  Mortality prediction of diverse rare diseases using electronic health record
(EHR) data is a crucial task for intelligent healthcare. However, data
insufficiency and the clinical diversity of rare diseases make it hard for
directly training deep learning models on individual disease data or all the
data from different diseases. Mortality prediction for these patients with
different diseases can be viewed as a multi-task learning problem with
insufficient data and large task number. But the tasks with little training
data also make it hard to train task-specific modules in multi-task learning
models. To address the challenges of data insufficiency and task diversity, we
propose an initialization-sharing multi-task learning method (Ada-Sit) which
learns the parameter initialization for fast adaptation to dynamically measured
similar tasks. We use Ada-Sit to train long short-term memory networks (LSTM)
based prediction models on longitudinal EHR data. And experimental results
demonstrate that the proposed model is effective for mortality prediction of
diverse rare diseases.
