title: Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment.
abstract: BACKGROUND
  The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods.
  
  
  METHODS
  The aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment. To optimize the model, different feature selection methods were considered. Robust extra-sample error estimation and different loss functions were used to assess model performance. The results were compared with widely used rule-based genotypic interpretation systems (Stanford HIVdb, Rega and ANRS).
  
  
  RESULTS
  A set of 3,143 treatment change episodes were extracted from the EuResist database. The dataset included patient demographics, treatment history and viral genotypes. A logistic regression model using high order interaction variables performed better than rule-based genotypic interpretation systems (accuracy 75.63% versus 71.74-73.89%, area under the receiver operating characteristic curve [AUC] 0.76 versus 0.68-0.70) and was equivalent to a random forest model (accuracy 76.16%, AUC 0.77). However, when rule-based genotypic interpretation systems were coupled with additional patient attributes, and the combination was provided as input to the logistic regression model, the performance increased significantly, becoming comparable to the fully data-driven methods.
  
  
  CONCLUSIONS
  Patient-derived supplementary features significantly improved the accuracy of the prediction of response to treatment, both with rule-based and data-driven interpretation systems. Fully data-driven models derived from large-scale data sources show promise as antiretroviral treatment decision support tools.
authors.affiliation: Computer Science and Automation Department, Roma Tre University, Rome, Italy. ahnven@yahoo.it
authors.forename: Mattia C F
authors.initials: MC
authors.lastname: Prosperi
authors.forename: Andre
authors.initials: A
authors.lastname: Altmann
authors.forename: Michal
authors.initials: M
authors.lastname: Rosen-Zvi
authors.forename: Ehud
authors.initials: E
authors.lastname: Aharoni
authors.forename: Gabor
authors.initials: G
authors.lastname: Borgulya
authors.forename: Fulop
authors.initials: F
authors.lastname: Bazso
authors.forename: Anders
authors.initials: A
authors.lastname: Sönnerborg
authors.forename: Eugen
authors.initials: E
authors.lastname: Schülter
authors.forename: Daniel
authors.initials: D
authors.lastname: Struck
authors.forename: Giovanni
authors.initials: G
authors.lastname: Ulivi
authors.forename: Anne-Mieke
authors.initials: AM
authors.lastname: Vandamme
authors.forename: Jurgen
authors.initials: J
authors.lastname: Vercauteren
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
mesh_terms: D000328:Adult; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D016208:Databases, Factual; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D016896:Treatment Outcome; D019562:Viral Load
chemical_list: D019380:Anti-HIV Agents