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

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases