38 research outputs found
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according to patients' history records. This paper develops deep
learning techniques for clinical endpoint prediction, which are effective in
many practical applications. However, the problem is very challenging since
patients' history records contain multiple heterogeneous temporal events such
as lab tests, diagnosis, and drug administrations. The visiting patterns of
different types of events vary significantly, and there exist complex nonlinear
relationships between different events. In this paper, we propose a novel model
for learning the joint representation of heterogeneous temporal events. The
model adds a new gate to control the visiting rates of different events which
effectively models the irregular patterns of different events and their
nonlinear correlations. Experiment results with real-world clinical data on the
tasks of predicting death and abnormal lab tests prove the effectiveness of our
proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201
HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
International Classification of Diseases (ICD) is a set of classification
codes for medical records. Automated ICD coding, which assigns unique
International Classification of Diseases codes with each medical record, is
widely used recently for its efficiency and error-prone avoidance. However,
there are challenges that remain such as heterogeneity, label unbalance, and
complex relationships between ICD codes. In this work, we proposed a novel
Bidirectional Hierarchy Framework(HieNet) to address the challenges.
Specifically, a personalized PageRank routine is developed to capture the
co-relation of codes, a bidirectional hierarchy passage encoder to capture the
codes' hierarchical representations, and a progressive predicting method is
then proposed to narrow down the semantic searching space of prediction. We
validate our method on two widely used datasets. Experimental results on two
authoritative public datasets demonstrate that our proposed method boosts
state-of-the-art performance by a large margin