title: A novel method predicting clinical response using only background clinical data in RA patients before treatment with infliximab.
abstract: OBJECTIVE
  The aim of the present study was to generate a novel method for predicting the clinical response to infliximab (IFX), using a machine-learning algorithm with only clinical data obtained before the treatment in rheumatoid arthritis (RA) patients.
  
  
  METHODS
  We obtained 32 variables out of the clinical data on the patients from two independent hospitals. Next, we selected both clinical parameters and machine-learning algorithms and decided the candidates of prediction method. These candidates were verified by clinical variables on different patients from two other hospitals. Finally, we decided the prediction method to achieve the highest score.
  
  
  RESULTS
  The combination of multilayer perceptron algorithm (neural network) and nine clinical parameters shows the best accuracy performance. This method could predict the good or moderate response to IFX with 92% accuracy. The sensitivity of this method was 96.7%, while the specificity was 75%.
  
  
  CONCLUSIONS
  We have developed a novel method for predicting the clinical response using only background clinical data in RA patients before treatment with IFX. Our method for predicting the response to IFX in RA patients may have advantages over the other previous methods in several points including easy usability, cost-effectiveness and accuracy.
authors.affiliation: a Department of Rheumatology and Applied Immunology, Faculty of Medicine , Saitama Medical University , Saitama , Japan.
authors.forename: Fumihiko
authors.initials: F
authors.lastname: Miyoshi
authors.affiliation: b Division of Rheumatology and Clinical Immunology , Jichi Medical University , Tochigi , Japan.
authors.forename: Kyoko
authors.initials: K
authors.lastname: Honne
authors.affiliation: b Division of Rheumatology and Clinical Immunology , Jichi Medical University , Tochigi , Japan.
authors.forename: Seiji
authors.initials: S
authors.lastname: Minota
authors.affiliation: c Immuno-Rheumatology Center, St. Luke's International Hospital , Tokyo , Japan , and.
authors.forename: Masato
authors.initials: M
authors.lastname: Okada
authors.affiliation: d Division of Immunology and Rheumatology , Internal Medicine 3, Hamamatsu University School of Medicine , Hamamatsu , Japan.
authors.forename: Noriyoshi
authors.initials: N
authors.lastname: Ogawa
authors.affiliation: a Department of Rheumatology and Applied Immunology, Faculty of Medicine , Saitama Medical University , Saitama , Japan.
authors.forename: Toshihide
authors.initials: T
authors.lastname: Mimura
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000175:diagnosis / Q000188:drug therapy*; D003362:Cost-Benefit Analysis; D005260:Female; D006801:Humans; D000069285:Infliximab / Q000627:therapeutic use*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D063189:Symptom Assessment; D016896:Treatment Outcome
chemical_list: D018501:Antirheumatic Agents; D000069285:Infliximab
keywords: Clinical data; Infliximab; Machine-learning; Rheumatoid arthritis; The prediction of clinical response