Abstract: Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.

Abstract: A computer system manages a health condition based on conditions of an environment. Health information of a user is analyzed to determine a health condition affected by environmental conditions. One or more events for the user are obtained based on personal information. The environmental conditions for one or more event locations are determined. One or more preventive items are indicated for the user to attend the one or more events in order for the health condition to tolerate the environmental conditions of the one or more event locations. Embodiments of the present invention further include a method and program product for managing a health condition based on conditions of an environment in substantially the same manner described above.

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: Mechanisms are provided to adapt computer modeling of an infectious disease based on noisy data and perform hyperlocal prediction of infectious disease dynamics and risks. Case report data is received and a trained background noise computer model is applied to generate first prediction results predicting infectious disease dynamics. The trained background noise computer model is trained to model infectious disease dynamics assuming that there is no community spread of the infectious disease. A first error measure of the first prediction results is determined and, in response to the first error measure being lower than a threshold value, the first prediction results are selected to output as predicted infectious disease dynamics. In response to the first error measure being equal/greater than the threshold value, second prediction results are selected. The second prediction results are generated by applying a trained infectious disease computer model to the received case report data.

Abstract: Mechanisms are provided to perform automatic case intervention detection in infectious disease case reports and for configuring an infectious disease computer model based on the automatic intervention detection. Case report data is received and a time ordered curve of the case report data is generated. One or more inflection points in the time ordered curve are identified. The one or more inflection points in the time ordered curve are correlated with one or more intervention entries specified in time stamped infectious disease intervention data, the one or more intervention entries specifying interventions implemented by authorities to control spread of the infectious disease. One or more model parameters of an infectious disease computer model are configured based on results of correlating the one or more inflection points with the one or more intervention entries.

Abstract: Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.

Abstract: Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.

Abstract: Mechanisms are provided for hyperlocal prediction of epidemic dynamics and risks. Regional machine learning training is performed on an infectious disease computer model at least by: receiving first case report data; pre-processing the first case report data to remove noise at least by applying a smoothening algorithm to form first smoothed data; aggregating the first smoothed data into regional data, wherein aggregating the first smoothed data comprises correlating the first smoothed data to a target region corresponding to a population; and training the model using the regional data. The trained model is executed on new second case report data for the target region and automatic monitoring of performance of the model is performed according to a prediction accuracy of the model. In response to the prediction accuracy being below a predetermined threshold, automatic retraining is initiated.

Abstract: Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.

Abstract: A mechanism is provided in a data processing system to implement a model pipeline for predicting changes in disease transmission rate using a spatial temporal epidemiological model. The mechanism receives input data comprising disease case data for a disease and mobility data and prepares the input data to generate a training dataset, a validation dataset, and a test dataset. A feature selection module performs feature selection on the input data to select a first set of features for a binary classification computer model, a second set of features for a three-level classification computer model, and a third set of features for a regression computer model.

Abstract: For locations along a route a user will be traveling, the alert system for environmental changes compares first and second sets of images associated with first and second timestamps, respectively. The alert system determines degrees of environmental changes for the locations based on the comparisons. The alert system then generates and sends an alert to a user device. In determining the degrees of environment changes, the alert system retrieves first and second set of images matching a given location and associated with first and second timestamps. The alert system identifies first and second sets of objects and extracts first and second sets of attributes for the first and second sets of images. The alert system compares the first and second sets of attributes and the first and second set of objects, and determines a given degree of environmental changes at the given location based on the comparisons.

Abstract: A computer system monitors users to capture contextual and environmental data for managing adverse events of those users. A level of risk for occurrence of an adverse event from performing a medical related activity is determined based on the medical related activity, a medical profile, and a risk profile of the user. The user is monitored to capture environmental and contextual information for the adverse event. The captured information is stored to associate the captured information with the adverse event. In response to occurrence of the adverse event, the user is prompted to provide information pertaining to conditions surrounding the adverse event. The stored information for the adverse event is updated with the user-provided information, and is transmitted to a provider associated with the medical related activity. Embodiments of the present invention further include a method and program product for managing adverse events in substantially the same manner described above.

Abstract: A bioinformatics system for autonomous hypothesis generation for relatedness of seemingly independent concepts having tenuous or no prior known relationship in a knowledgebase. Independent concept pairs are identified in a question, and they are evaluated in light of lists of their similar concepts, and in light input documents, word embeddings, and co-occurrence matrices that are organized as a time series. Relationships between the independent concepts are hypothesized based on temporal analysis of relatedness measures of their similar concepts, measured via candidate connecting concepts.

Abstract: A computer system manages a health condition based on conditions of an environment. Health information of a user is analyzed to determine a health condition affected by environmental conditions. One or more events for the user are obtained based on personal information. The environmental conditions for one or more event locations are determined. One or more preventive items are indicated for the user to attend the one or more events in order for the health condition to tolerate the environmental conditions of the one or more event locations. Embodiments of the present invention further include a method and program product for managing a health condition based on conditions of an environment in substantially the same manner described above.

Abstract: A computer system monitors users to capture contextual and environmental data for managing adverse events of those users. A level of risk for occurrence of an adverse event from performing a medical related activity is determined based on the medical related activity, a medical profile, and a risk profile of the user. The user is monitored to capture environmental and contextual information for the adverse event. The captured information is stored to associate the captured information with the adverse event. In response to occurrence of the adverse event, the user is prompted to provide information pertaining to conditions surrounding the adverse event. The stored information for the adverse event is updated with the user-provided information, and is transmitted to a provider associated with the medical related activity. Embodiments of the present invention further include a method and program product for managing adverse events in substantially the same manner described above.

Abstract: Communication session data pertaining to a first user can be received during an electronic communication session in which the first user and at least a second user participate. The communication session data can be analyzed. Based on the analysis, a concentration level score of the first user can be determined. A contextual indicator indicating the concentration level score of the first user can be generated. The contextual indicator can be communicated to a second client device used by the second user. Communicating the first contextual indicator to the second client device can initiate the second client device to present, in a user interface used by the second user for the communication session, a first user interface element indicating the concentration level score of the first user.

Abstract: Communication session data pertaining to a first user can be received during an electronic communication session in which the first user and at least a second user participate. The communication session data can be analyzed. Based on the analysis, a concentration level score of the first user can be determined. A contextual indicator indicating the concentration level score of the first user can be generated. The contextual indicator can be communicated to a second client device used by the second user. Communicating the first contextual indicator to the second client device can initiate the second client device to present, in a user interface used by the second user for the communication session, a first user interface element indicating the concentration level score of the first user.

Abstract: A computer system generates and manages clinical studies. A first collection of clinical documents is analyzed to extract clinical study goals and corresponding clinical study design components. A second collection of documents is analyzed to extract value sets indicating a mapping between clinical concepts and data elements of clinical information. The extracted clinical study design information and value sets are stored in a hierarchical structure within a repository that relates the clinical study goals, design components, concepts, and the data elements. A new clinical study is generated for a desired clinical study goal based on pattern recognition applied to the extracted clinical study design information and value sets in the repository using the repository. Embodiments of the present invention further include a method and program product for generating and managing clinical studies in substantially the same manner described above.

Abstract: For locations along a route a user will be traveling, the alert system for environmental changes compares first and second sets of images associated with first and second timestamps, respectively. The alert system determines degrees of environmental changes for the locations based on the comparisons. The alert system then generates and sends an alert to a user device. In determining the degrees of environment changes, the alert system retrieves first and second set of images matching a given location and associated with first and second timestamps. The alert system identifies first and second sets of objects and extracts first and second sets of attributes for the first and second sets of images. The alert system compares the first and second sets of attributes and the first and second set of objects, and determines a given degree of environmental changes at the given location based on the comparisons.

Abstract: A method, computer system, and a computer program product for pre-authorization is provided. The present invention may include identifying a treatment for a medical condition that yields a positive outcome. The present invention may also include identifying a plurality of key features in the identified treatment. The present invention may then include identifying a plurality of features within the plurality of key features that can be applied to a patient. The present invention may further include identifying a stakeholder based on the identified plurality of features applied to the patient. The present invention may also include authorizing the identified stakeholder. The present invention may then include creating a new block based on the authorized stakeholder. The present invention may further include adding the new block to a blockchain network for processing authorizations.