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J Biomed Inform. 2015 Feb;53:147-55. doi: 10.1016/j.jbi.2014.10.004. Epub 2014 Oct 14.

Towards actionable risk stratification: a bilinear approach.

Author information

1
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Electronic address: wangxi@us.ibm.com.
2
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Electronic address: fwang@us.ibm.com.
3
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Electronic address: jyhu@us.ibm.com.
4
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Electronic address: sorrentino@us.ibm.com.

Abstract

Risk stratification is instrumental to modern clinical decision support systems. Comprehensive risk stratification should be able to provide the clinicians with not only the accurate assessment of a patient's risk but also the clinical context to be acted upon. However, existing risk stratification techniques mainly focus on predicting the risk score for individual patients; at the cohort level, they offer little insight beyond a flat score-based segmentation. This essentially reduces a patient to a score and thus removes him/her from his/her clinical context. To address this limitation, in this paper we propose a bilinear model for risk stratification that simultaneously captures the three key aspects of risk stratification: (1) it predicts the risk of each individual patient; (2) it stratifies the patient cohort based on not only the risk score but also the clinical characteristics; and (3) it embeds all patients into clinical contexts with clear interpretation. We apply our model to a cohort of 4977 patients, 1127 among which were diagnosed with Congestive Heart Failure (CHF). We demonstrate that our model cannot only accurately predict the onset risk of CHF but also provide rich and actionable clinical insights into the patient cohort.

KEYWORDS:

Bilinear model; Dimensionality reduction; Logistic regression; Matrix factorization; Risk stratification

PMID:
25445924
DOI:
10.1016/j.jbi.2014.10.004
[Indexed for MEDLINE]
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