title: Toward personalizing treatment for depression: predicting diagnosis and severity.
abstract: OBJECTIVE
  Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment.
  
  
  METHODS
  We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35,000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute.
  
  
  RESULTS
  Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70-0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy.
  
  
  CONCLUSIONS
  It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
authors.forename: Sandy H
authors.initials: SH
authors.lastname: Huang
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
authors.forename: Paea
authors.initials: P
authors.lastname: LePendu
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
authors.forename: Srinivasan V
authors.initials: SV
authors.lastname: Iyer
authors.affiliation: Palo Alto Medical Foundation Research Institute, Palo Alto, California, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Tai-Seale
authors.affiliation: Group Health Research Institute, Seattle, Washington, USA.
authors.forename: David
authors.initials: D
authors.lastname: Carrell
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D003866:Depressive Disorder / Q000145:classification / Q000175:diagnosis*; D003937:Diagnosis, Differential; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D057285:Precision Medicine; D012372:ROC Curve; D012720:Severity of Illness Index
keywords: data mining; depression; electronic health records; ontology; personalized medicine