title: Dose-specific adverse drug reaction identification in electronic patient records: temporal data mining in an inpatient psychiatric population.
abstract: BACKGROUND
  Data collected for medical, filing and administrative purposes in electronic patient records (EPRs) represent a rich source of individualised clinical data, which has great potential for improved detection of patients experiencing adverse drug reactions (ADRs), across all approved drugs and across all indication areas.
  
  
  OBJECTIVE
  The aim of this study was to take advantage of techniques for temporal data mining of EPRs in order to detect ADRs in a patient- and dose-specific manner.
  
  
  METHODS
  We used a psychiatric hospital's EPR system to investigate undesired drug effects. Within one workflow the method identified patient-specific adverse events (AEs) and links these to specific drugs and dosages in a temporal manner, based on integration of text mining results and structured data. The structured data contained precise information on drug identity, dosage and strength.
  
  
  RESULTS
  When applying the method to the 3,394 patients in the cohort, we identified AEs linked with a drug in 2,402 patients (70.8 %). Of the 43,528 patient-specific drug substances prescribed, 14,736 (33.9 %) were linked with AEs. From these links we identified multiple ADRs (p < 0.05) and found them to occur at similar frequencies, as stated by the manufacturer and in the literature. We showed that drugs displaying similar ADR profiles share targets, and we compared submitted spontaneous AE reports with our findings. For nine of the ten most prescribed antipsychotics in the patient population, larger doses were prescribed to sedated patients than non-sedated patients; five antipsychotics [corrected] exhibited a significant difference (p<0.05). Finally, we present two cases (p < 0.05) identified by the workflow. The method identified the potentially fatal AE QT prolongation caused by methadone, and a non-described likely ADR between levomepromazine and nightmares found among the hundreds of identified novel links between drugs and AEs (p < 0.05).
  
  
  CONCLUSIONS
  The developed method can be used to extract dose-dependent ADR information from already collected EPR data. Large-scale AE extraction from EPRs may complement or even replace current drug safety monitoring methods in the future, reducing or eliminating manual reporting and enabling much faster ADR detection.
authors.affiliation: NNF Center for Protein Research, Department of Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
authors.forename: Robert
authors.initials: R
authors.lastname: Eriksson
authors.forename: Thomas
authors.initials: T
authors.lastname: Werge
authors.forename: Lars Juhl
authors.initials: LJ
authors.lastname: Jensen
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
mesh_terms: D000293:Adolescent; D016907:Adverse Drug Reaction Reporting Systems; D000368:Aged; D000369:Aged, 80 and over; D014150:Antipsychotic Agents / Q000008:administration & dosage* / Q000009:adverse effects*; D003625:Data Collection / Q000379:methods; D057225:Data Mining; D064420:Drug-Related Side Effects and Adverse Reactions / Q000209:etiology*; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D007297:Inpatients; D008297:Male; D001523:Mental Disorders / Q000188:drug therapy*; D008875:Middle Aged; D055815:Young Adult
chemical_list: D014150:Antipsychotic Agents