How To Predict ICU Mortality with Digital Health Data, DL4J, Apache Spark and Cloudera - Cloudera Engineering Blog

A recent example of such work is the ICLR 2016 paper "Learning to Diagnose with LSTM Recurrent Neural Networks" (of which Mr. Kale is a joint first author in his capacity as a PhD candidate at the USC Information Science Institute). In it, the authors trained a LSTM RNN or LSTM, to classify acute care diseases such as respiratory distress in critically ill children. The RNN (and the more complex LSTM RNN) is a neural net architecture with recurrent connections between hidden states, giving it a form of persistent state (or "memory") across sequential inputs. These connections enable RNNs to detect relationships not only between inputs, e.g., heart rate and blood pressure, but also over time, e.g., between a patient's state at time of admission and later in an ICU stay. This makes it especially well-suited to health problems, which often involve modeling changes over time.