Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 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. Mann IJCNN, 2009 Presented by Hung-Yi Cai 2010/12/01

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objectives  Methodology  Experiments  Conclusions  Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Propensity matching with… ─ Logistic Regression ─ Neural Gas ─ Self-Organizing Map ─ Crisp K-means 5

Intelligent Database Systems Lab N.Y.U.S.T. 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. 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Neural Gas 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Self-Organizing Map 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Time-to-event survival analysis for propensity-matched subjects using Kaplan- Meier analysis and Cox proportional hazards regression. 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Number of matched data based on logit-based propensity matching (N=3,332). 10

Intelligent Database Systems Lab N.Y.U.S.T. 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 =

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Linear fit results for regressing Cox PH Martingaler Residuals on Logits and best-winning nodes for NG and SOM. 12

Intelligent Database Systems Lab N.Y.U.S.T. 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. 13

Intelligent Database Systems Lab N.Y.U.S.T. 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. 14

Intelligent Database Systems Lab N.Y.U.S.T. 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. 15

Intelligent Database Systems Lab N.Y.U.S.T. 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. 16

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 17 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. I. M. 18 Comments  Advantages ─ The NG algorithm presents result better than other match method.  Applications ─ The classification of medical treatment