Highlights LSTM, RNN model for prediction of Alzheimer's diseases(AD) is developed from EMR data Information from 3 EMR domains were used: conditions, measurements and drugs We created positive AD cohorts using relevant medical knowledge as model inputs Selection of relevant input cohorts was crucial for overall RNN model prediction We efficiently applied the drugs and the measurement domain in prediction of AD

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This paper describes a Weather Impact Model (WIM) capable of serving a variety of predictive applications ranging from real-time operation and dayahead operation planning, to asset and outage management. The proposed model is capable of combining various weather parameters into different weather impact features of interest to a specific application. This work focuses on the development of a universal weather impacts model based on the logistic regression embedded in a Geographic Information System (GIS). It is capable of merging massive data sets from historical outage and weather data, to real-time weather forecast and network monitoring measurements, into a feature known as weather hazard probability. The examples of the outage and asset management applications are used to illustrate the model capabilities.

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