1,306 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
Reconsideration of the QCD corrections to the decays into light hadrons using the principle of maximum conformality
In the paper, we analyze the decays into light hadrons at the
next-to-leading order QCD corrections by applying the principle of maximum
conformality (PMC). The relativistic correction at the -order level has been included in the discussion, which gives about
contribution to the ratio . The PMC, which satisfies the renormalization
group invariance, is designed to obtain a scale-fixed and scheme-independent
prediction at any fixed order. To avoid the confusion of treating -terms,
we transform the usual pQCD series into the one under the
minimal momentum space subtraction scheme. To compare with the prediction under
conventional scale setting, , after applying the PMC, we obtain
, where the
errors are squared averages of the ones caused by and . The PMC prediction agrees with the recent PDG value within errors, i.e.
. Thus we think the mismatching
of the prediction under conventional scale-setting with the data is due to
improper choice of scale, which however can be solved by using the PMC.Comment: 5 pages, 2 figure
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