Predicting Human Behaviour with Recurrent Neural Networks

Predicting Pronunciations with Syllabification and Stress with Recurrent Neural Networks

Word pronunciations, consisting of phoneme sequences and the associated syllabification and stress patterns, are vital for both speech recognition and text-to-speech (TTS) systems. For speech recognition phoneme sequences for words may be learned from audio data. We train recurrent neural network (RNN) based models to predict the syllabification and stress pattern for such pronunciations making...

Predicting Medications from Diagnostic Codes with Recurrent Neural Networks

It is a surprising fact that electronic medical records are failing at one of their primary purposes, that of tracking the set of medications that the patient is actively taking. Studies estimate that up to 50% of such lists omit active drugs, and that up to 25% of all active medications do not appear on the appropriate patient list. Manual efforts to maintain these lists involve a great deal o...

Nonlinear Structural Behaviour Identification using Digital Recurrent Neural Networks

Primljeno (Received): 2011-10-10 Prihvaćeno (Accepted): 2011-12-21 Original scientific paper Dynamical systems contain nonlinear relations which are difficult to model with conventional techniques. Hence, efficient nonlinear models are needed for system analysis, optimization, simulation and diagnosis of nonlinear systems. In recent years, computational-intelligence techniques such as neural ne...

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medicat...

Concerning the formation of chaotic behaviour in recurrent neural networks