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Stabilizing sparse Cox model using clinical structures in electronic medical records
conference contribution
posted on 2014-08-24, 00:00 authored by Truyen TranTruyen Tran, Shivapratap Gopakumar, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshStability in clinical prediction models is crucial for
transferability between studies, yet has received little
attention. The problem is paramount in highdimensional
data which invites sparse models with feature
selection capability. We introduce an effective
method to stabilize sparse Cox model of time-to-events
using clinical structures inherent in Electronic Medical
Records (EMR). Model estimation is stabilized using
a feature graph derived from two types of EMR structures:
temporal structure of disease and intervention recurrences,
and hierarchical structure of medical knowledge
and practices. We demonstrate the efficacy of the
method in predicting time-to-readmission of heart failure
patients. On two stability measures – the Jaccard
index and the Consistency index – the use of clinical
structures significantly increased feature stability without
hurting discriminative power. Our model reported
a competitive AUC of 0.64 (95% CIs: [0.58,0.69]) for 6
months prediction.
transferability between studies, yet has received little
attention. The problem is paramount in highdimensional
data which invites sparse models with feature
selection capability. We introduce an effective
method to stabilize sparse Cox model of time-to-events
using clinical structures inherent in Electronic Medical
Records (EMR). Model estimation is stabilized using
a feature graph derived from two types of EMR structures:
temporal structure of disease and intervention recurrences,
and hierarchical structure of medical knowledge
and practices. We demonstrate the efficacy of the
method in predicting time-to-readmission of heart failure
patients. On two stability measures – the Jaccard
index and the Consistency index – the use of clinical
structures significantly increased feature stability without
hurting discriminative power. Our model reported
a competitive AUC of 0.64 (95% CIs: [0.58,0.69]) for 6
months prediction.
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Event
Pattern Recognition for Healthcare Analytics. International Workshop (2nd : 2014 : Stockholm, Sweden)Pagination
1 - 4Publisher
International Association of Pattern RecognitionLocation
Stockholm, SwedenPlace of publication
[Stockholm, Sweden]Start date
2014-08-24End date
2014-08-24Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
[2014, The Conference]Title of proceedings
IAPR 2014: Proceedings of 2nd International Workshop on Pattern Recognition for Healthcare AnalyticsUsage metrics
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