david odgers - bmi retreat 2015 poster

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Introduc)on Outcome Variables Phenotypic Profiling for Rheumatoid Arthri7s Treatment Efficacy Using Electronic Health Records Stanford University, Department of Biomedical Informa)cs David J. Odgers, Natalie Tellis, Heather Hall, Michel Dumon)er Results Rheumatoid arthri)s (RA) accounts for onefiKh of the deaths due to arthri)s, the leading cause of disability in the United States. Finding effec)ve treatments for managing arthri)s symptoms are a major challenge, since the mechanisms of autoimmune disorders are not fully understood and disease presenta)on differs for each pa)ent. The American College of Rheumatology clinical guidelines for treatment consider the severity of the disease when deciding treatment, but do not include any predic)on of drug efficacy. Using Electronic Health Records and Biomedical Linked Open Data (LOD), we hypothesize that LASSO regression classifiers can be created from base clinical features consis)ng of 482 coprescrip)ons and 3,210 comorbidi)es for the top three treatment regimes for RA pa)ents. Addi)onally, we hypothesize that extending the coprescrip)ons and comorbidi)es into features directly rela)ng to basic biomedical knowledge using LOD will improve the overall LASSO classifier performance. Cohort Selec)on Conclusion The LASSO classifiers with extended features showed rela)ve improvement in almost all metrics over the baseline of strictly clinical features. All the AUCs were between .71 and .81 with a 7% rela)ve increase in AUC over baseline for DMARDS features. Test error improved across all models from 312% over baseline. Sensi)vity and Specificity showed both improvements and decline between the models, even though the classifier performance described by the Mathews Correla)on Coefficient improved or stayed stable for all three treatments regimes when compared to baseline. These results warrant further inves)ga)on, tes)ng addi)onal therapeu)c areas with addi)onal LOD resources. Extended Features

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Page 1: David Odgers - BMI Retreat 2015 Poster

Introduc)on  

Outcome  Variables  

Phenotypic  Profiling  for  Rheumatoid  Arthri7s  Treatment  Efficacy  Using  Electronic  Health  Records  

Stanford  University,  Department  of  Biomedical  Informa)cs    David  J.  Odgers,    Natalie  Tellis,    Heather  Hall,  Michel  Dumon)er  

Results  

Rheumatoid  arthri)s  (RA)  accounts  for  one-­‐fiKh  of  the  deaths  due  to  arthri)s,  the  leading  cause  of  disability  in  the  United  States.  Finding  effec)ve  treatments  for  managing  arthri)s  symptoms  are  a  major  challenge,  since  the  mechanisms  of  autoimmune  disorders  are  not  fully  understood  and  disease  presenta)on  differs  for  each  pa)ent.  The  American  College  of  Rheumatology  clinical  guidelines  for  treatment  consider  the  severity  of  the  disease  when  deciding  treatment,  but  do  not  include  any  predic)on  of  drug  efficacy.    Using  Electronic  Health  Records  and  Biomedical  Linked  Open  Data  (LOD),  we  hypothesize  that  LASSO  regression  classifiers  can  be  created  from  base  clinical  features  consis)ng  of  482  co-­‐prescrip)ons  and  3,210  comorbidi)es  for  the  top  three  treatment  regimes  for  RA  pa)ents.    Addi)onally,  we  hypothesize  that  extending  the  co-­‐prescrip)ons  and  comorbidi)es  into  features  directly  rela)ng  to  basic  biomedical  knowledge  using  LOD  will  improve  the  overall  LASSO  classifier  performance.  

Cohort  Selec)on  

Conclusion  

The  LASSO  classifiers  with  extended  features  showed  rela)ve  improvement  in  almost  all  metrics  over  the  baseline  of  strictly  clinical  features.    All  the  AUCs  were  between  .71  and  .81  with  a  7%  rela)ve  increase  in  AUC  over  baseline  for  DMARDS  features.    Test  error  improved  across  all  models  from  3-­‐12%  over  baseline.    Sensi)vity  and  Specificity  showed  both  improvements  and  decline  between  the  models,  even  though  the  classifier  performance  described  by  the  Mathews  Correla)on  Coefficient  improved  or  stayed  stable  for  all  three  treatments  regimes  when  compared  to  baseline.    These  results  warrant  further  inves)ga)on,  tes)ng  addi)onal  therapeu)c  areas  with  addi)onal  LOD  resources.  

Extended  Features