Abstract: Exemplary methods predict the locations of individuals in buildings over time based on Wi-Fi access point connectivity and other reference information. Predicted locations are used to predict risk of contact, for example of infected persons with others in public spaces.

Abstract: Exemplary methods predict the locations of individuals in buildings over time based on Wi-Fi access point connectivity and other reference information. Predicted locations are used to predict risk of contact, for example of infected persons with others in public spaces.

Abstract: Some embodiments of the present invention evaluate claim submissions. Prediction(s) are generated that employ machine learning engine(s) and/or expert models executing on processor(s). The prediction(s) may forecast if claim data when submitted to a payer will result in at least one of the following: an approved submission; a denied submission; and an apparent payment variation. The machine learning engine(s) may be updated using use the prediction. Labeled data may be generated by classifying patient claim data residing in a database of claim records and histories with at least one of the following: an anomaly detection label; a contract based label; and a combination of the above. Claims classification model(s) may be trained using the labeled data. Predictive paid amount model(s) may be created that employ at least the labeled data and/or an amount paid on a claim.