title: Patient-tailored prioritization for a pediatric care decision support system through machine learning.
abstract: OBJECTIVE
  Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization.
  
  
  METHODS
  We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients.
  
  
  RESULTS
  The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships.
  
  
  CONCLUSIONS
  We demonstrate the ability of a Bayesian structure learning method to 'phenotype the population' seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances.
  
  
  CONCLUSIONS
  This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services.
authors.affiliation: Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, USA.
authors.forename: Jeffrey G
authors.initials: JG
authors.lastname: Klann
authors.forename: Vibha
authors.initials: V
authors.lastname: Anand
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Downs
mesh_terms: D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D057225:Data Mining; D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records; D005240:Feasibility Studies; D006801:Humans; D018802:Patient-Centered Care; D010372:Pediatrics / Q000458:organization & administration*; D010641:Phenotype; D017410:Practice Guidelines as Topic; D011314:Preventive Health Services / Q000458:organization & administration*; D012372:ROC Curve
keywords: Bayesian analysis; clinical decision support; data mining; pediatrics; phenotype