Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

By Ehtesham Iqbal, Robbie Mallah, R. Jackson, M. Ball, Zina M. Ibrahim, M. Broadbent, O. Dzahini, R. Stewart, Caroline Johnston, R. Dobson. 2015

78 citations recorded by the source corpus.

Objectives Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify instances of Adverse Drug Events (ADEs) from free text psychiatric EHRs. Methods We used the GATE Natural Language Processing (NLP) software to mine instances of ADEs from free text content within the Clinical Record Interactive Search (CRIS) system, a de-identified psychiatric case register developed at the South London and Maudsley NHS Foundation Trust, UK. The tool was built around a set of four movement disorders (extrapyramidal side effects [EPSEs]) related to antipsychotic therapy and rules were then generalised such that the tool could be applied to additional ADEs. We report the frequencies of recorded EPSEs in patients diagnosed with a Severe Mental Illness (SMI) and then report performance in identifying eight other unrelated ADEs. Results The tool identified EPSEs with >0.85 precision and >0.86 recall during testing. Akathisia was found to be the most prevalent EPSE overall and occurred in the Asian ethnic group with a frequency of 8.13%. The tool performed well when applied to most of the non-EPSEs but least well when applied to rare conditions such as myocarditis, a condition that appears frequently in the text as a side effect warning to patients. Conclusions The developed tool allows us to accurately identify instances of a potential ADE from psychiatric EHRs. As such, we were able to study the prevalence of ADEs within subgroups of patients stratified by SMI diagnosis, gender, age and ethnicity. In addition we demonstrated the generalisability of the application to other ADE types by producing a high precision rate on a non-EPSE related set of ADE containing documents. Availability The application can be found at http://git.brc.iop.kcl.ac.uk/rmallah/dystoniaml.

Subfields: Public Health, Environmental and Occupational Health

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “in order to enable the discovery of DDIs [60], [61] and ADEs [26], [62], [63], [64].”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “[67] used GATE [51] to detect ADEs related to extrapyramidal side effects of antipsychotic therapy.”
Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression (2017, methodology): “11 More recently, NLP has been used to identify adverse drug events including extrapyramidal side effects in psychiatric patients 12 and to phenotype children at risk for Kawasaki disease in emergency department notes.”