assigned to the patients, wherein the patients are diagnosed with a disease; computing by the computerized device a p-value for a result received for a patient diagnosed with the disease and being treated by the treatment, by applying the model to at least one patient; and issuing an alert responsive to the p-value being indicative of the result being unexpected beyond a threshold.

Abstract: A method, a system and a computer program product suitable for use in a manufacturing environment comprising a multiplicity of nominally identical independent tools. A computing device generates a multi dimensional array of process trace data derived from at least one of the independent tools, wherein, the array includes data representing a first dimension comprising a list of steps in a manufacturing recipe and data representing a second dimension comprising a list of a set of sensors generating measurements from at least one of the independent tools. The computing device conducts an analysis on at least one preselected subset of the multi dimensional array for the purpose of evaluating at least one operating characteristic of at least one of the independent tools. The computing device presents results of the analysis via a set of hierarchically linked and browseable graphics.

Abstract: A method, a system and a computer program product suitable for use in a manufacturing environment comprising a multiplicity of nominally identical independent tools. A computing device generates a multi dimensional array of process trace data derived from at least one of the independent tools, wherein, the array includes data representing a first dimension comprising a list of steps in a manufacturing recipe and data representing a second dimension comprising a list of a set of sensors generating measurements from at least one of the independent tools. The computing device conducts an analysis on at least one preselected subset of the multi dimensional array for the purpose of evaluating at least one operating characteristic of at least one of the independent tools. The computing device presents results of the analysis via a set of hierarchically linked and browseable graphics.

Abstract: A method and system for evaluating a performance of a semiconductor manufacturing tool while manufacturing microelectronic devices are disclosed. At least one report is generated based on executions of at least one statistical test. The report includes at least one heat map having rows that correspond to sensors, columns that correspond to trace data obtained during recipe steps, and cells at the intersection of the rows and the columns. At least one sensor in the tool obtains trace data of a recipe step while manufacturing at least one microelectronic device. A computing device analyzes the obtained trace data to determine a level of operational significance found in the data and assigns a score to the trace data that indicates a level of operational significance. Then, the computing device places the score in a corresponding cell of the heat map. A user uses the cell for evaluating the tool performance.

Abstract: Techniques for estimating a quality of one or more wafers are presented. One or more first wafers comprising one or more first dies are tested. A probability of wafer failure is determined in accordance with one or more first test measurements of the one or more first dies. A pass status and/or a fail status of one or more second wafers is inferred by testing a select one or more second dies of the one or more second wafers and evaluating one or more second test measurements of the select one or more second dies in accordance with the determined probability of wafer failure.

Abstract: A method and system for evaluating a performance of a semiconductor manufacturing tool while manufacturing microelectronic devices are disclosed. At least one report is generated based on executions of at least one statistical test. The report includes at least one heat map having rows that correspond to sensors, columns that correspond to trace data obtained during recipe steps, and cells at the intersection of the rows and the columns. At least one sensor in the tool obtains trace data of a recipe step while manufacturing at least one microelectronic device. A computing device analyzes the obtained trace data to determine a level of operational significance found in the data and assigns a score to the trace data that indicates a level of operational significance. Then, the computing device places the score in a corresponding cell of the heat map. A user uses the cell for evaluating the tool performance.

Abstract: Techniques for estimating a quality of one or more wafers are presented. One or more first wafers comprising one or more first dies are tested. A probability of wafer failure is determined in accordance with one or more first test measurements of the one or more first dies. A pass status and/or a fail status of one or more second wafers is inferred by testing a select one or more second dies of the one or more second wafers and evaluating one or more second test measurements of the select one or more second dies in accordance with the determined probability of wafer failure.

Abstract: A method for wafer manufacturing process abnormalities detection, the method includes: generating a classifier in response to compression based similarities between relevant wafer manufacturing process information of pairs of wafers; and utilizing the classifier to detect wafer manufacturing process abnormalities.