improved propensity matching for heart failure using neural gas and self-organizing maps

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Improved Propensity Matching for Heart Failure Using Neural Gas and Self-Organizing Maps

Leif E. Peterson, Sameer Ather, Vijay Divakaran, Anita Deswal, Biykem Bozkurt, Douglas L. MannIJCNN, 2009

Presented by Hung-Yi Cai2010/12/01

Intelligent Database Systems Lab

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Outlines· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments

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Motivation· Heart failure (HF) has a poor prognosis and is a

major cause of morbidity and mortality.· Unfortunately, the body of information on risk

information for HF among elderly patients is based to a large extent on older heart transplant patients with more severe conditions and more comorbidities.

· Therefore, data obtained from older cases are less amenable for non-biased studies of HF.

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Objectives· The purpose of this paper is to study the effect of

four methods of propensity matching on the relative hazard of mortality among NYHA class III-IV heart failure patients vs. patients in NYHA class I-II.

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I. M.Methodology· Propensity matching with…

─ Logistic Regression─ Neural Gas─ Self-Organizing Map─ Crisp K-means

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I. M.Logistic Regression· In statistics, logistic regression is used for

prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.

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I. M.Neural Gas

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I. M.Self-Organizing Map

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I. M.Experiments· Time-to-event survival analysis for

propensity-matched subjects using Kaplan-Meier analysis and Cox proportional hazards regression.

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I. M.Experiments· Number of matched data based on logit-based

propensity matching (N=3,332).

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I. M.Experiments· NG was based on M = 50 nodes. SOM was

based on a 7 x 7 square map, and thus M = 49. CKM was based on k = 50.

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I. M.Experiments· Linear fit results for regressing Cox PH Martingaler

Residuals on Logits and best-winning nodes for NG and SOM.

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I. M.Experiments· Kaplan-Meier plot of all-cause mortality as a function of

having NYHA I-II vs. NYHA III-IV for N = 3,332 subjects propensity matched with logistic regression.

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I. M.Experiments· Kaplan-Meier plot of all-cause mortality as a function of having NYHA I-

II vs. NYHA III-IV for N = 3,996 (normalized features) and N = 4,262 (standardized features) subjects propensity matched with neural gas.

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I. M.Experiments· Kaplan-Meier plot of all-cause mortality as a function of having NYHA I-

II vs. NYHA III-IV for N = 4,004 (normalized features) subjects and N = 4 ,282 (standardized features) propensity matched with a 7 x 7 (M = 49) self-organizing map.

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I. M.Experiments· Kaplan-Meier plot of all-cause mortality as a function of having NYHA I-

II vs. NYHA III-IV for N = 4,248 subjects propensity matched with crisp K-means cluster analysis using k = 50 clusters.

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Conclusions· Overall, NG resulted in an increased HR for

mortality and explained considerably more variation in Martingale residuals when compared with logit-based propensity matching.

Intelligent Database Systems Lab

N.Y.U.S.T.

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Comments· Advantages

─ The NG algorithm presents result better than other match method.

· Applications─ The classification of medical treatment