Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving information retrieval from electronic health records using
  dynamic and multi-collaborative filtering   Due to the rapid growth of information available about individual patients,
most physicians suffer from information overload when they review patient
information in health information technology systems. In this manuscript, we
present a novel hybrid dynamic and multi-collaborative filtering method to
improve information retrieval from electronic health records. This method
recommends relevant information from electronic health records for physicians
during patient visits. It models information search dynamics using a Markov
model. It also leverages the key idea of collaborative filtering, originating
from Recommender Systems, to prioritize information based on various
similarities among physicians, patients and information items. We tested this
new method using real electronic health record data from the Indiana Network
for Patient Care. Our experimental results demonstrated that for 46.7% of
testing cases, this new method is able to correctly prioritize relevant
information among top-5 recommendations that physicians are truly interested
in.

8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th