  In recent years, increasingly augmentation of health data, such as patient
Electronic Health Records (EHR), are becoming readily available. This provides
an unprecedented opportunity for knowledge discovery and data mining algorithms
to dig insights from them, which can, later on, be helpful to the improvement
of the quality of care delivery. Predictive modeling of clinical risk,
including in-hospital mortality, hospital readmission, chronic disease onset,
condition exacerbation, etc., from patient EHR, is one of the health data
analytic problems that attract most of the interests. The reason is not only
because the problem is important in clinical settings, but also there are
challenges working with EHR such as sparsity, irregularity, temporality, etc.
Different from applications in other domains such as computer vision and
natural language processing, the labeled data samples in medicine (patients)
are relatively limited, which creates lots of troubles for effective predictive
model learning, especially for complicated models such as deep learning. In
this paper, we propose MetaPred, a meta-learning for clinical risk prediction
from longitudinal patient EHRs. In particular, in order to predict the target
risk where there are limited data samples, we train a meta-learner from a set
of related risk prediction tasks which learns how a good predictor is learned.
The meta-learned can then be directly used in target risk prediction, and the
limited available samples can be used for further fine-tuning the model
performance. The effectiveness of MetaPred is tested on a real patient EHR
repository from Oregon Health & Science University. We are able to demonstrate
that with CNN and RNN as base predictors, MetaPred can achieve much better
performance for predicting target risk with low resources comparing with the
predictor trained on the limited samples available for this risk.
