Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population

By Robert Eriksson, Thomas Werge, Lars Juhl Jensen, Søren Brunak. 2014

47 citations recorded by the source corpus.

BackgroundData 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.ObjectivesThe 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.MethodsWe 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.ResultsWhen 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 patients 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).ConclusionsThe 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.

Subfields: Artificial Intelligence, Health Information Management, Toxicology

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Citations in the corpus

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, background): “[42, 43] to detect ADEs from narratives of patients from a mental health center in Denmark.”
From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare (2015, background): “Minin g local information included in EHR data-aware houses has a lready proved an effective way of managing a wide range of healthcare challenges such as supporting disease ma nagement system [66, 67], pharmacovigilance [68], building m odels for predicting health risk assessment [69, 70], communi ”