Abstract
The patient's medication history and status changes play essential roles in medical treatment. A notable amount of medication status information typically resides in unstructured clinical narratives that require a sophisticated approach to automated classification. In this paper, we investigated rule-based and machine learning methods of medication status change classification from clinical free text. We also examined the impact of balancing training data in machine learning classification when using the data from skewed class distribution.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 762-766 |
| Number of pages | 5 |
| Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
| Volume | 2010 |
| State | Published - 2010 |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Classification of medication status change in clinical narratives. / Sohn, Sunghwan; Murphy, Sean P.; Masanz, James J.; Kocher, Jean-Pierre; Savova, Guergana K.
In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, Vol. 2010, 2010, p. 762-766.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Classification of medication status change in clinical narratives
AU - Sohn, Sunghwan
AU - Murphy, Sean P.
AU - Masanz, James J.
AU - Kocher, Jean-Pierre
AU - Savova, Guergana K.
PY - 2010
Y1 - 2010
N2 - The patient's medication history and status changes play essential roles in medical treatment. A notable amount of medication status information typically resides in unstructured clinical narratives that require a sophisticated approach to automated classification. In this paper, we investigated rule-based and machine learning methods of medication status change classification from clinical free text. We also examined the impact of balancing training data in machine learning classification when using the data from skewed class distribution.
AB - The patient's medication history and status changes play essential roles in medical treatment. A notable amount of medication status information typically resides in unstructured clinical narratives that require a sophisticated approach to automated classification. In this paper, we investigated rule-based and machine learning methods of medication status change classification from clinical free text. We also examined the impact of balancing training data in machine learning classification when using the data from skewed class distribution.
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UR - http://www.scopus.com/inward/citedby.url?scp=84890446094&partnerID=8YFLogxK
M3 - Article
C2 - 21347081
AN - SCOPUS:84890446094
VL - 2010
SP - 762
EP - 766
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
SN - 1559-4076
ER -